MAchine Learning Algorithms

The demand for credit is increasing constantly. Banks are looking for various methods of credit evaluation that provide the most accurate results in a shorter period in order to minimize their rising risks. This study focuses on various methods that enable the banks to increase their asset quality without market loss regarding the credit allocation process. These methods enable the automatic evaluation of loan applications in line with the sector practices, and enable determination of credit policies/strategies based on actual needs. Within the scope of this study, the relationship between the predetermined attributes and the credit limit outputs are analyzed by using a sample data set of consumer loans. Random forest (RF), sequential minimal optimization (SMO), PART, decision table (DT), J48, multilayer perceptron (MP), JRip, naïve Bayes (NB), one rule (OneR) and zero rule (ZeroR) algorithms were used in this process. As a result of this analysis, SMO, PART and random forest algorithms are the top three approaches for determining customer credit limits.

Today various real world applications network such as VANETs, MANETs, and WSNs supply a wide range of applications from security to infotainment. As the communication and network field has linked faraway corners of the world using advanced network technology, intruders, hackers or attackers have also raised attacks on networking infrastructure as in enough. Administrators can need to prevent such hacks by using advanced intrusion detection tools. The use of resources, loss of packs, and justice are common performance measures for network based applications, which are usually required by network safety applications to reduce transmission time and to achieve high reliability. The performance of the overall unit is decided by two logics, one is security and another one is privacy. To maintain such data secured architectures, Intrusion Detection System (IDS) is the most commonly used mechanism to detect the attacks on cloud. Hence, more number of researchers focused on this topic. To overcome the limitations of earlier IDS approaches and an elaborate comparison of machine learning based IDS in terms of factors efficiency, execution/detection time, flexibility and true/false rate.

Memorial days of disasters represent an opportunity to evaluate the progress of recovery. This article uses sentiment analysis (SA) to assess post-disaster recovery on the 10th anniversary of L’Aquila’s earthquake using Twitter data. We have analyzed 4349 tweets from 4 to 10 April 2019 with the hashtag: #L’Aquila that we have obtained from a third-party vendor. The polarity is first defined using a supervised classification based on experts’ rules on post-disaster reconstruction and Grammarly tones. Then, this polarity is compared with the outcome of an unsupervised classification based on the pre-trained SA machine learning algorithm developed by MonkeyLearn. We have found a significant negative assessment of the post-disaster recovery process in L’Aquila. About 33.1% of the tweets had a negative polarity, followed by 29.3% tweets with a neutral polarity, 28.7% with positive polarity, and 8.9% unrelated to the anniversary. Further analysis of the tweets confirms that after 10 years...

Falling is a commonly occurring mishap with elderly people, which may cause serious injuries. Thus, rapid fall detection is very important in order to mitigate the severe effects of fall among the elderly people. Many fall monitoring systems based on the accelerometer have been proposed for the fall detection. However, many of them mistakenly identify the daily life activities as fall or fall as daily life activity. To this aim, an efficient machine learning-based fall detection algorithm has been proposed in this paper. The proposed algorithm detects fall with efficient sensitivity, specificity, and accuracy as compared to the state-of-the-art techniques. A publicly available dataset with a very simple and computationally efficient set of features is used to accurately detect the fall incident. The proposed algorithm reports and accuracy of 99.98% with the Support Vector Machine(SVM) classifier.

People of different races are characterized by the
language they speak. They can identify voices of
someone’s race just by listening and talking through
conversation. This paper presents an efficient
comparison of machine learning algorithm based on
Filipino-Vietnamese speeches for tone classification
using feature parameter. The system was trained
using audio recorded speeches samples. Datasets
were taken from multiple sessions involving 10
respondents; 5 (five) of which are Filipinos and 5
(five) Vietnamese. The respondents were asked to
read the paragraphs and record their voices while
reading the data. The empirical test shows that
during the pre-processing of data records,
Vietnamese have longer range of duration as
compared to Filipinos because of their manners in
reading and intensity on accent-bearing syllables. In
constructing the speech recognition model, four
classification algorithms were used, namely: KNN
(K-Nearest Neighbour), Naïve-Bayes, SMO
(Support Vector Machine) and MLP (Multilayer
Perceptron). The evaluation of the training set in
terms of accuracy, correctly classified instances and
incorrectly classified instances are evaluated by the
performance of the developed system. As the data
established, the results show that SMO and MLP
performed better for all the given datasets, with
accuracy rates ranging from 99.2694% for MLP and
98.7179% for SMO. However, KNN algorithm
turned out to have the lowest rate of 96.3882%.

Movies play an important role in our everyday life. Film industry involves huge investment in terms of money, time,
efforts and people. The number of movies produced in the world is growing at an exponential rate and success rate of movie is of
utmost importance since billions of dollars are invested in the making of each of these movies. In such a scenario, prior
knowledge about the success or failure of a particular movie and what factor affect the movie success will benefit the production
houses since these predictions will give them a fair idea of how to go about with the advertising and campaigning, which itself is
an expensive affair altogether. So, the prediction of the success of a movie is very essential to the film industry. In this proposed
system, we give our detailed analysis of the Internet Movie Database (IMDb) and predict the IMDb score. This database contains
categorical and numerical information such as IMDb score, director, gross, budget and so on and so forth. This system proposes
a way to predict how successful a movie will be prior to its arrival at the box office instead of listening to critics and others on
whether a movie will be successful or not. The proposed system provides a quite efficient approach to predict IMDb score on
IMDb Movie Dataset. We will try to unveil the important factors influencing the score of IMDb Movie Data. In the exploratory
analysis we found that number of voted users, number of critics for reviews, number of Facebook likes, duration of the movie
and gross collection of movie affect the IMDb score strongly. Drama and Biopic movies are best in genres.

Stock worth measures have reliably pulled in the thought of various agents and authorities. Standard speculation holds that stock trades are sporadic in nature, and it is senseless to endeavour to envision them. Since various components are incorporated, anticipating the stock worth itself is a troublesome issue. Until further notice, the market continues like a popularity based machine, yet as time goes on, it acts like a measuring machine, so it can foresee exhibit designs over a progressively broadened time period. The utilization of machine learning advancement and various counts in stock worth examination and figure is a promising field. In this paper, we first rapidly plot the logical classification of the stock markets and taxonomy of stock market prediction methods. By then, we will focus on some investigation achieves stock assessment and deciding. We look at the particular, technical, fundamental, and long short term memory network strategies used in stock examination. Finally, we present a couple of challenges and investigation opportunities in this field.

Movies play an important role in our everyday life. Film industry involves huge investment in terms of money, time, efforts and people. The number of movies produced in the world is growing at an exponential rate and success rate of movie is of utmost importance since billions of dollars are invested in the making of each of these movies. In such a scenario, prior knowledge about the success or failure of a particular movie and what factor affect the movie success will benefit the production houses since these predictions will give them a fair idea of how to go about with the advertising and campaigning, which itself is an expensive affair altogether. So, the prediction of the success of a movie is very essential to the film industry. In this proposed system, we give our detailed analysis of the Internet Movie Database (IMDb) and predict the IMDb score. This database contains categorical and numerical information such as IMDb score, director, gross, budget and so on and so forth. Th...

This work is based on the dataset acquired from a direct marketing campaign conducted by a Portuguese banking institution through phone calls where each customers was often contacted through more than one means of communication. The effect of marketing campaigns on the general public has declined due to economic pressures, competition, and the increase of marketing campaigns over time. The goal of this paper is to augment the campaign by using Business Intelligence and Data Mining techniques to find a model that would increase the campaigns efficacy by selecting a more explicit set of potential customers who would most likely subscribe to the banks term deposit contract, based on the data set.

Machine Learning and Applications: An International Journal (MLAIJ) is a quarterly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the machine learning. The journal is devoted to the publication of high quality papers on theoretical and practical aspects of machine learning and applications.The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on machine learning advancements, and establishing new collaborations in these areas. Original research papers, state-of-the-art reviews are invited for publication in all areas of machine learning.
Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of machine learning.

Automatic Student performance prediction is a crucial job due to the large volume of data in educational databases. This job is being addressed by educational data mining (EDM). EDM develop methods for discovering data that is derived from educational environment. These methods are used for understanding student and their learning environment. The educational institutions are often curious that how many students will be pass/fail for necessary arrangements. In previous studies, it has been observed that many researchers have intension on the selection of appropriate algorithm for just classification and ignores the solutions of the problems which comes during data mining phases such as data high dimensionality, class imbalance and classification error etc. Such types of problems reduced the accuracy of the model. Several well-known classification algorithms are applied in this domain existing models a student performance prediction model based on supervised learning decision tree classifier. In addition, an ensemble method is applied to improve the performance of the classifier. Ensemble methods approach is designed to solve classification, prediction problem. we propose a method for predicting final grades of students by a Recurrent Neural Network (RNN) from the log data stored in the educational systems. We applied this method to the log data from 108 students and examined the accuracy of prediction. From the experimental results, comparing with multiple regression analysis, it is confirmed that an RNN is effective to early prediction of final and suitable job for the student based on their academic performance and knowledge on skill set.

Sales forecasting became crucial for industries in past decades with rapid globalization, widespread adoption of information technology towards e-business, understanding market fluctuations, meeting business plans, and avoiding loss of sales. This research precisely predicts the automotive industry sales using a bag of multiple machine learning and time series algorithms coupled with historical sales and auxiliary features. Three-year historical sales data (from 2017 till 2020) were used for the model building or training, and one-year (2020-2021) predictions were computed for 900 unique SKU's (stock-keeping units). In the present study, the SKU is a combination of sales office, core business field, and material customer group. Various data cleaning and exploratory data analysis algorithms were implemented over raw datasets before use for modeling. Mean absolute percentage error (mape) were estimated for individual predictions from time series and machine learning models. The best model was selected for unique SKU's as per the most negligible mape value.

Agriculture is the basic source of food supply in all the countries of the world—whether underdeveloped, developing or
developed. Not only providing food, this sector has contributions to almost every other sector of a country. According to the
Bureau of Statistics (BS), 2017, about 17 % of the country’s Gross Domestic Product (GDP) is a contribution of the agricultural
sector, and it employs more than 45% of the total labour force. In the decreasing crop production and shortage of food across
the world, one of the crucial criteria of agriculture now-a-days is selecting the right crop for the right piece of land at the right
time. Hence, in our research we have proposed a method which would help suggest the most suitable crops for a specific land
based on the analysis of the data of previous soil series classification using machine learning. In our work, we have implemented
Bagging Classifier, Support Vector Machine, and k-Nearest Neighbour for soil classification and crop recommendation. We
have trained these algorithms with the training data and later these were tested with test dataset. Then compared the
performances of all the tested methods to arrive at the best outcome

Mash Industries ltd is a Kenyan manufacturing company specializing in the production of hygiene products. The company was established in 1962 in Nairobi, Kenya the company is committed to developing new products for the east African market, as well as innovating products to satisfy the ever changing needs & preferences of the fast growing middle-class consumers. The company also manufactures branded products such as soft tissues and sanitary towels for various other supermarket chains, that sell them as own brands. Mash Industries ltd has branches across east Africa, including in Uganda, Tanzania, and Rwanda. They have a catalogue of over 10 brand products. Mash Industries ltd is involved in wholesale of its products through its warehouse outlets, as well as retail sales by way of its chain of five retail shops across the city of Nairobi.

Arab users give their comments and opinions daily, and it increases dramatically with the online reviews of products or services from companies, reviews in Arabic, and its dialects. This text describes the user's condition or needs for satisfaction or dissatisfaction, and this evaluation is either negative or positive criticism.  Based on the need to work on the Arabic text sentiment analysis problem, the case of the Jordanian dialect, using machine learning in the process of data classification. The main aim of this research work is to classify text into two classes consisting of several phases. The first phase has tools used in natural language processing, stemming, stop word removal, and tokenization to filter the text. The second phase modified the Artificial Bee Colony (ABC) Algorithm, with Upper Confidence Bound (UCB) Algorithm, to promote the exploitation ability. The model proposed in this study to an enhanced ABC algorithm with the Upper Confidence Bound (UCB) to get the minimum number of the optimal feature, the features affecting the classification, then passing this number to the Wrapper method, Forward Feature selection strategy, then using four classifiers of machine learning algorithms (K-Nearest Neighbors (KNN), Support vector machines (SVM), Naïve-Bayes (NB), and Polynomial Neural Networks (PNN). This proposed model applied to the Jordanian dialect database, which contains comments from Jordanian telecom company's customers. The proposed model is applied to the database of the Algerian dialect, which contains long Arabic texts, to see the efficiency of the proposed model for the classification of short and long texts. Four performance evaluation criteria were used: precision, recall, f1-score, and accuracy. To be a future step to build on or use for the classification of Arabic dialects, the experimental results show that the proposed model gives height accuracy up to 99% by applying to the Jordanian dialect, and an increase of results 2% better than the model applied to the Algerian dialect.

Keywords: Natural Language Processing. Text classification. sentiment analysis. Feature selection. inspired algorithms. ABC. UBC. KNN. SVM. PNN. Naïve Bayes

The history of Artificial Intelligence and Machine Learning dates back to 1950’s. In recent years, there has been an increase in popularity for applications that implement AI and ML technology. As with traditional
development, software testing is a critical component of an efficient AI/ML application. However, the approach to development methodology used in AI/ML varies significantly from traditional development.
Owing to these variations, numerous software testing challenges occur. This paper aims to recognize and to explain some of the biggest challenges that software testers face in dealing with AI/ML applications. For future research, this study has key implications. Each of the challenges outlined in this paper is ideal for further investigation and has great potential to shed light on the way to more productive software testing strategies and methodologies that can be applied to AI/ML applications.

International Conference on Machine Learning, NLP and Data Mining (MLDA 2022)
will provide an excellent international forum for sharing knowledge and results in theory,
methodology and applications of Machine Learning, Natural Language Computing and Data
Mining. Authors are solicited to contribute to the conference by submitting articles that
illustrate research results, projects, surveying works and industrial experiences that describe
significant advances in the following areas, but are not limited to these topics only.

4
th International Conference on Machine Learning & Applications (CMLA 2022) will
provide an excellent international forum for sharing knowledge and results in theory,
methodology and applications of on Machine Learning & Applications. The aim of the
conference is to provide a platform to the researchers and practitioners from both academia as
well as industry to meet and share cutting-edge development in the field.
Authors are solicited to contribute to the conference by submitting articles that illustrate
research results, projects, surveying works and industrial experiences that describe significant
advances in the following areas, but are not limited to.

Machine Learning and Applications: An International Journal (MLAIJ) is a quarterly open access peer- reviewed journal that publishes articles which contribute new results in all areas of the machine learning. The journal is devoted to the publication of high quality papers on theoretical and practical aspects of machine learning and applications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on machine learning advancements, and establishing new collaborations in these areas. Original research papers, state-of-the-art reviews are invited for publication in all areas of machine learning.

The development of the Internet changed the way people eat and responding for food. Amazon is a biggest website where users can easily purchase all kind of food they need with only a mouse-click. Here our aim is to create a classifier that classifies the reviews in to either positive or negative based on the data given such as the time at which the review was written, rating given by the customer. We can analyse the using various models like Naive Bayes, KNN, Logistic Regression etc. Eventually we want to build an acceptable model which helps us better understand how customers rate and review the food they purchased. Here, we classified the reviews into 2 categories 1) Positive: which indicates the customer is satisfied with the product 2) Negative: which indicates the customer is not satisfied with the product The main propaganda of this project is, it can be used in e-commerce where the review plays a vital role based on which the interest of the new customers depends. The aim of this project is to classify the reviews to predict the customer satisfaction in the project; we are going to use various Machine Learning Algorithms to classify the review and compare their performance and finally declare the final model.

Scope & Topics 4 th International Conference on Machine Learning & Applications (CMLA 2022) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications. The aim of the conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field. Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to.

4
th International Conference on Machine Learning & Applications (CMLA 2022) will
provide an excellent international forum for sharing knowledge and results in theory,
methodology and applications of on Machine Learning & Applications. The aim of the
conference is to provide a platform to the researchers and practitioners from both academia as
well as industry to meet and share cutting-edge development in the field.

Intrusion Detection System (IDS) has been an effective way to achieve higher security in detecting malicious activities for the past couple of years. Anomaly detection is an intrusion detection system. Current anomaly detection is often associated with high false alarm rates and only moderate accuracy and detection rates because it's unable to detect all types of attacks correctly. An experiment is carried out to evaluate the performance of the different machine learning algorithms using KDD-99 Cup and NSL-KDD datasets. Results show which approach has performed better in term of accuracy, detection rate with reasonable false alarm rate.

Air passengers (the buyers) are often looking for the best time period to purchase airfares to get as much saving as possible while airlines (the sellers) always try to maximize their revenues by revising different prices for the same service. The airline implements dynamic pricing for the flight ticket. Flight ticket prices change during the morning and evening time of the day. Also, it changes with the holidays or festival season. There are several different factors on which the price of the flight ticket depends. The price of an airline ticket is affected by a number of factors, such as flight distance, purchasing time, fuel price, etc. The sellers have all the necessary information (for example historical sale, market demand, customer profile, and behaviour) to make the decision whether to increase or decrease airfares at different times prior to the departure dates. On the other hand, the buyers are only able to access limited information only which is not enough to predict the airfare prices. Considering the features such as departure time, the number of days left for departure and time of the day it will give the best time to buy the ticket. Features are extracted from the collected data to apply Machine Learning (ML) models. Then using this information, we are intended to build a system that can help buyers whether to buy a ticket or not.

The increase in the deployment of IoT networks has improved productivity of humans and organisations. However, IoT networks are increasingly becoming platforms for launching DDoS attacks due to inherent weaker security and resource-constrained nature of IoT devices. This paper focusses on detecting DDoS attack in IoT networks by classifying incoming network packets on the transport layer as either "Suspicious" or "Benign" using unsupervised machine learning algorithms. In this work, two deep learning algorithms and two clustering algorithms were independently trained for mitigating DDoS attacks. We lay emphasis on exploitation based DDOS attacks which include TCP SYN-Flood attacks and UDP-Lag attacks. We use Mirai, BASHLITE and CICDDoS2019 dataset in training the algorithms during the experimentation phase. The accuracy score and normalized-mutual-information score are used to quantify the classification performance of the four algorithms. Our results show that the autoencoder performed overall best with the highest accuracy across all the datasets.

The process of examining, filtering, and presenting data to obtain valuable information and make decisions is known as information analysis. Food resources are in high demand in countries like India, where they serve the population and help to secure the nation's security. Crop production is largely influenced by weather variations, soil quality, water availability, and fertilizer application, among other factors. The various types of soil play a significant effect in agricultural production. Recommending fertilizers to agriculturists may assist them in making better crop selection and maintenance decisions. Crop yield prediction can be done using a variety of studies using information and communication technology (ICT). Different sorts of mining techniques for data analysis and data acquisition can be widely used for a variety of purposes. Smart agriculture is a method of transmitting data from average farmers to skilled farmers.

The driver’s cognitive and physiological states affect his/her ability to control the vehicle. Thus, these driver states are essential to the safety of automobiles. The design of advanced driver assistance systems (ADAS) or autonomous vehicles will depend on their ability to interact effectively with the driver. A deeper understanding of the driver state is, therefore, paramount. Electroencephalography (EEG) is proven to be one of the most effective methods for driver state monitoring and human error detection. This paper discusses EEG-based driver state detection systems and their corresponding analysis algorithms over the last three decades. First, the commonly used EEG system setup for driver state studies is introduced. Then, the EEG signal preprocessing, feature extraction, and classification algorithms for driver state detection are reviewed. Finally, EEG-based driver state monitoring research is reviewed in-depth, and its future development is discussed. It is concluded that the current EEG-based driver state monitoring algorithms are promising for safety applications. However, many improvements are still required in EEG artifact reduction, real-time processing, and between-subject classification accuracy.

With the advent of the Internet and social media, while hundreds of people have benefitted from the vast sources of information available, there has been an enormous increase in the rise of cyber-crimes, particularly targeted towards women. According to a 2019 report in the [4] Economics Times, India has witnessed a 457% rise in cybercrime in the five year span between 2011 and 2016. Most speculate that this is due to impact of social media such as Facebook, Instagram and Twitter on our daily lives. While these definitely help in creating a sound social network, creation of user accounts in these sites usually needs just an email-id. A real life person can create multiple fake IDs and hence impostors can easily be made. Unlike the real world scenario where multiple rules and regulations are imposed to identify oneself in a unique manner (for example while issuing one’s passport or driver’s license), in the virtual world of social media, admission does not require any such checks. In this paper, we study the different accounts of Instagram, in particular and try to assess an account as fake or real using Machine Learning techniques namely Logistic Regression and Random Forest Algorithm

8th International Conference on Artificial Intelligence and Soft Computing (AISO 2022) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence, Soft Computing. The aim of the conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.

Falling is a commonly occurring mishap with elderly people, which may cause serious injuries. Thus, rapid fall detection is very important in order to mitigate the severe effects of fall among the elderly people. Many fall monitoring systems based on the accelerometer have been proposed for the fall detection. However, many of them mistakenly identify the daily life activities as fall or fall as daily life activity. To this aim, an efficient machine learning-based fall detection algorithm has been proposed in this paper. The proposed algorithm detects fall with efficient sensitivity, specificity, and accuracy as compared to the state-of-the-art techniques. A publicly available dataset with a very simple and computationally efficient set of features is used to accurately detect the fall incident. The proposed algorithm reports and accuracy of 99.98% with the Support Vector Machine(SVM) classifier.

2nd International Conference on AI, Machine Learning and Applications (AIMLA 2022) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence and Machine Learning. The Conference looks for significant contributions to all major fields of the Artificial Intelligence, Machine Learning in theoretical and practical aspects. The aim of the Conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.

The construction of the school campus network has provided an advanced comprehensive and informative atmosphere for teaching, research project, and management of institutions. Within the method of conversion and intelligentization, the data produced by all types of application systems in schools and colleges are growing, and the large data environment of campus has been formed. Big data in colleges encompass abundant information, so, using new data storage and analysis tools to store and analyze these vast amounts of data and to get useful information from them. In this paper, a deep learning analysis algorithm based on Map Reduce is compared to deal with college data using Federal College of Education (Technical) Asaba. Using Map Reduce parallel computing framework to realize campus data computing, the study tends to look at the analysis and application systems of college big data in different themes and levels and to dig out valuable information hidden behind college data, also to find out the best deep learning algorithm for analyzing educational big data in terms of accuracy, classification error, minimum support threshold, minimum confidence threshold, and runtime. The experimental results show that certain deep learning algorithms are more effective than the others in educational data mining based on Map Reduce. It provides new research concepts for large data mining in colleges and offers a technical reference for the development of a good and smart educational system

Wellbeing has prime significance in our everyday life. Sound wellbeing is important to accomplish the day by day work appropriately. With huge amounts of new human services innovation new businesses, IoT is quickly altering the social insurance diligence. IoT Based Patient Monitoring System utilizing ESP8266 and Arduino. Wellbeing checking is the serious issue in this day and age. Heartbeat rate and internal heat level readings are recorded over ThingSpeak and Google sheets with the goal that quiet wellbeing can be checked from anyplace on the planet over web. we have structured the IoT Based Patient Health Monitoring System utilizing gadgets. This IoT gadget could peruse the beat rate and measure the encompassing temperature. It constantly screens the beat rate and encompassing temperature and updates them to an IoT stage. We are observing different parameters of the patient utilizing web of things. In the patient checking framework dependent on Internet of things venture, the ongoing parameters of patient's wellbeing are sent to cloud utilizing Internet availability. These parameters are sent to a remote Internet area so client can see these subtleties from anyplace on the planet. There is a significant distinction among customary and IOT based patient observing framework. In IOT based framework, subtleties of the patient wellbeing can be seen by numerous clients. The explanation for this is the information should be observed by visiting a site or URL. Though, in GSM based patient checking, the wellbeing parameters are sent utilizing GSM by means of SMS. Health is the most important part of any human’s life without health it is useless to any treasure of life. Most humans live a busy life in which going to a doctor for weekly or even monthly checkup is an impossible task. Without monitoring your health it is not possible to whether you are a healthy or sick person. This problem leads to the design of a product which monitors your health every day without going to a doctor. In this paper, a system is designed as a prototype for monitoring alerting based on the health of a person. This system is fully automated little or no human help is needed. Any doctor can monitor this person from anywhere through the internet.

Heart is one of the most important members of the body, and heart disease is the major cause of death in the world and Iran. This is why the early/on time diagnosis is one of the significant basics for preventing and reducing deaths of this disease. So far, many studies have been done on heart disease with the aim of prediction, diagnosis, and treatment. However, most of them have been mostly focused on the prediction of heart disease. The purpose of this study is to develop models for heart disease diagnosis using machine learning, neural network, and deep learning algorithms. The models have been developed using the Cleveland heart disease dataset from University of California Irvine (UCI) repository. After complete data processing, including outlier detection, normalization, discretization, feature selection and feature extraction, the dataset is transformed into two normalized data and discretized data, according to the nature of the algorithms. Moreover, in constructing models of machine learning and neural networks, two randomized searches with cross-validation and grid search with Talos scan approaches are used for model tuning. Among evaluated models, including decision tree algorithms, random forest, support vector machine (SVM) and XGBoost, the highest accuracy is 92.9% using SVM, and among neural network models, multilayer perceptron (MLP) has resulted in the highest accuracy of 94.6%.