International Journal of Applied and Natural Sciences最新文献

Analysis of Applications Principles of Barcode Technology in Product Cost Estimation and Identification 条码技术在产品成本估算与识别中的应用原理分析

International Journal of Applied and Natural Sciences Pub Date : 2023-10-26 DOI: 10.61424/ijans.v1i2.21

Dickson, Makasda Solomon

引用次数: 0

Breaking Barriers in Kidney Disease Detection: Leveraging Intelligent Deep Learning and Artificial Gorilla Troops Optimizer for Accurate Prediction 突破肾脏疾病检测的障碍:利用智能深度学习和人工大猩猩部队优化器进行准确预测

International Journal of Applied and Natural Sciences Pub Date : 2023-09-04 DOI: 10.61424/ijans.v1i1.8

S. Satyanarayana, Thayyaba Khatoon MD, N V Madhu Bindu

{"title":"Breaking Barriers in Kidney Disease Detection: Leveraging Intelligent Deep Learning and Artificial Gorilla Troops Optimizer for Accurate Prediction","authors":"S. Satyanarayana, Thayyaba Khatoon MD, N V Madhu Bindu","doi":"10.61424/ijans.v1i1.8","DOIUrl":"https://doi.org/10.61424/ijans.v1i1.8","url":null,"abstract":"In this groundbreaking study, we propose an innovative approach to tackle the formidable task of early detectionand accurate prediction of kidney diseases. By harnessing the potential of a comprehensive healthcare dataset andleveraging a machine learning model originally developed for kidney disease diagnosis, our methodology integratesintelligent feature selection techniques. These techniques, including heuristic based feature selection andevolutionary gravitational search-based feature selection (EGS-FS), allow us to identify the most informative featuresfor accurate prediction. Classification is performed using our newly designed Intelligent Deep Learning basedClassifier, which is further optimized using the cutting-edge Artificial Gorilla Troops Optimizer algorithm. Toassess the performance of our proposed model, we conduct a thorough evaluation and comparison against existingmethods using a range of statistical measures. Remarkably, our experimental results on the widely recognizedChronic Kidney Disease dataset showcase an exceptional accuracy value of 99%. This research not only contributesto the advancement of kidney disease prediction but also provides invaluable insights for efficient patientmanagement. By embracing this novel approach, clinicians can make informed decisions and revolutionize the fieldof kidney disease detection and treatment.","PeriodicalId":478033,"journal":{"name":"International Journal of Applied and Natural Sciences","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135497305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Constructing the Future: Unveiling Novel Approaches to Entrepreneurship Development in Construction and Contracting Industries 构建未来:揭示建筑和承包行业创业发展的新途径

International Journal of Applied and Natural Sciences Pub Date : 2023-09-03 DOI: 10.61424/ijans.v1i1.12

Sameer Jain

引用次数: 0

Arsenic Adsorption on Designed Packed Bed Column using Chemically Modified PAN Fiber- A Simulation and Modeling Studies. 化学改性聚丙烯腈纤维在设计填料床柱上吸附砷的模拟与建模研究。

International Journal of Applied and Natural Sciences Pub Date : 2023-09-02 DOI: 10.61424/ijans.v1i1.14

Muhammad suleman Memon, Imran Nazir Unar, Khadija Qureshi

{"title":"Arsenic Adsorption on Designed Packed Bed Column using Chemically Modified PAN Fiber- A Simulation and Modeling Studies.","authors":"Muhammad suleman Memon, Imran Nazir Unar, Khadija Qureshi","doi":"10.61424/ijans.v1i1.14","DOIUrl":"https://doi.org/10.61424/ijans.v1i1.14","url":null,"abstract":"The efficiency of Chemically modified PAN Fiber (CM-PAN) as an adsorbent for the removal of As(III) from groundwater performed. For polluted water, many techniques have been developed recently to remove arsenic from it, such as chemical precipitation, adsorption, ion exchange, membrane processes, and others. Adsorption is the most often used technique because of its high arsenic performance and low cost. Arsenic (III) was removed from the aqueous solution in this study that use an adsorption technique with chitosan/iron oxide coated PAN Fiber as the adsorbent. In this work, Aspen AdsimV11 was used to create a steady-state model of a fixed bed column in the impact of various process factors. We created a small scale column of 2-cm-diameter and with various bed depths such as 1, 2 & 3cm. By an up-flow rate of 1, 2 & 3 mL/min and large scale column 8-cm-diameter and with various bed depths such as 8cm and by an up-flow rate of 512, 1024 & 1536mL/min. The amount of polluted water that was react towards the breakpoints where adsorption capacity was 91%of As(III) in a concentration of 0.0004004 mol/liter, 88%of As(III) in a concentration of 0.000269 mol/liter and 85% in a concentration of 0.0001335 mol/lit. By increasing starting concentration and bed depth and, total As (III) uptake decreased with increasing flow rate. Bed depth service time (BDST), mass transfer, first order kinetics, and regression analysis of equilibrium data were used to simulate the dynamics of the adsorption process and fit the Langmuir isotherms. The water quality of the treated effluent is adequate for home use, according to the findings. Ion exchange and physiosorption on the adsorbent surface appeared to be the mechanisms for As(III) removal in the CM-PAN column. For arsenic removal from groundwater, this appropriate module would be generate by using these results.","PeriodicalId":478033,"journal":{"name":"International Journal of Applied and Natural Sciences","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134969802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Data-Driven Machine Learning Techniques for the Prediction of Cholera Outbreak in West Africa 预测西非霍乱爆发的数据驱动机器学习技术

International Journal of Applied and Natural Sciences Pub Date : 2023-08-25 DOI: 10.61424/ijans.v1i1.5

Onyijen O. H., Olaitan E.O, Olayinka T. C, Oyelola S.

{"title":"Data-Driven Machine Learning Techniques for the Prediction of Cholera Outbreak in West Africa","authors":"Onyijen O. H., Olaitan E.O, Olayinka T. C, Oyelola S.","doi":"10.61424/ijans.v1i1.5","DOIUrl":"https://doi.org/10.61424/ijans.v1i1.5","url":null,"abstract":"The cholera epidemic remains a public threat throughout history, affecting vulnerable populations living with unreliable water and substandard sanitary conditions. Various studies have observed that the occurrence of cholera has a strong linkage with environmental factors such as climate change and geographical location. Poor Hygiene has been strongly linked to the seasonal occurrence and widespread of cholera through the creation of weather patterns that favour the disease’s transmission, infection, and the growth of Vibrio cholerae, which cause the disease. Over the past decades, there have been great achievements in developing epidemic models for the proper prediction of cholera. However, machine learning techniques have not been explicitly deployed in modelling cholera epidemics due to the challenges that come with its datasets, such as imbalanced data and missing information. This paper explores the use of machine learning algorithms such as decision tree, random forest, and logistics regression to evaluate the prevalence of cholera epidemics in West African countries while overcoming the data imbalance problem. In addition, mean square error, mean absolute error, F1 score, precision and balanced accuracy metrics were used to evaluate the performance of the three (3) models. The results show that logistic regression has an accuracy of 0.47%, random forest 0.978% and the most efficient model was the decision tree 0.998% with a mean squared error and mean absolute error of 0.001% respectively shows that the model will accurately predict cholera outbreak in Africa. Overall results will improve the understanding of the significant roles of machine learning techniques in healthcare data. The study recommends a review of healthcare systems to facilitate quality data collection and deployment of machine learning techniques","PeriodicalId":478033,"journal":{"name":"International Journal of Applied and Natural Sciences","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134982691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0