Pub Date : 2022-07-17DOI: 10.47709/ijmdsa.v1i1.1612
Ayesha Muazzam
I am writing this letter to you in order to bring light toward the special issue of poultry production in Pakistan. There are several diseases in poultry which causing stress in poultry production. Some are viral diseases which don’t have treatment [1, 2, 3, 4, 5]. Only vaccination and biosecurity measures can prevent disease. There are some viral diseases which are effecting poultry badly, Newcastle Disease, Infectious Bronchitis, Marek’s Disease, Infectious Chicken Anemia, and Infectious Bursal Disease. Some are bacterial diseases which can be treated through antibiotics [6, 7, 8, 9]. There are some bacterial diseases which are effecting poultry badly, Infectious Coryza, MG, Salmonella diseases are some major bacterial disease. There are some Protozoal diseases which are effecting poultry production and these can be treated through anti-protozoal drugs.
{"title":"Disease stress in Poultry; Letter to editor","authors":"Ayesha Muazzam","doi":"10.47709/ijmdsa.v1i1.1612","DOIUrl":"https://doi.org/10.47709/ijmdsa.v1i1.1612","url":null,"abstract":"I am writing this letter to you in order to bring light toward the special issue of poultry production in Pakistan. There are several diseases in poultry which causing stress in poultry production. Some are viral diseases which don’t have treatment [1, 2, 3, 4, 5]. Only vaccination and biosecurity measures can prevent disease. There are some viral diseases which are effecting poultry badly, Newcastle Disease, Infectious Bronchitis, Marek’s Disease, Infectious Chicken Anemia, and Infectious Bursal Disease. Some are bacterial diseases which can be treated through antibiotics [6, 7, 8, 9]. There are some bacterial diseases which are effecting poultry badly, Infectious Coryza, MG, Salmonella diseases are some major bacterial disease. There are some Protozoal diseases which are effecting poultry production and these can be treated through anti-protozoal drugs.","PeriodicalId":243191,"journal":{"name":"International Journal of Multidisciplinary Sciences and Arts","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115808514","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}
Pub Date : 2022-06-22DOI: 10.47709/ijmdsa.v1i1.2271
Moazzam Siddiq
Machine learning algorithms have shown promise in predicting the likelihood of a patient developing a disease or condition. Early diagnosis of diseases such as cancer, diabetes, and cardiovascular diseases can improve the patient's outcomes and quality of life. In this paper, we review the current state of machine learning algorithms for disease prediction and discuss their potential applications in clinical practice. We start by discussing the types of data used for disease prediction, including clinical data, genetic data, and imaging data. We then review the different types of machine learning algorithms used for disease prediction, including logistic regression, decision trees, random forests, and deep learning. We discuss the advantages and limitations of each algorithm and provide examples of their applications in disease prediction. Next, we discuss the challenges associated with implementing machine learning algorithms in clinical practice, such as data privacy concerns and the need for high-quality data. We also discuss the ethical considerations associated with the use of machine learning algorithms for disease prediction. Finally, we highlight the potential benefits of using machine learning algorithms for disease prediction, including improved patient outcomes, reduced healthcare costs, and personalized medicine. We conclude that machine learning algorithms have the potential to revolutionize disease prediction and early diagnosis, but further research is needed to address the challenges associated with their implementation in clinical practice.
{"title":"Use of Machine to predict patient developing a disease or condition for early diagnose","authors":"Moazzam Siddiq","doi":"10.47709/ijmdsa.v1i1.2271","DOIUrl":"https://doi.org/10.47709/ijmdsa.v1i1.2271","url":null,"abstract":"Machine learning algorithms have shown promise in predicting the likelihood of a patient developing a disease or condition. Early diagnosis of diseases such as cancer, diabetes, and cardiovascular diseases can improve the patient's outcomes and quality of life. In this paper, we review the current state of machine learning algorithms for disease prediction and discuss their potential applications in clinical practice. We start by discussing the types of data used for disease prediction, including clinical data, genetic data, and imaging data. We then review the different types of machine learning algorithms used for disease prediction, including logistic regression, decision trees, random forests, and deep learning. We discuss the advantages and limitations of each algorithm and provide examples of their applications in disease prediction. Next, we discuss the challenges associated with implementing machine learning algorithms in clinical practice, such as data privacy concerns and the need for high-quality data. We also discuss the ethical considerations associated with the use of machine learning algorithms for disease prediction. Finally, we highlight the potential benefits of using machine learning algorithms for disease prediction, including improved patient outcomes, reduced healthcare costs, and personalized medicine. We conclude that machine learning algorithms have the potential to revolutionize disease prediction and early diagnosis, but further research is needed to address the challenges associated with their implementation in clinical practice. \u0000 ","PeriodicalId":243191,"journal":{"name":"International Journal of Multidisciplinary Sciences and Arts","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115723934","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}
Pub Date : 2022-05-15DOI: 10.47709/ijmdsa.v1i1.1463
Muhammad Lukman Haris Firmansah
Learning statistics generally still presented in the form of stories in the books. This study aims to determine the visualization of the basic concepts of presenting statistical data using video. video designs stories into real stories experienced by students so that fact objectivity is created. it aims to make the messages in the story match the experiences experienced by students. this creating recaling knowledge and creating understanding. The research method used in this research is qualitative research with the phenomenological type, where the video is visually used to present messages, namely the concept of presenting data in tables and diagrams. The presentation technique in the video is in the form of a message demonstration that contains facts, concepts, procedures and principles. As for the data analysis used later, namely the observation data, interviews and documentation after using the video by means of data triangulasi. The research of this study were to compare the observation data, interviews and documentation in the form of student understanding data. Based on the observation data, it shows that students pay attention to the video and can explain the story again in the video. interview data shows and answers many type of data and its presentation. documentation data in the form of activity data in class at the second meeting compared to data at the first meeting without using video. Observations, interviews and the results obtained that observations with the aim of knowing whether students understand the sequence in the story is worth 74%, remembering the presentation data 86%, understanding the explanation of data in tables and diagrams 84%, explaining and explaining problems when the video is repeated 75%. This data was obtained when making observations and asking students when they were shown a video.
{"title":"Visualization and Message Design Concepts of Presenting Statistical Data through Videos to Improve Understanding","authors":"Muhammad Lukman Haris Firmansah","doi":"10.47709/ijmdsa.v1i1.1463","DOIUrl":"https://doi.org/10.47709/ijmdsa.v1i1.1463","url":null,"abstract":"Learning statistics generally still presented in the form of stories in the books. This study aims to determine the visualization of the basic concepts of presenting statistical data using video. video designs stories into real stories experienced by students so that fact objectivity is created. it aims to make the messages in the story match the experiences experienced by students. this creating recaling knowledge and creating understanding. The research method used in this research is qualitative research with the phenomenological type, where the video is visually used to present messages, namely the concept of presenting data in tables and diagrams. The presentation technique in the video is in the form of a message demonstration that contains facts, concepts, procedures and principles. As for the data analysis used later, namely the observation data, interviews and documentation after using the video by means of data triangulasi. The research of this study were to compare the observation data, interviews and documentation in the form of student understanding data. Based on the observation data, it shows that students pay attention to the video and can explain the story again in the video. interview data shows and answers many type of data and its presentation. documentation data in the form of activity data in class at the second meeting compared to data at the first meeting without using video. Observations, interviews and the results obtained that observations with the aim of knowing whether students understand the sequence in the story is worth 74%, remembering the presentation data 86%, understanding the explanation of data in tables and diagrams 84%, explaining and explaining problems when the video is repeated 75%. This data was obtained when making observations and asking students when they were shown a video.","PeriodicalId":243191,"journal":{"name":"International Journal of Multidisciplinary Sciences and Arts","volume":"381 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122777937","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}
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