Network-Based Approach and IVI Methodologies, a Combined Data Investigation Identified Probable Key Genes in Cardiovascular Disease and Chronic Kidney Disease

In fact, the risk of dying from CVD is significant when compared to the risk of developing end-stage renal disease (ESRD). Moreover, patients with severe CKD are often excluded from randomized controlled trials, making evidence-based therapy of comorbidities like CVD complicated. Thus, the goal of this study was to use an integrated bioinformatics approach to not only uncover Differentially Expressed Genes (DEGs), their associated functions, and pathways but also give a glimpse of how these two conditions are related at the molecular level. We started with GEO2R/R program (version 3.6.3, 64 bit) to get DEGs by comparing gene expression microarray data from CVD and CKD. Thereafter, the online STRING version 11.1 program was used to look for any correlations between all these common and/or overlapping DEGs, and the results were visualized using Cytoscape (version 3.8.0). Further, we used MCODE, a cytoscape plugin, and identified a total of 15 modules/clusters of the primary network. Interestingly, 10 of these modules contained our genes of interest (key genes). Out of these 10 modules that consist of 19 key genes (11 downregulated and 8 up-regulated), Module 1 (RPL13, RPLP0, RPS24, and RPS2) and module 5 (MYC, COX7B, and SOCS3) had the highest number of these genes. Then we used ClueGO to add a layer of GO terms with pathways to get a functionally ordered network. Finally, to identify the most influential nodes, we employed a novel technique called Integrated Value of Influence (IVI) by combining the network's most critical topological attributes. This method suggests that the nodes with many connections (calculated by hubness score) and high spreading potential (the spreader nodes are intended to have the most impact on the information flow in the network) are the most influential or essential nodes in a network. Thus, based on IVI values, hubness score, and spreading score, top 20 nodes were extracted, in which RPS27A non-seed gene and RPS2, a seed gene, came out to be the important node in the network.

On the Protection of Personal Data Information in Big Data Investigation Activities

In the context of the vigorous development of big data and network communication technologies, the universality of personal data information processing, the openness of concept definitions, and the potential risks in practice have led to theoretical and practical changes in the definition of personal data information. With the widespread use of big data technology in criminal investigations, the theoretical and practical activities of big data investigation have gradually formed. Big data investigation activities are often accompanied by infringements on citizens' personal data and other legitimate rights and interests. At present, the traditional model of personal data information protection cannot restrict the code of conduct in big data investigation activities. For this reason, it is necessary to introduce a comprehensive governance model, which mainly includes relative control of individuals, balance of multiple interests and dynamic risk adjustment. Etc., and focus on the transformation of the legal protection model of personal information.

Diabetes Risk Forecasting Using Logistic Regression

Diabetes can be a collection of metabolic problems and lots of human beings are affected. Diabetes Mellitus can be caused by a variety of factors including age, stoopedness, lack of activity, inherited diabetes, lifestyle, poor eating habits, hypertension, and so on. Diabetics are more likely to develop diseases like coronary illness, kidney contamination, eye sickness, stroke and other risks. Distributed computing and Internet of Things (IoT) are two instruments that assume a vital part in the present life with respect to numerous angles and purposes including medical care observing of patients and old society. Diabetes Healthcare Monitoring Services are vital these days on the grounds that and that to distant medical care observing in light of the fact that truly going to clinics and remaining in a line is exceptionally ineffectual adaptation of patient checking. Current practice in emergency clinic is to gather required data for diabetes conclusion through different tests and proper treatment is given dependent on analysis. Utilizing enormous data investigation can consider large datasets and discover covered up data, uncertain examples to find information from the data and expect the outcome as demand. Diabetics are caused because of a tremendous uphill in the blood partition containing glucose. There is an advancement conspire accessible using train test split and K overlay cross approval utilizing Scikit learn technique. Various ML algorithms consisting of SVM, RF, KNN, NB, Decision Tree and Logistic Regression are also used.

A Deep Reinforcement Learning for Medical Trajectory Data Clustering Type 2 Diabetes

Deep Reinforcement learning is incorporated in trajectory data clustering to investigate the trajectories gathered from medical information’s. Generally Trajectory mining determines the patterns in data, detects anomalies, and does informative clustering, location prediction, and classification. The main intent of Medical trajectory data clustering is identifying the trajectories with identical patterns for better patient treatment outcomes. Medical trajectory data stored in a multidimensional format which is further processed using the machine learning and deep learning architectures. Machine learning approaches employed to mine trajectory data and identifying the future treatment is a complicated task. To deal with this, the deep learning approaches in trajectory mining concentrate to eliminate the computational complexity on type 2 diabetic’s data. To overcome this problem, deep reinforcement learning for medical trajectory data clustering approach is proposed that is a combination of various strategies to flexible adapt to changes of the trajectory data. After the proposed pre-processing and feature transformation, features are clustered on basis of the weights of the model with lesser efforts and the proposed clustering plays a key role in the process of multi-attribute trajectory data investigation. The proposed deep learning methodology is more suitable for clustering the multi-attribute trajectory with fewer complexity computations than existing machine learning based methods. The experimental results also states that the results of deep reinforcement learning are promising than the other approaches with respect to precision, Recall and F Measure respectively.

First horizontal and first vertical derivatives from high resolution aeromagnetic data over the Gongola basin upper Benue trough Northeastern Nigeria

High-resolution aeromagnetic data investigation was carried out over the Gongola basin upper Benue trough northeastern Nigeria. Total intensity magnetic map were processed to get the residual map, reduction to equator, polynomial fitting, radially average power spectrum (RAPS) were done, subsequently the first horizontal and first vertical derivatives of the data was carried out in order to identify linear structures (faults and fractures). The outcomes from both the horizontal and vertical derivatives give contact locations that are continuous, thin and show major structures in the NE-SW direction both in the maps and in the rose diagrams. The study focused on delineation of geological structures such as rock contacts; rock boundaries, fractures and faulted zones from the maps, they are principally important in mineral resources studies because many of these resources are located along fracture zones. Linear structures perceived in this kind of studies are also reliable indicators for geologic structures. The result of this work is also significant in identifying areas to be avoided when constructing bridges, dams as well as siting nuclear power plants and delineation of potential risk areas of natural hazard. Keywords: Aeromagnetic Data, first Horizontal Derivative, first Vertical Derivatives, Gongola Basin, Linear Features.

PENGARUH PELATIHAN MOTIVASI DAN KOMPENSASI TERHADAP KINERJA KARYAWAN BAGIAN SPINING 2 PT. SARI WARNA ASLI II BOYOLALI

Di era globalisasi seperti sekarang hampir seluruh perusahan di tuntut untuk menambah kemampuan sumber daya manusianya agar tujuan perusahaan dapat tercapai. Maka penulis melakukan penelitian yang berhubungan dengan kinerja karyawan. Bagian Spining 2 PT. Sari Warna Asli II Boyolali merupakan departemen baru. Esensi dari penelitian ini iyalah agar dapat mengukur dan bisa mengetahui tingkat kerja karyawan pada konsep pelatihan, motivasii serta kompensasi pada kerja spinning 2 PT Sari Warna Asli II Boyolali. Pada penelitian yang di lakukan metode yang di gunakan iyalah metode kuantitatif. Cara pengambilan data melalui kuesioner dan stadi dokumen. Dengan populasi 635 karyawan, sampel pada data ini adalah 250 responden karyawan bagian Spining 2 PT. Sari Warna Asli II Boyolali. Teknik penyelidikan data yang dipakaiyaitu regresi linear berganda dengan menggunakan SPSS 22. Hasil riset ini dapat ditarik kesimpulan bahwa pelatihan,motivasi, dan kompensasi berpengaruh positif dan signifikan secara simultan dan parsial terhadap produktivitas karyawan. In this era of globalization, almost all companies are required to increase the capacity of their human resources so that company goals can be achieved. So the authors conducted research related to employee performance. Spining Section 2 PT. Sari Warna Asli II Boyolali is a new department. The essence of this research is to be able to measure and be able to know the work level of employees on the concept of training, motivation and compensation at spinning 2 work at PT Sari Warna Asli II Boyolali. In research, the method used is the quantitative method. How to collect data through questionnaires and document stadi. With a population of 635 employees, the sample in this data is 250 respondents employees of the Spining 2 PT. Sari Warna Asli II, Boyolali. The data investigation technique used is multiple linear regression using SPSS 22. The result of this researc can be concluded thats training, motivation, and compensation have a positive and significant effect simultaneously and partially on employee productivity..