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Adrenal lesions are usually discovered incidentally during other health screenings and are usually benign. However, it is vital to take precautions when a malignant adrenal lesion is detected. Especially deep learning models developed in the last ten years give successful results on medical images. In this paper, adrenal lesion characterization on T1-weighted magnetic resonance abdomen images was aimed using convolutional neural network (CNN) which is one of the deep learning methods. Firstly, effects of important model parameters are assessed on performance of CNN, so optimum CNN model is obtained for classification of adrenal lesions. For a fixed number of convolution filters determined in the first stage of the study, CNN model implemented by different kernel sizes were trained. According to the best result obtained, this time the kernel size was kept constant, and experiments were made for different filter numbers. Finally, studies were carried out with CNN structures of different depths and the results were compared. As a result of the studies, when filter is selected as [5 20], the best results in the trainings conducted with a single-block CNN structure are obtained 0.97, 0.90, 0.98, 0.90, 0.90, and 0.94, for accuracy, sensitivity, specificity, precision, F1-score, and AUC score, respectively. The study was compared with the studies in the literature, and it was seen that it was superior to them.

WOS: 000476620800074, For analysis of Parkinson illness gait disabilities detection is essential. The only motivation behind this examination is to equitably and consequently differentiate among sound subjects and the one who is forbearing the Parkinson, utilizing IOT based indicative framework. In this examination absolute, 16 distinctive force sensors being attached with the shoes of subjects which documented the Multisignal Vertical Ground Reaction Force (VGRF). Overall sensors signals utilizing 1024 window estimate around the raw signals, utilizing the Packet wavelet change (PWT) five diverse characteristics that includes entropy, energy, variance, standard deviation and waveform length were derived and support vector machine (SVM) is to recognize Parkinson patients and healthy subjects. SVM is trained on 85% of the dataset and tested on 15% dataset. Preparation accomplice relies upon 93 patients with idiopathic PD (mean age: 66.3 years; 63% men and 37% ladies), and 73 healthy controls (mean age: 66.3 years; 55% men and 45% ladies). IOT framework included all 16 sensors, from which 8 compel sensors were appended to left side foot of subject and the rest of the 8 on the right side foot. The outcomes demonstrate that fifth sensor worn on a Medial part of the dorsum of right foot highlighted by R5 gives 903% accuracy. Henceforth this examination gives the knowledge to utilize single wearable force sensor. Hence, this examination deduce that a solitary sensor might help in differentiation amongst Parkinson and healthy subjects.

Decision making could be critically important for people in some situations. People have intentions to choose a side if it is time to make decision. These intentions are strongly related to the knowledge and experience. But sometimes outer effects could reshape their intentions easily. In this paper, an experimental work is studied for Human-Machine Interaction in which if holograms could change or affect the human intention. And also the question which asks whether people trust on a hologram agent while making decision or not is researched. To study this research, a memory game application is developed and this application is run on Microsoft Hololens device. Hololens is used to maintain the Augmented Reality (AR) environment with holograms. An algorithm with a Finite State Machines (FSM) is developed to manage the response of hologram agent while giving hint to the confused users. The accuracy of the hints changes nonlinearly. 3 different game stages are trained on users to see how they are affected by both virtual and real world noises. According to the results, intention of majority of users was affected by the hologram while making the decisions. Also it is observed that some users who were concentrated too much to memorize the order of objects did not realize the hologram, and some few could not understand the actions of hologram., Karar verme, bazı durumlarda insanlar için kritik derecede önemli olabilir. İnsanların karar verme zamanı geldiğinde taraf seçme niyetleri vardır. Bu niyetler, bilgi ve geçmiş deneyimlerle ilişkilidir. Ancak bazen dış etkiler insan niyetini kolayca yeniden şekillendirebilir. Bu makalede, İnsan-Makine Etkileşimi için deneysel bir çalışma gerçekleştirilmiştir. Burada hologramların insan niyetini değiştirip değiştiremeyeceği araştırılmıştır. Ayrıca insanların karar verirken hologram etmene güvenip güvenmediği de araştırılmıştır. Bu araştırmayı incelemek için bir hafıza oyunu uygulaması geliştirilmiş ve bu uygulama Microsoft Hololens cihazında koşturulmuştur. Hololens, Hologramlarla Artırılmış Gerçeklik (AG) ortamını sağlamak için kullanılmıştır. Sonlu Durum Makineleri (SDM) içeren bir algoritma, kafası karışan kullanıcılara ipucu verirken hologram etmenin tepkilerini yönetmek için geliştirilmiştir. İpuçlarının doğruluğu doğrusal olmayan bir şekilde değişmektedir. Kullanıcıların hem sanal hem de gerçek dünya seslerinden nasıl etkilendiklerini görmek için 3 farklı oyun aşaması tasarlanmıştır. Çalışmadan elde edilen sonuçlara göre, kullanıcıların çoğunluğunun niyeti karar verirken hologramdan etkilenmiştir. Ayrıca nesnelerin sırasını ezberlemek için konsantre olan bazı kullanıcıların hologramın farkına varamadığı, bazılarının da hologramın eylemlerini anlayamadığı görülmüştür.

Feature extraction that is detection of effective features is one of the phases of biomedical signal classification. In feature extraction phase, the detection of features that increase performance of classification is very important in terms of diagnosis of disease. Due to this reason, the using of an effective algorithm for feature extraction increases classification accuracy and also it decreases processing time of classifier. In this study, two well-known dictionary-learning algorithms are used to extract features of ECG signals. The features of ECG signals are extracted by using Method of Optimal Direction (MOD) and K-Singular Value Decomposition (K-SVD). However, the extracted features are classified by Artificial Neural Network (ANN). Twelve different ECG signal classes which taken from MIT-BIH ECG Arrhythmia Database are used. When the obtained results are examined, it is seen that performance of classifier increases in usage of K-SVD for feature extraction. The highest classification accuracy is obtained as 98.74% with 5 nonzero elements in [20 1] feature vector, while K-SVD is used in feature extraction phase. The obtained results are assessed by comparing with the results obtained when discrete wavelet transform and principal component analysis are used