Multi-Stage Learning Framework Using Convolutional Neural Network and Decision Tree-Based Classification for Detection of DDoS Pandemic Attacks in SDN-Based SCADA Systems.

Supervisory Control and Data Acquisition (SCADA) systems, which play a critical role in monitoring, managing, and controlling industrial processes, face flexibility, scalability, and management difficulties arising from traditional network structures. Software-defined networking (SDN) offers a new opportunity to overcome the challenges traditional SCADA networks face, based on the concept of separating the control and data plane. Although integrating the SDN architecture into SCADA systems offers many advantages, it cannot address security concerns against cyber-attacks such as a distributed denial of service (DDoS).

Ensemble Learning Framework for DDoS Detection in SDN-Based SCADA Systems.

Supervisory Control and Data Acquisition (SCADA) systems play a crucial role in overseeing and controlling renewable energy sources like solar, wind, hydro, and geothermal resources. Nevertheless, with the expansion of conventional SCADA network infrastructures, there arise significant challenges in managing and scaling due to increased size, complexity, and device diversity. Using Software Defined Networking (SDN) technology in traditional SCADA network infrastructure offers management, scaling and flexibility benefits.

Prediction of Heart Disease Based on Machine Learning Using Jellyfish Optimization Algorithm.

Heart disease is one of the most known and deadly diseases in the world, and many people lose their lives from this disease every year. Early detection of this disease is vital to save people's lives. Machine Learning (ML), an artificial intelligence technology, is one of the most convenient, fastest, and low-cost ways to detect disease.

Diagnosis of Chronic Kidney Disease Based on Support Vector Machine by Feature Selection Methods.

As Chronic Kidney Disease progresses slowly, early detection and effective treatment are the only cure to reduce the mortality rate. Machine learning techniques are gaining significance in medical diagnosis because of their classification ability with high accuracy rates. The accuracy of classification algorithms depend on the use of correct feature selection algorithms to reduce the dimension of datasets.

Erratum to "IL8 and serum soluble TRAIL levels following anti-VEGF monoclonal antibody treatment in patients with metastatic colon cancer" [Clin. Lab. 2012; 58:501-505].

Background: Colorectal cancer is the third most common human cancer and the third leading cause of cancer related death. BevacizumAb is a humanized monoclonal antibody developed against vascular endothelial growth factor (VEGF) for the treatment of metastatic cancers. Our goal was to evaluate the possibility of using serum sTRAIL and IL8 as markers of treatment efficacy and prognosis in patients with metastatic colon cancer.

IL8 and serum soluble TRAIL levels following anti-VEGF monoclonal antibody treatment in patients with metastatic colon cancer.

Background: Colorectal cancer is the third most common human cancer and the third leading cause of cancer related death. BevacizumAb is a humanized monoclonal antibody developed against vascular endothelial growth factor (VEGF) for the treatment of metastatic cancers. Our goal was to evaluate the possibility of using serum sTRAIL and IL8 as markers of treatment efficacy and prognosis in patients with metastatic colon cancer.

Software development for the analysis of heartbeat sounds with LabVIEW in diagnosis of cardiovascular disease.

In this paper, a time-frequency spectral analysis software (Heart Sound Analyzer) for the computer-aided analysis of cardiac sounds has been developed with LabVIEW. Software modules reveal important information for cardiovascular disorders, it can also assist to general physicians to come up with more accurate and reliable diagnosis at early stages. Heart sound analyzer (HSA) software can overcome the deficiency of expert doctors and help them in rural as well as urban clinics and hospitals.

Status of influenza vaccination in patients presenting to two neighborhood primary health care clinics in Antalya.

The objective of our study was to determine the status of influenza vaccination in patients presenting to two neighborhood primary health care clinics at the provincial centre of Antalya. This type of descriptive research was conducted between March 15 and April 15, 2006, at Primary Health Care Clinics Number 9 and 16 in Antalya. A prepared questionnaire was completed by Akdeniz University Medical Faculty intern physicians during face-to-face interviews with 1494 patients.

Combining neural network and genetic algorithm for prediction of lung sounds.

Recognition of lung sounds is an important goal in pulmonary medicine. In this work, we present a study for neural networks-genetic algorithm approach intended to aid in lung sound classification. Lung sound was captured from the chest wall of The subjects with different pulmonary diseases and also from the healthy subjects.

Examination of the effects of degeneration on vertebral artery by using neural network in cases with cervical spondylosis.

The scope of this study is to diagnose vertebral arterial inefficiency by using Doppler measurements from both right and left vertebral arterials. Total of 96 patients' Doppler measurements, consisting of 42 of healthy, 30 of spondylosis, and 24 of clinically proven vertebrobasillary insufficiency (VBI), were examined. Patients' age and sex information as well as RPSN, RPSVN, LPSN, LPSVN, and TOTALVOL medical parameters obtained from vertebral arterials were classified by neural networks, and the performance of said classification reached up to 93.

A simple computer-based measurement and analysis system of pulmonary auscultation sounds.

Listening to various lung sounds has proven to be an important diagnostic tool for detecting and monitoring certain types of lung diseases. In this study a computer-based system has been designed for easy measurement and analysis of lung sound using the software package DasyLAB. The designed system presents the following features: it is able to digitally record the lung sounds which are captured with an electronic stethoscope plugged to a sound card on a portable computer, display the lung sound waveform for auscultation sites, record the lung sound into the ASCII format, acoustically reproduce the lung sound, edit and print the sound waveforms, display its time-expanded waveform, compute the Fast Fourier Transform (FFT), and display the power spectrum and spectrogram.