Over the past few years, short message service (SMS) usage has significantly increased. This service is used to deliver text messages by billions of people. Service providers have launched a number of popular applications, including mobile banking, summons checkpoints, SMS chat, and others. This chapter explores the numerous SMS applications that are available to users and provides an outline of how this service is provided. We examine the causes of its success and the problems that need to be solved. We also look at upcoming trends and the difficulties that to improve this service, certain obstacles must be overcome. This chapter should help you understand how SMS applications work and what to expect from them going forwards given the improvements to current SMS and technological advancement. We propose a privacy-preserving Naive Bayes classifier and apply it to the problem of private text classification. In this setting, a party (Alice) holds a text message, while another party (Bob) holds a classifier. At the end of the protocol, Alice will only learn the result of the classifier applied to her text input and Bob learns nothing. Our solution is based on Secure Multiparty Computation (SMC). Our Rust implementation provides a fast and secure solution for the classification of unstructured text. Applying our solution to the case of spam detection (the solution is generic, and can be used in any other scenario in which the Naive Bayes classifier can be employed), we can classify an SMS as spam or ham in less than 340 ms in the case where the dictionary size of Bob’s model includes all words (n 5200) and Alice’s SMS has at most m 160 unigrams. In the case with n 369 and m 8 (the average of a spam SMS in the database), our solution takes only 21 ms.
{"title":"Classifying Message Application Using Kotlin","authors":"","doi":"10.46632/daai/3/1/22","DOIUrl":"https://doi.org/10.46632/daai/3/1/22","url":null,"abstract":"Over the past few years, short message service (SMS) usage has significantly increased. This service is used to deliver text messages by billions of people. Service providers have launched a number of popular applications, including mobile banking, summons checkpoints, SMS chat, and others. This chapter explores the numerous SMS applications that are available to users and provides an outline of how this service is provided. We examine the causes of its success and the problems that need to be solved. We also look at upcoming trends and the difficulties that to improve this service, certain obstacles must be overcome. This chapter should help you understand how SMS applications work and what to expect from them going forwards given the improvements to current SMS and technological advancement. We propose a privacy-preserving Naive Bayes classifier and apply it to the problem of private text classification. In this setting, a party (Alice) holds a text message, while another party (Bob) holds a classifier. At the end of the protocol, Alice will only learn the result of the classifier applied to her text input and Bob learns nothing. Our solution is based on Secure Multiparty Computation (SMC). Our Rust implementation provides a fast and secure solution for the classification of unstructured text. Applying our solution to the case of spam detection (the solution is generic, and can be used in any other scenario in which the Naive Bayes classifier can be employed), we can classify an SMS as spam or ham in less than 340 ms in the case where the dictionary size of Bob’s model includes all words (n 5200) and Alice’s SMS has at most m 160 unigrams. In the case with n 369 and m 8 (the average of a spam SMS in the database), our solution takes only 21 ms.","PeriodicalId":226827,"journal":{"name":"Data Analytics and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130893549","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}
The majority of people worldwide are affected by chronic liver disease, which is the leading cause of death worldwide. It is now extremely challenging for researchers in the healthcare industry to predict diseases from the extensive databases. We use classification algorithms from machine learning to resolve this issue. Predicting liver disease is the primary objective of this project. We have implemented five machine learning algorithms: Naive Bayes, SVM, K-Nearest Neighbor, Logistic Regression, and Random Forest. The comparison of these classifier algorithms is entirely based on performance, classification accuracy and execution time. As a result, the objective of our project is to compare and contrast the overall performance of various machine learning algorithms in order to lessen the exorbitant cost of liver disease prediction
{"title":"Analysis Machine Learning Methods For Forecasting Liver Disease","authors":"","doi":"10.46632//daai/3/1/18","DOIUrl":"https://doi.org/10.46632//daai/3/1/18","url":null,"abstract":"The majority of people worldwide are affected by chronic liver disease, which is the leading cause of death worldwide. It is now extremely challenging for researchers in the healthcare industry to predict diseases from the extensive databases. We use classification algorithms from machine learning to resolve this issue. Predicting liver disease is the primary objective of this project. We have implemented five machine learning algorithms: Naive Bayes, SVM, K-Nearest Neighbor, Logistic Regression, and Random Forest. The comparison of these classifier algorithms is entirely based on performance, classification accuracy and execution time. As a result, the objective of our project is to compare and contrast the overall performance of various machine learning algorithms in order to lessen the exorbitant cost of liver disease prediction","PeriodicalId":226827,"journal":{"name":"Data Analytics and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123240842","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}
This paper is about a overview of AI in medical field, dealing with recent and future applications that are related to AI. The aim is to develop knowledge and information about AI among the primary care physicians in the health care. Firstly, I've described about what is Artificial Intelligence then, who’s the father of it, what are the types of AI that is used in the medical field, features of AI, approaches and its needs. This paper is also about how AI is used in the health care, diagnosis, creation of new drug and delivery of drug, AI in COVID-19 pandemic, how it is used to analyze CT scans, x-rays, MRIs and about how Machine Learning is used in the health care and also how google is dealing with the future problem using Machine learning.
{"title":"Artificial Intelligence on Medical Fields","authors":"Keerthi Rani s","doi":"10.46632/daai/3/2/21","DOIUrl":"https://doi.org/10.46632/daai/3/2/21","url":null,"abstract":"This paper is about a overview of AI in medical field, dealing with recent and future applications that are related to AI. The aim is to develop knowledge and information about AI among the primary care physicians in the health care. Firstly, I've described about what is Artificial Intelligence then, who’s the father of it, what are the types of AI that is used in the medical field, features of AI, approaches and its needs. This paper is also about how AI is used in the health care, diagnosis, creation of new drug and delivery of drug, AI in COVID-19 pandemic, how it is used to analyze CT scans, x-rays, MRIs and about how Machine Learning is used in the health care and also how google is dealing with the future problem using Machine learning.","PeriodicalId":226827,"journal":{"name":"Data Analytics and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120969078","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}
Farming is a most important resource of income for Indians and which has significant impact in the Indian economy. Yield and quality delivery is extremely important for Crop development should higher. As a result, suitable environment and also moisture in the crop beds can play a significant role in crop manufacture. The majority of irrigation is done using traditional methods of the stream flows from one ending to the other. Variable humidity levels in the field may result from such a supply. A planned irrigation structure can help to improve the management of the water system This research presents a terrain-specific programmed water system that reduces manual labor while optimizing water usage and enhancing crop productivity. The Arduino kit is used to create the setup, along with a moisture sensor and a Wi-Fi section. Our preliminary configuration is linked to a “cloud framework” and data is collected. Cloud services then analyze the data and make appropriate recommendations.
{"title":"Internet of Things (IOT) Based on towards Motorized Interactive Irrigation System","authors":"","doi":"10.46632/daai/3/2/36","DOIUrl":"https://doi.org/10.46632/daai/3/2/36","url":null,"abstract":"Farming is a most important resource of income for Indians and which has significant impact in the Indian economy. Yield and quality delivery is extremely important for Crop development should higher. As a result, suitable environment and also moisture in the crop beds can play a significant role in crop manufacture. The majority of irrigation is done using traditional methods of the stream flows from one ending to the other. Variable humidity levels in the field may result from such a supply. A planned irrigation structure can help to improve the management of the water system This research presents a terrain-specific programmed water system that reduces manual labor while optimizing water usage and enhancing crop productivity. The Arduino kit is used to create the setup, along with a moisture sensor and a Wi-Fi section. Our preliminary configuration is linked to a “cloud framework” and data is collected. Cloud services then analyze the data and make appropriate recommendations.","PeriodicalId":226827,"journal":{"name":"Data Analytics and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125970730","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}
R. Pachpute, Abhishek Jadhav, Aditya Shinde, Kaustubh Naik, S. Gawai
Medicines at Your Fingertips is a website aimed to provide all the information about any medicine, it’s side effects and all the other health related information. All the data of the medicines is stored in our database and it is fetched during the execution of user’s request. We have created the frontend using HTML, CSS, JS, JQUERY, JQUERY UI and Bootstrap. The Backend is built using Django Framework of Python. Our website contains 6 main components which are: Chatbot: An intelligent chatbot which will give any information that the user has asked. The AI chatbot is created using the Pytorch library of python. It will also help the user to navigate from the whole website. Drugs a to Z, a drug dictionary to give all the information about the desired medicine. There are 2 ways to search the medicine. Pill Finder: To search the medicine alphabetically or using the search bar Phonetic Search: To search the medicine using voice command. Drugs By Condition: It contains the information about all types of health conditions, their causes and treatment along with some medicines which are used to treat them. Side Effects: It also has alphabetically sorted medicines which gives the information about the side effects of the particular medicine along with a search bar. First Aid: This part consists of 3 components which gives the information about first aid treatments and My Med List to set reminders for the doses of your medicine.
{"title":"A Survey on “Medicines at Your Fingertips”","authors":"R. Pachpute, Abhishek Jadhav, Aditya Shinde, Kaustubh Naik, S. Gawai","doi":"10.46632/daai/3/2/26","DOIUrl":"https://doi.org/10.46632/daai/3/2/26","url":null,"abstract":"Medicines at Your Fingertips is a website aimed to provide all the information about any medicine, it’s side effects and all the other health related information. All the data of the medicines is stored in our database and it is fetched during the execution of user’s request. We have created the frontend using HTML, CSS, JS, JQUERY, JQUERY UI and Bootstrap. The Backend is built using Django Framework of Python. Our website contains 6 main components which are: Chatbot: An intelligent chatbot which will give any information that the user has asked. The AI chatbot is created using the Pytorch library of python. It will also help the user to navigate from the whole website. Drugs a to Z, a drug dictionary to give all the information about the desired medicine. There are 2 ways to search the medicine. Pill Finder: To search the medicine alphabetically or using the search bar Phonetic Search: To search the medicine using voice command. Drugs By Condition: It contains the information about all types of health conditions, their causes and treatment along with some medicines which are used to treat them. Side Effects: It also has alphabetically sorted medicines which gives the information about the side effects of the particular medicine along with a search bar. First Aid: This part consists of 3 components which gives the information about first aid treatments and My Med List to set reminders for the doses of your medicine.","PeriodicalId":226827,"journal":{"name":"Data Analytics and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115989311","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}
In the past three decades,WirelessSensorNetwork(WSN)has become aleading area of research. Now days, WSN plays vital role for all kind of humanoid applications like research, automation, robotic industries etc.Most of the researches are contributing through their research works for WSN to simply the infrastructure, data communication, Security and shortest path signal communication. Eventually we come across the WSN issues and stated that Load Balancing, Security and shortest distance for transmission. Artificial Intelligence is the powerful technology for many kinds of applications. In this paper, we used Stochastic Gradient Decent Search (SGD) algorithm for optimizing the WSN signals.
{"title":"A Novel Framework for Prevention of Black hole in Wireless Sensor Networks using Deep Belief Network","authors":"","doi":"10.46632/daai/3/2/35","DOIUrl":"https://doi.org/10.46632/daai/3/2/35","url":null,"abstract":"In the past three decades,WirelessSensorNetwork(WSN)has become aleading area of research. Now days, WSN plays vital role for all kind of humanoid applications like research, automation, robotic industries etc.Most of the researches are contributing through their research works for WSN to simply the infrastructure, data communication, Security and shortest path signal communication. Eventually we come across the WSN issues and stated that Load Balancing, Security and shortest distance for transmission. Artificial Intelligence is the powerful technology for many kinds of applications. In this paper, we used Stochastic Gradient Decent Search (SGD) algorithm for optimizing the WSN signals.","PeriodicalId":226827,"journal":{"name":"Data Analytics and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114452859","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}
Chronic Kidney Disease (CKD) involves a slow loss of kidney function. Kidneys remove wastes and fluids from your blood, which are later eliminated in urine, covering over a period of months to years, signs and symptoms of kidney disease are usually indistinct, and are a serious disease. It is enlightened in six stages congenial to the severity level. It is categorized into various stages based on the Glomerular Filtration Rate (GFR), which in turn utilizes several attributes, like age, sex, race and Serum Creatinine. Among multiple available models for estimating GFR value, Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI), which is a linear model, has been found to be quite efficient because it allows detecting all CKD stages. Random Forest had 99.24% truthfulness. The model's best result is created by considering the 10 most reelected features. When compared to previous studies, our results are amid the good for assessment metrics and the ranking accuracy.
{"title":"Predicting Chronic Kidney Disease using RF Algorithm for Big Data","authors":"J. K","doi":"10.46632/daai/3/2/28","DOIUrl":"https://doi.org/10.46632/daai/3/2/28","url":null,"abstract":"Chronic Kidney Disease (CKD) involves a slow loss of kidney function. Kidneys remove wastes and fluids from your blood, which are later eliminated in urine, covering over a period of months to years, signs and symptoms of kidney disease are usually indistinct, and are a serious disease. It is enlightened in six stages congenial to the severity level. It is categorized into various stages based on the Glomerular Filtration Rate (GFR), which in turn utilizes several attributes, like age, sex, race and Serum Creatinine. Among multiple available models for estimating GFR value, Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI), which is a linear model, has been found to be quite efficient because it allows detecting all CKD stages. Random Forest had 99.24% truthfulness. The model's best result is created by considering the 10 most reelected features. When compared to previous studies, our results are amid the good for assessment metrics and the ranking accuracy.","PeriodicalId":226827,"journal":{"name":"Data Analytics and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126474585","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}
S. Josephine Reenamary, Rev. Sr. ArockiaValan Rani
One of the human body's most important organs is the heart. It helps the body's blood to circulate and become cleaner. The global leading cause of death is heart attack. Chest discomfort, a faster heartbeat, and breathing problems were a few indications. The accuracy of this data was regularly checked. This publication presented a broad summary of heart attacks and current treatments. Additionally, a quick overview of the important machine learning methods for heart attack prediction that are available in the literature is provided. The machine learning techniques described include Decision Tree, Logistic Regression, SVM, Naive Bayes, Random Forest, KNN, and XG Boost Classifier. The algorithms are contrasted based on the braced of characteristics.
{"title":"Heart Disease Detection -A Machine Learning Approach","authors":"S. Josephine Reenamary, Rev. Sr. ArockiaValan Rani","doi":"10.46632/daai/3/2/12","DOIUrl":"https://doi.org/10.46632/daai/3/2/12","url":null,"abstract":"One of the human body's most important organs is the heart. It helps the body's blood to circulate and become cleaner. The global leading cause of death is heart attack. Chest discomfort, a faster heartbeat, and breathing problems were a few indications. The accuracy of this data was regularly checked. This publication presented a broad summary of heart attacks and current treatments. Additionally, a quick overview of the important machine learning methods for heart attack prediction that are available in the literature is provided. The machine learning techniques described include Decision Tree, Logistic Regression, SVM, Naive Bayes, Random Forest, KNN, and XG Boost Classifier. The algorithms are contrasted based on the braced of characteristics.","PeriodicalId":226827,"journal":{"name":"Data Analytics and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122114222","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}
In today's world, wireless communication systems are extremely important for applications related to entertainment, business, commerce, health and safety. These systems continue to advance from generation to generation and at this time, fifth generation (5G) wireless networks are being deployed globally the globe. Beyond 5G wireless systems, which will represent the sixth generation (6G) of the evolution, are already being discussed in academia and industry. The application of artificial intelligence (AI) and machine learning (ML) to such wireless networks will be one of the primary and essential elements of 6G systems. According to our present understanding of wireless technologies up to 5G, every component and building block of a wireless system, such as the physical, network, and application layers, will involve one or more of them.
{"title":"Machine Learning Techniques for 5g And Beyond","authors":"T. Angalaeswari, M. Logeswari","doi":"10.46632/daai/3/2/11","DOIUrl":"https://doi.org/10.46632/daai/3/2/11","url":null,"abstract":"In today's world, wireless communication systems are extremely important for applications related to entertainment, business, commerce, health and safety. These systems continue to advance from generation to generation and at this time, fifth generation (5G) wireless networks are being deployed globally the globe. Beyond 5G wireless systems, which will represent the sixth generation (6G) of the evolution, are already being discussed in academia and industry. The application of artificial intelligence (AI) and machine learning (ML) to such wireless networks will be one of the primary and essential elements of 6G systems. According to our present understanding of wireless technologies up to 5G, every component and building block of a wireless system, such as the physical, network, and application layers, will involve one or more of them.","PeriodicalId":226827,"journal":{"name":"Data Analytics and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128222871","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}
The humankind has faced severe effects on the virus. By analyzing the similarities in the characteristics and inheritable patterns, the analyst can get a better understand about the virus and this may helps to determine the cure or the drug. With an overall viral relationship with the accuracy rate of 96, the trial results are more encouraging.
{"title":"Streambit Dashboard For DNA Analysis","authors":"","doi":"10.46632/daai/3/1/3","DOIUrl":"https://doi.org/10.46632/daai/3/1/3","url":null,"abstract":"The humankind has faced severe effects on the virus. By analyzing the similarities in the characteristics and inheritable patterns, the analyst can get a better understand about the virus and this may helps to determine the cure or the drug. With an overall viral relationship with the accuracy rate of 96, the trial results are more encouraging.","PeriodicalId":226827,"journal":{"name":"Data Analytics and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126734448","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：