A Framework for Implementing Prediction Algorithm over Cloud Data as a Procedure for Cloud Data Mining

The cloud has become an important phrase in data storage for many reasons. Cloud services and applications are widespread in many industries including healthcare due to easy access. The limitless quantity of data available on the clouds has triggered the interest of many researchers in the recent past. It has forced us to deploy machine learning for analyzing the data to get insights as well as model building. In this paper, we have built a service on Heroku Cloud which is a cloud platform as a service (PaaS) and has 15 thousand records with 25 features. The data belongs to healthcare and is related to post-surgery complications. The boost prediction algorithm was applied for analysis and implementation was done in python. The results helped us to determine and tune some of the hyperparameters which have correlations with complications and the reported accuracy of training and testing was found to be 91% and 88% respectively

Eksitonska stanja u kvantnoj točki

The exciton binding energies in finite-potential quantum dot discs of GaAs are obtained and the eigenstates and the eigenvalues of the exciton are calculated. We present the exciton binding energy for different values of the disc radius (R) and the disc half-width (L/2). The exciton-state stability for large and small sizes of the dot is discussed. We compare our results with the existing theoretical and experimental results. Our results give good estimates for the optimal quantum dot disc geometry, and represent useful data in studies of the optical properties of quantum dots in nano-scale devices.Izračunali smo energiju vezanja, svojstvena stanja i svojstvene vrijednosti eksitona u kvantnoj točki s konačnim potencijalom. Opisujemo energiju vezanja eksitona za više vrijednosti polumjera (R) i poluširine (L/2) diska. Raspravljamo stabilnost eksitona za male i veće dimenzije diska. Uspoređujemo naše rezultate s poznatim drugim teorijskim i eksperimentalnim rezultatima. Naši rezultati daju dobre ocjene za povoljnu veličinu diska kvantne točke, i predstavljaju korisne podatke za proučavanje optičkih svojstava kvantnih točaka u napravama nano veličine

Eksitonska stanja u kvantnoj točki

The exciton binding energies in finite-potential quantum dot discs of GaAs are obtained and the eigenstates and the eigenvalues of the exciton are calculated. We present the exciton binding energy for different values of the disc radius (R) and the disc half-width (L/2). The exciton-state stability for large and small sizes of the dot is discussed. We compare our results with the existing theoretical and experimental results. Our results give good estimates for the optimal quantum dot disc geometry, and represent useful data in studies of the optical properties of quantum dots in nano-scale devices.Izračunali smo energiju vezanja, svojstvena stanja i svojstvene vrijednosti eksitona u kvantnoj točki s konačnim potencijalom. Opisujemo energiju vezanja eksitona za više vrijednosti polumjera (R) i poluširine (L/2) diska. Raspravljamo stabilnost eksitona za male i veće dimenzije diska. Uspoređujemo naše rezultate s poznatim drugim teorijskim i eksperimentalnim rezultatima. Naši rezultati daju dobre ocjene za povoljnu veličinu diska kvantne točke, i predstavljaju korisne podatke za proučavanje optičkih svojstava kvantnih točaka u napravama nano veličine

Pengaruh Arus Sepanjang Pantai (Longshore Current) terhadap Sebaran Sedimen Dasar di Perairan Teluk Awur, Jepara

Mekanisme berpindahnya sedimen dari satu tempat ke tempat yang lain sangat dipengaruhi oleh longshore current(arus sepanjang pantai), hal ini menyebabkan terjadinya abrasidiwilayah Perairan Teluk Awur akibat penjalaran gelombang yang dibangkitkan oleh angin (gelombang permukaan). Tujuan dilakukannya penelitian ini untuk mengetahui pengaruh dari arus sepanjang pantai terhadap angkutan sedimen dasar yang ada di Perairan Teluk Awur, Jepara. Penelitian ini dilaksanakan tanggal 2-5 Maret 2015 di Perairan Teluk Awur, Jepara. Metode yang digunakan dalam penelitian ini adalah kuantitaif. Peramalan gelombang laut menggunakan metode SMB (Sverdrup Munk Bretchneider) dengan inputan data angin, sedangkan untuk menentukan transport sedimen menggunakan rumus empiris yang didapat dari pengaruh gelombang.Hasil penelitian di Teluk Awur menunjukkan tinggi gelombang pecah ( ) berkisar antara 0,4 – 1,5 meter dengan kedalaman gelombang pecah berkisar antara ( ) 0,5 – 1,8 meter. Arus sepanjang pantai kecepatannya berkisar antara 0,7 – 2,0 m/s dengan arah cenderung menuju utara, karena gelombang dominan datang dari arah barat dan bentuk dari daratan Teluk Awur. Dominasi jenis sedimen di Teluk Awur berupa pasir dan gravely sand, dengan potensi angkutan sedimen berkisar 65,3 – 2.176 m³/hari atau 23.824 – 794.547 m³/tahun

Enhancing the Fake News Classification Model Using Find-Tuning Approach

Over the last few years, the rise of fake news on social media has emerged as a significant issue, posing a potential threat to individuals, organizations, and society as a whole. As a solution to this issue, researchers have been using various natural language processing (NLP) techniques to detect fake news. In this study, we introduce a new strategy for fake news detection and classification. Our approach involves enhancing the performance of accuracy through fine-tuning, by merging BEART model with the proposed model DCNN. We have collected the data from secondary sources and combined it into a unified dataset. To improve its quality, we performed various processes such as data cleaning, transformation, integration, and reduction, which involved techniques like stop word removal, tokenization, and stemming, resulting in binary classification. Therefore, DCNN" was trained to classify news articles as real or fake, and the experiments on the dataset show that this approach performs better than several recent studies for detecting fake news, achieving high accurac

Studi Dinamika Ekosistem Perairan di Teluk Lampung: Pemodelan Gabungan Hidrodinamika-Ekosistem

Penelitian ini bertujuan untuk mengkaji dinamika ekosistem perairan di Teluk Lampung dengan menggunakan gabungan model hidrodinamika-ekosistem dengan pendekatan numerik. Secara umum, hasil simulasi pola arus residu M2 cenderung masuk dari mulut teluk sebelah barat, sebagian terus memasuki sampai kepala teluk dan sebagian keluar kembali dari mulut teluk bagian timur. Selain itu, terlihat pula adanya suatu eddy yang mengalir berlawanan arah jarum jam di sekitar kepala teluk. Pola penyebaran masing-masing kompartimen ekosistem hasil model memiliki kesamaan dengan hasil pengamatan di lapangan, serta konsisten dengan pola arus residu M2. Pengaruh suplai dari sungai, interaksi antara proses biologis seperti produktifitas primer, sekunder (pemangsaan), kematian alami plankton, serta proses dekomposisi oleh bakteri belum begitu berperan dalam neraca dan standing stock ekosistem di Teluk Lampung. Peranan suplai dari laut lebih dominan dibanding dengan proses-proses biokimiawi yang berinteraksi di dalam teluk. Hasil perhitungan tingkat efisiensi aliran energi dari proses dekomposisi dan produksi urine zooplankton ke produktifitas primer mengalami kehilangan sebesar 30.48 %, sementara dari produktifitas primer ke produktifitas sekunder (pemangsaan) mengalami penambahan 17.24 %

3,5-Bis(4-bromo­phen­yl)-1-phenyl-4,5-dihydro-1H-pyrazole

In the title compound, C21H16Br2N2, the central pyrazole ring adopts an flattened envelope conformation, with the stereogenic C atom in the flap position. The deviations from planarity for this ring are relatively minor (r.m.s. deviation = 0.045 Å) and the dihedral angles formed with the N- and Cimine-bound benzene rings are 7.73 (13) and 11.00 (13)°, respectively. By contrast, the benzene ring bound at the chiral C atom is almost orthogonal to the rest of the mol­ecule; the dihedral angle formed between this ring and the pyrazole ring is 79.53 (13)°. In the crystal, the packing is stabilized by C—H⋯N and C—H⋯Br inter­actions

CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison

Large, labeled datasets have driven deep learning methods to achieve expert-level performance on a variety of medical imaging tasks. We present CheXpert, a large dataset that contains 224,316 chest radiographs of 65,240 patients. We design a labeler to automatically detect the presence of 14 observations in radiology reports, capturing uncertainties inherent in radiograph interpretation. We investigate different approaches to using the uncertainty labels for training convolutional neural networks that output the probability of these observations given the available frontal and lateral radiographs. On a validation set of 200 chest radiographic studies which were manually annotated by 3 board-certified radiologists, we find that different uncertainty approaches are useful for different pathologies. We then evaluate our best model on a test set composed of 500 chest radiographic studies annotated by a consensus of 5 board-certified radiologists, and compare the performance of our model to that of 3 additional radiologists in the detection of 5 selected pathologies. On Cardiomegaly, Edema, and Pleural Effusion, the model ROC and PR curves lie above all 3 radiologist operating points. We release the dataset to the public as a standard benchmark to evaluate performance of chest radiograph interpretation models. The dataset is freely available at https://stanfordmlgroup.github.io/competitions/chexpert .Comment: Published in AAAI 201