scholarly journals Classification of forest fire areas using machine learning algorithm

2021 ◽  
Vol 3 (1) ◽  
pp. 008-015
Author(s):  
Saruni Dwiasnati ◽  
Yudo Devianto
Keyword(s):  
Machine Learning ◽  
Forest Fire ◽  
Forest Fires ◽  
Naive Bayes ◽  
Learning Algorithm ◽  
Fire Hazard ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Burned Area ◽  
K Nearest Neighbor

Forest fires that occur will cause various kinds of problems, both in terms of health, such as smoke that can interfere with the respiratory system, in terms of the economy such as the economic wheel cannot run as usual, in terms of the environment can damage the surrounding environment and the environment that is missed by smoke, and other disasters. Forest fires can also have an impact on the costs that will be incurred to resolve the problems that arise due to forest fires, so research is needed to find out and measure the area affected by forest fires that burned in the range of 1980 - 2019 using a dataset of approximately 10,000. The target in this research is to be able to generate the best percentage scenario and find out the model of using the algorithm used to explore the algorithm in the Machine Learning method for the model for estimating the area of forest fires, namely the Siak Kampar Peninsula in Riau Province. In this study, 7 parameters were used to create a forest and land fire hazard map, namely weather temperature, Burned Area Density, hotspot density, wind speed, land cover type, rainfall, and land use. The seven parameters will be searched for accuracy results using the Classification method with Machine Learning algorithms, including Naïve Bayes, SVM, and K-Nearest Neighbor (K-NN). In this study, comparisons were made to obtain the best algorithm for estimating forest fire areas. By generating each algorithm is 71.72% for the Naïve Bayes algorithm, 75.00% for the SVM algorithm, and 64.71% for the K-NN algorithm.

scholarly journals Predicting Student’s Performance Using Machine Learning Algorithm

2021 ◽  
pp. 53-58
Author(s):  
Sheela Rani P ◽  
Dhivya S ◽  
Dharshini Priya M ◽  
Dharmila Chowdary A
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Prediction Model ◽  
Naive Bayes ◽  
Learning Algorithm ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Approaches ◽  
K Nearest Neighbors

Machine learning is a new analysis discipline that uses knowledge to boost learning, optimizing the training method and developing the atmosphere within which learning happens. There square measure 2 sorts of machine learning approaches like supervised and unsupervised approach that square measure accustomed extract the knowledge that helps the decision-makers in future to require correct intervention. This paper introduces an issue that influences students' tutorial performance prediction model that uses a supervised variety of machine learning algorithms like support vector machine , KNN(k-nearest neighbors), Naïve Bayes and supplying regression and logistic regression. The results supported by various algorithms are compared and it is shown that the support vector machine and Naïve Bayes performs well by achieving improved accuracy as compared to other algorithms. The final prediction model during this paper may have fairly high prediction accuracy .The objective is not just to predict future performance of students but also provide the best technique for finding the most impactful features that influence student’s while studying.

scholarly journals Effectiveness of Classification Methods on the Diabetes System

2021 ◽  
pp. 33-43
Author(s):  
Ahmed T. Shawky ◽  
Ismail M. Hagag
Keyword(s):  
Machine Learning ◽  
Naive Bayes ◽  
Early Stage ◽  
Research Paper ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Machine Learning Classification ◽  
Bayes Algorithm

In today’s world using data mining and classification is considered to be one of the most important techniques, as today’s world is full of data that is generated by various sources. However, extracting useful knowledge out of this data is the real challenge, and this paper conquers this challenge by using machine learning algorithms to use data for classifiers to draw meaningful results. The aim of this research paper is to design a model to detect diabetes in patients with high accuracy. Therefore, this research paper using five different algorithms for different machine learning classification includes, Decision Tree, Support Vector Machine (SVM), Random Forest, Naive Bayes, and K- Nearest Neighbor (K-NN), the purpose of this approach is to predict diabetes at an early stage. Finally, we have compared the performance of these algorithms, concluding that K-NN algorithm is a better accuracy (81.16%), followed by the Naive Bayes algorithm (76.06%).

scholarly journals CLASSIFICATION OF HEAD AND NECK CANCER TYPES USING MACHINE LEARNING ALGORITHM

2020 ◽  
pp. 198-205
Author(s):  
Prof O. Olabode ◽  
Prof A. O. Adetunmbi ◽  
Folake Akinbohun ◽  
Dr Ambrose Akinbohun
Keyword(s):  
Machine Learning ◽  
Head And Neck Cancer ◽  
Head And Neck ◽  
Neck Cancer ◽  
Naive Bayes ◽  
Information Gain ◽  
Learning Algorithm ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Chi Square

The worldwide incidence of head and neck cancer exceeds half a million cases annually. The morbidity and mortality of head and neck cancers considering thyroid, nasopharyngeal, sinonasal and laryngeal were reported high. The degree of facial disfigurement is unrivalled. Information Gain and Chi Square, Decision and Naïve Bayes were deployed for the study. The dataset was divided into training and test data. The results showed that the performance of Naïve Bayes outperformed Decision Trees. With the application of machine learning algorithms, head and neck cancer can be classified. KEYWORDS: Head and Neck, thyroid, Chi Square, Information Gain

scholarly journals Comparative study on machine learning algorithms for early fire forest detection system using geodata.

2020 ◽  
Vol 10 (5) ◽  
pp. 5507
Author(s):  
Zouiten Mohammed ◽  
Chaaouan Hanae ◽  
Setti Larbi
Keyword(s):  
Machine Learning ◽  
Forest Fire ◽  
Forest Fires ◽  
Learning Algorithm ◽  
Detection System ◽  
Learning Algorithms ◽  
Spatial Prediction ◽  
Machine Learning Algorithms ◽  
Geographical Information ◽  
Deep Learning Algorithm

Forest fires have caused considerable losses to ecologies, societies and economies worldwide. To minimize these losses and reduce forest fires, modeling and predicting the occurrence of forest fires are meaningful because they can support forest fire prevention and management. In recent years, the convolutional neural network (CNN) has become an important state-of-the-art deep learning algorithm, and its implementation has enriched many fields. Therefore, a competitive spatial prediction model for automatic early detection of wild forest fire using machine learning algorithms can be proposed. This model can help researchers to predict forest fires and identify risk zonas. System using machine learning algorithm on geodata will be able to notify in real time the interested parts and authorities by providing alerts and presenting on maps based on geographical treatments for more efficacity and analyzing of the situation. This research extends the application of machine learning algorithms for early fire forest prediction to detection and representation in geographical information system (GIS) maps.

scholarly journals Enabling Accurate Indoor Localization Using a Machine Learning Algorithm

2020 ◽  
Vol 4 (1) ◽  
pp. 96
Author(s):  
Haidar Abdulrahman Abbas ◽  
Kayhan Zrar Ghafoor
Keyword(s):  
Machine Learning ◽  
Naive Bayes ◽  
Learning Algorithm ◽  
Excellent Result ◽  
Indoor Positioning ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Training Dataset ◽  
Support Vector ◽  
Indoor Environments

In this paper, fingerprint referencing methods based on wireless fidelity Wi-Fi received signal strength (RSS) have used for indoor positioning. More precisely, Naïve Bayes, decision tree (DT), and support vector machine (SVM) one-to-one multi-classes and error-correcting-output-codes classifier are to enable accurate indoor positioning. Then, normalization is used to reduce positioning error by reducing the fluctuation and diverse distribution of the RSS values. Different devices are used in this experiment; the training dataset is not included in the main dataset. Nonetheless, the learned model by the SVM algorithm cannot be affected by the elimination of train datasets of the test device. The efficiency of DT is lower than the other machine learning algorithms, because it performs by Boolean function, and it provides the low accuracy of prediction for dataset than the algorithms. Naïve Bayes technique based on Bayes Theorem is better than DT and close to SVM for positioning approves that 1–1.5 m positioning accuracy for indoor environments can be achieved by the proposed approach which is an excellent result than traditional protocol.

scholarly journals Classification Breast Cancer Revisited with Machine Learning

2020 ◽  
Vol 1 (1) ◽  
pp. 42-50
Author(s):  
Hanna Arini Parhusip ◽  
Bambang Susanto ◽  
Lilik Linawati ◽  
Suryasatriya Trihandaru ◽  
Yohanes Sardjono ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Random Forest ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Random Forest Algorithm ◽  
K Nearest Neighbor ◽  
Cancer Data

The article presents the study of several machine learning algorithms that are used to study breast cancer data with 33 features from 569 samples. The purpose of this research is to investigate the best algorithm for classification of breast cancer. The data may have different scales with different large range one to the other features and hence the data are transformed before the data are classified. The used classification methods in machine learning are logistic regression, k-nearest neighbor, Naive bayes classifier, support vector machine, decision tree and random forest algorithm. The original data and the transformed data are classified with size of data test is 0.3. The SVM and Naive Bayes algorithms have no improvement of accuracy with random forest gives the best accuracy among all. Therefore the size of data test is reduced to 0.25 leading to improve all algorithms in transformed data classifications. However, random forest algorithm still gives the best accuracy.

scholarly journals Prediction of Denial of Service Attack using Machine Learning Algorithms

2020 ◽  
Vol 9 (5) ◽  
pp. 1601-1606
Keyword(s):  
Machine Learning ◽  
Naive Bayes ◽  
Graphical Representation ◽  
Denial Of Service ◽  
Learning Algorithms ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
K Nearest Neighbor ◽  
Ddos Attack ◽  
Time Accuracy

DDoS attack is one of the significant security threats in today’s Internet world. The main intention of the network thread is to make the resource unavailable such as flooding attacks. Here, Machine learning algorithms have been used for detecting DDoS attacks. Generally, the success of any algorithm has depended on the selection of appropriate data sets and the identification of attack parameters. The KDD-CUP dataset has been taken for a detail investigation of the DDoS attack. The K-nearest neighbor, ID3, Naive Bayes and C4.5 algorithms are compared in a single platform concluding with the positives with Naive Bayes. The main objective of the paper is to compare and predict the error rate, computation time, Accuracy of the algorithms using the Tanagra tool. Finally, these correlative algorithms have been compared and verified through experimental verification and graphical representation.

Efficient detection of hacker community based on twitter data using complex networks and machine learning algorithm

2021 ◽  
pp. 1-17
Author(s):  
Ahmed Al-Tarawneh ◽  
Ja’afer Al-Saraireh
Keyword(s):  
Machine Learning ◽  
Complex Networks ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Efficient Detection ◽  
Suggested Keywords

Twitter is one of the most popular platforms used to share and post ideas. Hackers and anonymous attackers use these platforms maliciously, and their behavior can be used to predict the risk of future attacks, by gathering and classifying hackers’ tweets using machine-learning techniques. Previous approaches for detecting infected tweets are based on human efforts or text analysis, thus they are limited to capturing the hidden text between tweet lines. The main aim of this research paper is to enhance the efficiency of hacker detection for the Twitter platform using the complex networks technique with adapted machine learning algorithms. This work presents a methodology that collects a list of users with their followers who are sharing their posts that have similar interests from a hackers’ community on Twitter. The list is built based on a set of suggested keywords that are the commonly used terms by hackers in their tweets. After that, a complex network is generated for all users to find relations among them in terms of network centrality, closeness, and betweenness. After extracting these values, a dataset of the most influential users in the hacker community is assembled. Subsequently, tweets belonging to users in the extracted dataset are gathered and classified into positive and negative classes. The output of this process is utilized with a machine learning process by applying different algorithms. This research build and investigate an accurate dataset containing real users who belong to a hackers’ community. Correctly, classified instances were measured for accuracy using the average values of K-nearest neighbor, Naive Bayes, Random Tree, and the support vector machine techniques, demonstrating about 90% and 88% accuracy for cross-validation and percentage split respectively. Consequently, the proposed network cyber Twitter model is able to detect hackers, and determine if tweets pose a risk to future institutions and individuals to provide early warning of possible attacks.

scholarly journals Cyber Bullying Detection for Twitter Using ML Classification Algorithms

2021 ◽  
Vol 9 (11) ◽  
pp. 24-29
Author(s):  
Muskan Patidar
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Natural Language ◽  
Naive Bayes ◽  
Learning Algorithms ◽  
Naïve Bayes ◽  
Cyber Bullying ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Classification Algorithms

Abstract: Social networking platforms have given us incalculable opportunities than ever before, and its benefits are undeniable. Despite benefits, people may be humiliated, insulted, bullied, and harassed by anonymous users, strangers, or peers. Cyberbullying refers to the use of technology to humiliate and slander other people. It takes form of hate messages sent through social media and emails. With the exponential increase of social media users, cyberbullying has been emerged as a form of bullying through electronic messages. We have tried to propose a possible solution for the above problem, our project aims to detect cyberbullying in tweets using ML Classification algorithms like Naïve Bayes, KNN, Decision Tree, Random Forest, Support Vector etc. and also we will apply the NLTK (Natural language toolkit) which consist of bigram, trigram, n-gram and unigram on Naïve Bayes to check its accuracy. Finally, we will compare the results of proposed and baseline features with other machine learning algorithms. Findings of the comparison indicate the significance of the proposed features in cyberbullying detection. Keywords: Cyber bullying, Machine Learning Algorithms, Twitter, Natural Language Toolkit

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