Search

In today's world that called technology age, smartphones have become indispensable for users in many areas such as internet usage, social media usage, bank transactions, e-mail, as well as communication. The Android operating system is the most popular operating system that used with a rate of 85.4% in smartphones and tablets. Such a popular and widely used platform has become the target of malware. Malicious software can cause both material and moral damages to users.In this study, malwares that targeting smart phones were detected by using static, dynamic and hybrid analysis methods. In the static analysis, feature extraction was made in 9 different categories. These attributes are categorized under the titles of requested permissions, intents, Android components, Android application calls, used permissions, unused permissions, suspicious Android application calls, system commands, internet addresses. The obtained features were subjected to dimension reduction with principal component analysis and used as input to the deep neural network model. With the established model, 99.38% accuracy rate, 99.36% F1 score, 99.32% precision and 99.39% sensitivity values were obtained in the test data set.In the dynamic analysis part of the study, applications were run on a virtual smartphone, and Android application calls with strategic importance were obtained by hooking. The method called hybrid analysis was applied by combining the dynamically obtained features with the static features belonging to the same applications. With the established model, 96.94% accuracy rate, 96.78% F1 score, 96.99% precision and 96.59% sensitivity values were obtained in the test data set.

Early diagnosis of intracranial hemorrhage significantly reduces mortality. Hemorrhage is diagnosed by using various imaging methods and the most time-efficient one among them is computed tomography (CT). However, it is clear that accurate CT scans requires time, diligence, and experience. Computer-aided design methods are vital for the treatment because they facilitate early diagnosis of intracranial hemorrhage. At this point, deep learning can provide effective outcomes through an automated diagnosis way. However, as different from the known solutions, diagnosis of five different hemorrhage subtypes is a critical problem to be solved.This study focused on deep learning methods and employed cranial computed tomography scans in order to detect intracranial hemorrhage. The diagnosis approach in the study aimed to detect five subtypes of hemorrhage. In detail, EfficientNet-B3 and ResNet-Inception-V2 architectures were used for diagnosis purposes. Eventually, the study also proposed a two-architecture hybrid method for the diagnosis purpose. The obtained findings by the hybrid method were evaluated in terms of a comparative perspective.Results showed that the newly designed hybrid method was quite effective in terms of increasing classification rates of detecting intracranial hemorrhage according to the subtypes. Briefly, an accuracy of 98.5%, which is higher than those of the EfficientNet-B3 and the Inception-ResNet-V2, were obtained thanks to the developed hybrid method.

Skolyoz, omurganın eğrilmesi ile birlikte omurga genel yapısını deforme eden bir hastalıktır. Skolyoz tanı ve tedavi aşamasında çeşitli yöntemler olmakla birlikte, temel amaç Cobb açısı adı verilen eğrilik açısını azaltarak Skolyoz seviyesini düşürme çerçevesinde şekillenmektedir. Cobb açısı ölçümü esasında uzman tarafından, omurga röntgen filmleri üzerinde manuel olarak gerçekleştirilmektedir. Ancak bu sürecin derin öğrenme gibi bir Yapay Zeka yaklaşımıyla otomatikleştirilmesi hem hasta hem de uzman açısından büyük kolaylık ve kesinlik sağlayacaktır. Açıklamalardan hareketle bu çalışmada, öncelikli olarak Skolyoz ve derin öğrenme odaklı çalışmalar açısından literatürün güncel durumu ele alınmış, ardından Kapsül Ağları (CapsNet) tabanlı bir çözüm ile Cobb açısı ölçümlerinin otomatik bir hale getirilmesi sağlanmıştır. CapsNet çözümünün, ConvNet, BoostNet, RFR ve ResNet-50 modelleri ile karşılaştırılması neticesinde en iyi bulguları CapsNet modelinin verdiği tespit edilmiştir.

The fundamental purpose of every investor making investments in financial fields, is to make profit by buying an investment instrument at a low price and selling the same at a higher price. In this study, within the framework of the aforementioned standpoint, an effective “Trading” decision support model was designed, which can be used for stock market analyses, parity analyses, index analyses, and for the stock analyses of other stock exchanges, briefly for all investment instruments for which candlestick charts are created. An innovative model design was achieved through a bilateral perspective with both financial and scientific aspects by designing these models that operated on a pattern detection basis. The study incorporated the use of 2D candlestick charts of the BIST stocks. The charts were labeled in two separate data sets. Initially, 10,000 pieces of data were labeled on 550 2D candlestick charts, which were trained with YoloV3 Data Group-1 (DG-1). Subsequently, the data set was increased to 20,000 pieces. Out of this set of 20,000 labeled data prepared, 10,000 labeled data were picked randomly. The newly-created set of 10,000 labeled data was named DG-2, which was trained with the YoloV3, YoloV4, Faster R-CNN, SDD algorithms. An assessment was made regarding the performance results obtained following the trainings implemented for these four chosen algorithms. For the aforementioned assessment, three different scenarios were created, and out of all these scenarios, the YoloV3 DG-2 algorithm, which was trained with an improved data set, was observed to be most successful one. As a result of the comparative test scenarios, the YoloV3 DG-2 model achieved a pattern recognition success of 98%. On the other hand, it was also observed to have achieved a prediction success of 100%, while bringing in a return by 89.94%, regarding the object class detected. In addition, no additional parameters were used in this observed gain success. Consequently, the YoloV3 DG-2, determined as the final model, could be implemented as a decision support model for all investment instruments for which a candlestick chart can be created.

This study aims to explore new therapeutic opportunities for histone deacetylase (HDAC) inhibitors by leveraging drug repurposing approaches and analyzing their bioactivity and molecular fingerprints. The methodology includes investigating drug repurposing opportunities for HDAC inhibitors, evaluating the bioactivity of repurposing drugs on HDAC enzymes, investigating the role of HDAC genes in therapeutic effects, and analyzing molecular fingerprints with explainable artificial intelligence (XAI) to identify structurally similar compounds with potential HDAC inhibitory activity. In this context, chemical compounds with IC50 (7903 compounds) and Inhibition (1084 compounds) standard types of HDAC genes reported to be associated with childhood acute leukemia were represented by molecular fingerprints. Regression and classification models were applied to the molecular fingerprints, and the results obtained were supported by XAI. All the study results were shared interactively on the website address https://iuysal1905-childhoodacuteleukemia-drug-interacito-arayuz-r89zld.streamlit.app/ by designing a simulation environment. The influence of molecular fingerprints on the models and their impact on potential drug development in childhood acute leukemia were evaluated using XAI techniques, particularly through the analysis of SHAP values. The study contributes to the literature on the use of XAI technology in drug repurposing studies, especially in cancer, the study of molecular properties, and the active use of XAI in drug repurposing studies.

Iron (Fe) chelating medicines and Histone deacetylase (HDAC) inhibitors are two therapy options for hereditary Friedreich's Ataxia that have been shown to improve clinical results (FA). Fe chelation molecules can minimize the quantity of stored Fe, and HDAC inhibitors can boost the expression of the Frataxin (FXN) gene in enhancing FA. A complete quantitative structure-activity relationship (QSAR) search of inhibitors from the ChEMBL database is reported in this paper, which includes 437 compounds for Fe chelation and 1,354 compounds for HDAC inhibitors. For further investigation, the IC50 was chosen as the unit of bioactivity, and following data refinement, a final dataset of 436 and 1,163 compounds for Fe chelation and HDAC inhibition, respectively, was produced. The Random Forest (RF) technique was used to generate models (train R2 score, 0.701 and 0.892; test R2 score 0.572 and 0.460, for Fe and HDAC, respectively). The models created using the PubChem fingerprint were the strongest of the 12 fingerprint kinds; hence that feature was chosen for interpretation. The results showed the importance of properties related to nitrogen-containing functional groups (SHAP value of PubchemFP656 is -0.29) and aromatic rings (SHAP value of PubchemFP12 is -0.16). As a result, we explained the effect of the molecular fingerprints on the models and the impact on possible drugs that can be developed for FA with artificial intelligence (XAI), which can be explained through SHAP (Shapley Additive Explanations) values. Model scripts and fingerprinting methods are also available at https://github.com/tissueandcells/XAI.

Task Scheduling is an enormous challenge in cloud computing model as to map diverse tasks arises from various sources there should be an efficient scheduling mechanism which provision resources dynamically to users based on their corresponding requests. Ineffective scheduling leads to increase in makespan, energy consumption and violates SLA made between cloud user and service provider thereby quality of service will be degraded and trust on the cloud service provider will be degraded. Trust typically based on quality-of-service parameters such as Availability of virtual resources, Success rate of tasks, Turnaround efficiency of tasks which are included in SLA. In this paper, we designed a Multi objective trust aware scheduler which takes priority of tasks, VMs and schedule tasks to appropriate virtual resources while minimizing makespan, energy consumption. Whale Optimization algorithm used to model our task scheduler. Entire simulation carried out on Cloudsim. Workload used in this simulation is of both fabricated and real-time worklogs captured from HPC2N and NASA. Our proposed approach compared against existing metaheuristic approaches i.e., ACO, GA, PSO approaches. From Simulation results, we observed that there is a significant improvement in makespan, Energy consumption, total running time and trust parameters i.e., Availability, Success rate, Turnaround efficiency.

Scoliosis is a disease caused by the spine curving. It is treatable but physiotherapists may do different measurements for curvature angles. That’s a problem affecting the treatment planning. This study aims to develop a Deep Learning-based decision support system, which diagnoses scoliosis and plans treatments via Schroth method. The system has an interpretable and explainable CapsNet model processing x-ray image to detect 68-point vertebrae and make Cobb angle measurements. By using angle values and patient parameters, treatment is planned through an automated Schroth definition and the Fuzzy Logic. In the evaluations, the CapsNet had dominating findings (some of them are MSE: 0.0038, PCC: 0.93, Accuracy: 0.98). The Fuzzy Logic model was accurate at exercise plans for past cases. Also, physiotherapists and patients had positive feedback for the system usage, trustworthiness, diagnosis, treatment planning and tracking. As a conclusion, the system ensures advancements for automated diagnosis and treatment of scoliosis.

Blockchain systems are popular technologies that have recently emerged. As a decentralized system, blockchain technology has provided many solutions and many problems associated with these solutions. One of its most important problems is that while performing hash calculations very intensively to create a new consensus block, it reduces its efficiency depending on the duration. In this study, a new model to avoid Proof of Work (PoW), which directs the computations made to create blocks to optimization algorithms, is proposed. The proof mechanism proposed in this study is called Proof of Optimization (PoO). A traveling salesman problem (TSP) is entered into the designed system to solve the optimization algorithms. Nodes are asked to solve the TSP in certain iterations and populations. As a result, nodes are asked to create blocks with the fitness, density and time values obtained. PoO and PoW consensus algorithms were subjected to an experimental comparison in the system. The test results show that the block generation time of the PoO consensus model varies between 2 s according to the dataset solution with the least cities (ulysses22) and 60 s according to the dataset solution with the most cities (gr666). Additionally, as a result of experimental analyses, it was determined that decentralization, which is the percentage of block creation among miners in the blockchain, reached a more stable value and the fairness index rose above 0.90 on average. When the obtained values were compared with PoW, it was observed that the block time was more stable and the decentralization of the blockchain was higher. In this way, high-equipped nodes in the blockchain system are prevented from dominating the network. Thus, it is ensured that low-equipped nodes have the right to create blocks in the blockchain. The difficulty levels of the problems can be adjusted by changing the number of cities in the TSP evaluated in this study. In this way, the problem of creating blocks in the network can be made more difficult or easier at any time.