United World College of the Adriatic

Abstract This study aims to analyze the influence of cash holding dan firm value on income smoothing. In 13 companies obtained with IPO criteria before 2018 and have complete financial report data for research . The secondary data source used was financial reports on the Indonesian Stock Exchange. Data analysis was carried out using multiple linear regression techniques. The research results show that cash holding have a positive effect on income smoothing, and firm value has no effect on income smoothing. The implications of these findings can be used as a basis for assessing the possibility of income smoothing practiced carried out by the company. Keywords:: Cash Holding, Firm Value , and Income Smoothing Abstrak Kajian ini bertujuan untuk menganalisis pengaruh cash holding dan firm value terhadap Income Smoothing pada 13 perusahaan yang didapatkan dengan kriteria IPO sebelum tahun 2018 dan memiliki data laporan keuangan lengkap untuk penelitian. Sumber data sekunder yang digunakan adalah laporan keuangan di Bursa Efek Indonesia. Analisis data dilakukan melalui teknik regresi linear berganda. Hasil penelitian menunjukkan bahwa cash holding berpengaruh positif terhadap income smoothing, sedangkan firm value tidak berpengaruh terhadap income smoothing. Implikasi temuan ini dapat digunakan sebagai dasar penilain terhadap kemungkinan praktek income smoothing yang dilakukan perusahaan.

Alzheimer’s disease (AD) is a degenerative, incurable neurological disorder that progressively damages cognitive abilities. AD affects millions of people worldwide. The biopsy method is the most reliable method to identify AD, but it has the chance to cause irreparable harm. There are many non-invasive alternatives to biopsies that can be used to diagnose AD without undue risk. One of these alternatives comprises computer-assisted diagnostic systems, which enable the identification of brain-impairing illnesses/diseases. This paper used Magnetic Resonance Imaging scans of brains with four different classes to create a model to detect AD. The open-source OASIS dataset, which serves as the basis for the study’s data, was split into an 80% training set and a 20% test set. The dataset consisted of classes of moderate AD, mild AD, very mild AD, and non-AD scans. Five different Convolutional Neural Network methods were used for classification. The DenseNet-121, ResNet-50, ResNet-18, and AlexNet methods achieved detection accuracies of 90.5%, 95.1%, 88.4% and 70.5% respectively. The EfficientNetB-7 method failed to identify many cases of AD.