KLASTERISASI SELEKSI MAHASISWA CALON PENERIMA BEASISWA YAYASAN MENGGUNAKAN K-MEANS CLUSTERING

<p><em>Perekomendasian untuk penerima beasiswa Yayasan dikelompokkan menjadi 3 clsuter yaitu, diterima, dipertimbangkan dan ditolak sebagai penerima beasiswa yayasan. Algoritma K-Means Clustering merupakan salah satu teknik unsupervised learning yang digunakan untuk merekomendasi penerima beasiswa yayasan. Adapun tujuan dari penelitian ini adalah untuk merekomendasikan calon penerima beasiswa yayasan dengan menggunakan algoritma K-Means Clustering, rekomendasi meghasilkan penempatan data pendaftar beasiswa ke masing-masing kelompok klaster yang dihasilkan. Data pendaftar yang digunakan sebanyak 80 pendaftar. Melalui penyeleksian atribut k-means melakukan perhitungan untuk menempatkan setiap data ke cluster yang sudah ditentukan. Hasil dari perhitungan yang telah diolah sebanyak 16,3% diterima, 61,3% dipertimbangkan dan 22,5% ditolak. </em></p>

Hemodynamic Analysis of Pipeline Embolization Device Stent for Treatment of Giant Intracranial Aneurysm under Unsupervised Learning Algorithm

To treat large intracranial aneurysms, pipeline embolization device (PED) stent with unsupervised learning algorithms was utilized. Unsupervised learning model algorithm was used to screen aneurysm health big data, find aneurysm blood flow and PED stent positioning characteristic parameters, and guide PED stent treatment of intracranial aneurysms. The research objects were 100 patients with intracranial large aneurysm admitted to X Hospital of X Province from June 2020 to June 2021, who were enrolled into two groups. One group used the prototype transfer generative adversarial network (PTGAN) model to measure mean blood flow and mean vascular pressure and guide the placement of PED stents (PTGAN group). The other group did not use the model to place PED (control group). The PTGAN model can learn feature information from horizontal and vertical directions, with smooth edges and prominent features, which can effectively extract the main morphological and texture features of aneurysms. Compared with the convolutional neural network (CNN) model, the accuracy of the PTGAN model increased by 8.449% (87.452%–79.003%), and the precision increased by 8.347% (91.23%–82.883%). The recall rate increased by 7.011% (87.231%–80.22%), and the F1 score increased by 8.09% (89.73%–81.64%). After the adoption of the PTGAN model, the average blood flow inside the aneurysm body was 0.22 (m/s). After the adoption of the CNN model, the average blood flow inside the aneurysm body was 0.21 (m/s), and the difference was 0.01 (m/s), which was considerable ( p < 0.05 ). Through this research, it was found that the PTGAN model was better than the CNN model in terms of accuracy, precision, recall, and F1 score values. The PTGAN model was better than the CNN model in detecting the average blood flow rate and average blood pressure after treatment, and the blood flowed smoothly. Postoperative complications and postoperative relief were also better than those of the control group. In summary, based on the unsupervised learning algorithm, the PED stent had a good adoption effect in the treatment of intracranial aneurysms and was suitable for subsequent treatment.

A survey of unsupervised learning in medical image registration

Medical image registration has important value in actual clinical applications. From the traditional time-consuming iterative similarity optimization method to the short time-consuming supervised deep learning to today's unsupervised learning, the continuous optimization of the registration strategy makes it more feasible in clinical applications. This survey mainly focuses on unsupervised learning methods and introduces the latest solutions for different registration relationships. The registration for inter-modality is a more challenging topic. The application of unsupervised learning in registration for inter-modality is the focus of this article. In addition, this survey also proposes ideas for future research methods to show directions of the future research.