Deteksi Pesan Spam pada Forum Daring Menggunakan Metode Naive Bayes

Forum diskusi daring adalah salah satu layanan edukasi yang disediakan oleh pihak Universitas XYZ yang dapat digunakan oleh dosen dan mahasiswa. Forum ini akan dijadikan sarana untuk mengeluarkan pendapat mahasiswa mengenai pelajaran serta sebagai pendataan absensi kehadiran mahasiswa tersebut. Pada prakteknya sering sekali mahasiswa hanya memberikan tanggapan yang seharusnya tidak diunggah pada forum diskusi berupa pesan spam sehingga fungsi utama dari forum daring sebagai media pembelajaran dan sarana untuk mengeluarkan pendapat menjadi terkesampingkan. Pada studi ini akan menerapkan algoritma Naive Bayes Classifier (NBC) untuk melakukan klasifikasi terhadap pesan forum dimana pesan-pesan tersebut akan dikelompokkan menjadi pesan spam dan pesan non-spam. Hasil uji sistem dengan membagi dataset menjadi 80 data training dan 20 data training, menghasilkan kesimpulan bahwa nilai accuracy sistem sebesar 95%, nilai precision sistem sebesar 100%, dan nilai recall sistem sebesar 90%. PenggunaanalgoritmaNBC dapat dijadikan salah satu alternatif dalam mendeteksi pesan spam pada forum

Prediction model for diabetes mellitus using machine learning algorithms for enhanced diagnosis and prognosis in healthcare

Diabetes mellitus (DM) affects the hormone insulin, which causes improper glucose metabolism and raises the body’s blood sugar levels. With 4.2 million fatalities in 2019, DM is one of the top 10 global causes of mortality. Early detection of DM will aid in its treatment and avert complications. There must be a quick and simple technique to diagnose it. Such diseases can be managed and human lives can be saved with early diagnosis. Smart prediction techniques like Machine Learning (ML) have produced encouraging outcomes in predictive classifications. There has been a lot of interest in ML-based decision-support platforms for the prediction of chronic illnesses to provide improved diagnosis and prognosis help to medical professionals and the general population. By building predictive models using diagnostic medical datasets gathered from DM patients, ML algorithms efficiently extract knowledge that helps predict diabetic individuals. The association between DM and a healthy lifestyle is used in the model. In this study, the NHANES (National Health and Nutrition Examination Survey) data set is utilized, and five ML methods such as Artificial Neural Networks (ANN), CATBoost, XGBoost, XGBoost-histogram, and Light GBM to predict DM. The results of the experiment demonstrate that the XGB-h model outperformed other ML methods regarding area under the receiver operating characteristic curve (AUC-ROC), and accuracy. The most effective XGB-h framework can be used in a mobile app and a website to rapidly forecast DM. Real-time prediction using details delivered by the model at runtime can be developed as a whole bundle as a product. Clinicians can quickly determine who is likely to get diabetes using the proposed strategy, which will facilitate prompt intervention and caring

Design analysis of a hot strip mill runout table bed under ANSYS workbench

A structural analysis has been performed on the setup of a spray cooling system for a model of a hot strip mill. The objective was to design a run out table (ROT) whose length is reduced for a spray cooling system. The setup consists of a bed (ROT) whose height can be adjusted as desired, and a carriage which would be carrying the hot plate, moving along the bed, for experimentation. The CAD modelling was done in CATIA and the analysis was performed in ANSYS Workbench. A prototype of the whole setup was built from the virtual model and was controlled using products from National Instrument Corporation (NI). The analysis is being conducted for the design optimization of the entire setup and its safety

Arsenic in Surface Waters: A Report from River Ganga and its Tributary Jamania at Bhagalpur, Bihar, India

An investigation has been carried out to examine the arsenic pollution status of River Ganga & its tributary Jamania during pre-monsoon period of  2017 at Bhagalpur, Bihar (India). Altogether 17 water samples from different sampling sites along with their geo co-ordinates have been investigated for the value of arsenic using FTK test as well as spectrophotometer method. Throughout the study, arsenic value ranged from 10.69 ppb to 55.92 ppb. Out of the 17 water samples, the values of arsenic in 13 samples were from 20ppb to 54.1ppb. The concentration levels of arsenic in all the 17 river water samples and 2 public water supply samples (source: river water) in the present study were found above from the permissible limit of WHO (2008) and BIS (2004-2005) standards for drinking which is 10 ppb (part per billion)

IoT-Enhanced Healthcare: A Patient Care Evaluation Using the IoT Healthcare Test

Empirical insights into the significant effects of IoT-Enhanced Healthcare on patient care and health outcomes are provided by this study. The transformational potential of IoT technology is shown by data generated from a varied patient group, which includes continuous monitoring of blood pressure, body temperature, heart rate, and blood glucose levels via IoT devices. The usage of IoT devices is directly correlated with greater cardiovascular stability, as shown by consistently normal vital signs, according to statistical assessments. Additionally, the data highlights how patients using IoT devices have better control over their blood glucose levels, as seen by fewer cases of increased glucose levels. Evaluations of the quality of patient care show improved levels of satisfaction, efficacy of therapy, and communication, highlighting the benefits of IoT-Enhanced Healthcare. The evaluation of the outcomes of the IoT Healthcare Test confirms the precision and dependability of IoT devices in medical diagnosis, highlighting the significance of IoT-Enhanced Healthcare in transforming patient care. Together, these results provide strong evidence of IoT's ability to improve patient outcomes, treatment quality, and patient health

Reply to Oren et al., "New Phylum Names Harmonize Prokaryotic Nomenclature"

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COVID-19 Vaccination in Patients with Inborn Errors of Immunity Reduces Hospitalization and Critical Care Needs Related to COVID-19: a USIDNET Report

Background The CDC and ACIP recommend COVID-19 vaccination for patients with inborn errors of immunity (IEI). Not much is known about vaccine safety in IEI, and whether vaccination attenuates infection severity in IEI. Objective To estimate COVID-19 vaccination safety and examine effect on outcomes in patients with IEI. Methods We built a secure registry database in conjunction with the US Immunodeficiency Network to examine vaccination frequency and indicators of safety and effectiveness in IEI patients. The registry opened on January 1, 2022, and closed on August 19, 2022. Results Physicians entered data on 1245 patients from 24 countries. The most common diagnoses were antibody deficiencies (63.7%). At least one COVID-19 vaccine was administered to 806 patients (64.7%), and 216 patients received vaccination prior to the development of COVID-19. The most common vaccines administered were mRNA-based (84.0%). Seventeen patients were reported to seek outpatient clinic or emergency room care for a vaccine-related complication, and one patient was hospitalized for symptomatic anemia. Eight hundred twenty-three patients (66.1%) experienced COVID-19 infection. Of these, 156 patients required hospitalization (19.0%), 47 required ICU care (5.7%), and 28 died (3.4%). Rates of hospitalization (9.3% versus 24.4%, p &lt; 0.001), ICU admission (2.8% versus 7.6%, p = 0.013), and death (2.3% versus 4.3%, p = 0.202) in patients who had COVID-19 were lower in patients who received vaccination prior to infection. In adjusted logistic regression analysis, not having at least one COVID-19 vaccine significantly increased the odds of hospitalization and ICU admission. Conclusion Vaccination for COVID-19 in the IEI population appears safe and attenuates COVID-19 severity

Symptom-based stratification of patients with primary Sjögren's syndrome: multi-dimensional characterisation of international observational cohorts and reanalyses of randomised clinical trials

Background Heterogeneity is a major obstacle to developing effective treatments for patients with primary Sjögren's syndrome. We aimed to develop a robust method for stratification, exploiting heterogeneity in patient-reported symptoms, and to relate these differences to pathobiology and therapeutic response. Methods We did hierarchical cluster analysis using five common symptoms associated with primary Sjögren's syndrome (pain, fatigue, dryness, anxiety, and depression), followed by multinomial logistic regression to identify subgroups in the UK Primary Sjögren's Syndrome Registry (UKPSSR). We assessed clinical and biological differences between these subgroups, including transcriptional differences in peripheral blood. Patients from two independent validation cohorts in Norway and France were used to confirm patient stratification. Data from two phase 3 clinical trials were similarly stratified to assess the differences between subgroups in treatment response to hydroxychloroquine and rituximab. Findings In the UKPSSR cohort (n=608), we identified four subgroups: Low symptom burden (LSB), high symptom burden (HSB), dryness dominant with fatigue (DDF), and pain dominant with fatigue (PDF). Significant differences in peripheral blood lymphocyte counts, anti-SSA and anti-SSB antibody positivity, as well as serum IgG, κ-free light chain, β2-microglobulin, and CXCL13 concentrations were observed between these subgroups, along with differentially expressed transcriptomic modules in peripheral blood. Similar findings were observed in the independent validation cohorts (n=396). Reanalysis of trial data stratifying patients into these subgroups suggested a treatment effect with hydroxychloroquine in the HSB subgroup and with rituximab in the DDF subgroup compared with placebo. Interpretation Stratification on the basis of patient-reported symptoms of patients with primary Sjögren's syndrome revealed distinct pathobiological endotypes with distinct responses to immunomodulatory treatments. Our data have important implications for clinical management, trial design, and therapeutic development. Similar stratification approaches might be useful for patients with other chronic immune-mediated diseases. Funding UK Medical Research Council, British Sjogren's Syndrome Association, French Ministry of Health, Arthritis Research UK, Foundation for Research in Rheumatology