scholarly journals Internet of Things: Comparative Study on Classification Algorithms (k-NN, Naive Bayes and Case based Reasoning)

2015 ◽  
Vol 130 (12) ◽  
pp. 7-9 ◽  
Author(s):  
Roshna Chettri ◽  
Shrijana Pradhan ◽  
Lekhika Chettri
Keyword(s):  
Internet Of Things ◽  
Comparative Study ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Classification Algorithms ◽  
Case Based Reasoning ◽  
Case Based

A novel case-based reasoning system for breast cancer diagnosis: considering the effects of external characteristics of cases Abstract (Preprint)

2021 ◽  
Author(s):  
Dongxiao Gu ◽  
Wang Zhao ◽  
Xuejie Yang ◽  
Kaixiang Su ◽  
Changyong Liang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Diagnosis ◽  
Naive Bayes ◽  
Breast Cancer Diagnosis ◽  
Naïve Bayes ◽  
Case Based Reasoning ◽  
Reasoning System ◽  
System P ◽  
External Characteristics ◽  
Case Based

BACKGROUND Artificial intelligence can help physicians improve the accuracy of breast cancer diagnosis. However, the effectiveness of AI applications is limited by doctors’ adoption of the results recommended by the AI systems. A case-based reasoning system for breast cancer diagnosis (CBR-BCD) that considers the effects of external characteristics of cases (ECC) can not only provide doctors with more accurate results for auxiliary diagnosis, but also improve doctors’ trust in the results, so as to encourage doctors to adopt the results recommended by the system. OBJECTIVE The objective of our study is to develop a novel integrated case-based reasoning (CBR) framework based on Naive Bayes and K-Nearest Neighbor (KNN) algorithms considering the effects of external characteristics of cases (CBR-ECC) and a corresponding system named CBR-BCD to assist in diagnosis and promote adoption by doctors. METHODS We used a real-world data set from the Maputo Central Hospital in Mozambique and constructed the CBR-ECC model and corresponding CBR-BCD system. We performed data processing and obtained six internal features and three external features of the cases. We randomly divided the 1214 cases into a training group and a testing group. The performance of the model was evaluated by accuracy and the area under the receiver operating characteristic curve (AUC). RESULTS The system based on the CBR-ECC model was developed. In the first stage of this model, Naive Bayes showed the best performance, compared with KNN and J48 decision tree classifiers, with an accuracy rate of 95.87%. In the second stage, the accuracy of the KNN model with the optimal K value of 2 was 99.40%. In the third stage, after considering the external characteristics of the cases, the rankings of recommendation changed. Finally, we report the users’ evaluation of the novel CBR system in a real hospital scenario; we found that it is superior to the original system. CONCLUSIONS CBR-BCD not only enables accurate case recommendations to support health practitioners in diagnosing breast cancer and reducing diagnostic inaccuracies, but also facilitates the adoption of system-recommended results by physicians, which is valuable for clinicians to assist in diagnosis. It enables the early screening of breast cancer to improve the quality of breast cancer management and reduces the socioeconomic burden compared to traditional methods.

scholarly journals ANALISIS PERBANDINGAN TIGA METODE UNTUK MENDIAGNOSA PENYAKIT MATA PADA MANUSIA

2021 ◽  
Vol 8 (4) ◽  
pp. 1654-1664
Author(s):  
Ahmad Fahmi Adam
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Case Based Reasoning ◽  
Certainty Factor ◽  
Case Based

Untuk mendiagnosa penyakit mata pada manusia diperlukan perhitungan probabilitas yang terbaik. Karena mata merupakan salah satu bagian terpenting pada tubuh manusia yang harus di jaga kesehatannya. Penelitian ini bertujuan untuk menganalisis perbandingan dari 3 metode diantaranya : metode Case-Based Reasoning, Naïve Bayes dan Certainty Factor sehingga bisa diketahui metode mana yang terbaik untuk melakukan pendiagnosaan. Setelah melakukan perbandingan, untuk perhitungan metode Case-Based Reasoning didapatkan hasil probabilitas 61,6 %, metode Naïve Bayes didapatkan hasil 56,36% dan metode Certainty Factor didapatkan hasil 90,4%. Dapat disimpulkan, metode Certainty Factor adalah metode yang terbaik untuk melakukan pendiagnosaan penyakit mata pada manusia. Setelah itu, akan dibuatkan suatu sistem pakar menggunakan metode Certainty Factor untuk mendiagnosa penyakit mata pada manusia. Sistem pakar merupakan peniru suatu pakar dalam melakukan diagnosis suatu penyakit. Tujuan dibuatkan sistem pakar ini, supaya dapat membantu pasien untuk mendiagnosa jenis penyakit mata apa berdasarkan gejala gejala yang dialaminya.

A novel case-based reasoning system for breast cancer diagnosis: considering the effects of external characteristics of cases (Preprint)

2021 ◽  
Author(s):  
Dongxiao Gu ◽  
Wang Zhao ◽  
Xuejie Yang ◽  
Kaixiang Su ◽  
Changyong Liang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Diagnosis ◽  
Naive Bayes ◽  
Breast Cancer Diagnosis ◽  
Naïve Bayes ◽  
Case Based Reasoning ◽  
Reasoning System ◽  
System P ◽  
External Characteristics ◽  
Case Based

BACKGROUND Artificial intelligence can help physicians improve the accuracy of breast cancer diagnosis. However, the effectiveness of AI applications is limited by doctors’ adoption of the results recommended by the AI systems. A case-based reasoning system for breast cancer diagnosis (CBR-BCD) that considers the effects of external characteristics of cases (ECC) can not only provide doctors with more accurate results for auxiliary diagnosis, but also improve doctors’ trust in the results, so as to encourage doctors to adopt the results recommended by the system. OBJECTIVE The objective of our study is to develop a novel integrated case-based reasoning (CBR) framework based on Naive Bayes and K-Nearest Neighbor (KNN) algorithms considering the effects of external characteristics of cases (CBR-ECC) and a corresponding system named CBR-BCD to assist in diagnosis and promote adoption by doctors. METHODS We used a real-world data set from the Maputo Central Hospital in Mozambique and constructed the CBR-ECC model and corresponding CBR-BCD system. We performed data processing and obtained six internal features and three external features of the cases. We randomly divided the 1214 cases into a training group and a testing group. The performance of the model was evaluated by accuracy and the area under the receiver operating characteristic curve (AUC). RESULTS The system based on the CBR-ECC model was developed. In the first stage of this model, Naive Bayes showed the best performance, compared with KNN and J48 decision tree classifiers, with an accuracy rate of 95.87%. In the second stage, the accuracy of the KNN model with the optimal K value of 2 was 99.40%. In the third stage, after considering the external characteristics of the cases, the rankings of recommendation changed. Finally, we report the users’ evaluation of the novel CBR system in a real hospital scenario; we found that it is superior to the original system. CONCLUSIONS CBR-BCD not only enables accurate case recommendations to support health practitioners in diagnosing breast cancer and reducing diagnostic inaccuracies, but also facilitates the adoption of system-recommended results by physicians, which is valuable for clinicians to assist in diagnosis. It enables the early screening of breast cancer to improve the quality of breast cancer management and reduces the socioeconomic burden compared to traditional methods.

scholarly journals PENENTUAN KESESUAIAN LAHAN PERTANIAN TANAMAN CABAI MENGGUNAKAN METODE NAÏVE BAYES DI KABUPATEN KUPANG

2021 ◽  
Vol 9 (1) ◽  
pp. 56-64
Author(s):  
Welmy Sinlae ◽  
Sebastianus A. S. Mola ◽  
Nelci Dessy Rumlaklak
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Case Based Reasoning ◽  
Case Based

Tanaman cabai merupakan salah satu tanaman yang dibudidayakan di Nusa Tenggara Timur (NTT). Wilayah Kabupaten Kupang merupakan salah satu wilayah penghasil cabai yang ada di NTT. Produksi cabai secara keseluruhan di Kabupaten Kupang pada tahun 2019 sampai 2020 mengalami peningkatan. Namun peningkatan produksi ini belum maksimal mengingat banyaknya lahan yang belum dimanfaatkan sebagai lahan pertanian. Oleh karena itu dibutuhkan sebuah sistem yang membantu dalam menentukan kesesuaian lahan pertanian untuk penanaman cabai. Dalam penelitian ini penulis menerapkan penalaran berbasis kasus/case-based reasoning (CBR) dalam menentukan kesesuaian lahan pertanian tanaman cabai. Metode yang digunakan dalam penelitian ini adalah Naïve Bayes dengan 7 kriteria yaitu: curah hujan, drainase, tekstur tanah, kedalaman tanah, C-organik, kemiringan lahan dan bahaya terjadinya bencana. Proses pencarian solusi dimulai dengan mengeliminasi data yang tidak relevan menggunakan metode Naive Bayes dan berlanjut dengan perankingan nilai kemiripan terbaik menggunakan KNN. Berdasarkan hasil pengujian dengan 110 kasus lahan cabai didapatkan hasil akurasi tertinggi sebesar 92.2% dan rata-rata hasil akurasi dari keseluruhan fold sebesar 89.1%.      

Case Based Reasoning And Naive Bayes Implementation In Laptop Purchasing Recommender System

2021 ◽  
Vol 9 (3) ◽  
pp. 411
Author(s):  
I Gusti Ngurah Agung Dharmawangsa ◽  
I Wayan Supriana
Keyword(s):  
Recommender System ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Case Based Reasoning ◽  
The Right ◽  
Case Based ◽  
Selection Of ◽  
The Ideal

Purchasing a new laptop will be difficult if we do not know what the ideal laptop specification for our needs. Especially with a wide selection of laptops. From this problem, system that can give a recommendation to choose the right laptop based on purchaser’s specification choice is needed. This research using two method, Case Based Reasoning and Naive Bayes. The concept of Case Based Reasoning is the process of solving new problems based on the solutions of similar past problems, While Naive Bayes assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature. Naive bayes will be implemented in retrive process of case based reasoning. The recommender system utilizing 7 feature, Kecepatan Processor, Kapasitas Ram, Tipe Grafis, Ukuran Layar, Ukuran Harddisk, Kecepatan Layar, and Harga. The percentage of respondents who said the system was successful in providing the right recommendations was 70 percent of the total respondents.

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

scholarly journals Structural Health Monitoring using Varied Machine Learning Algorithms

2018 ◽  
Vol 7 (3.12) ◽  
pp. 793 ◽  
Author(s):  
B Shanthi ◽  
Mahalakshmi N ◽  
Shobana M
Keyword(s):  
Machine Learning ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Naive Bayes ◽  
Learning Algorithms ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Classification Algorithms ◽  
Structural Health

Structural Health Monitoring is essential in today’s world where large amount of money and labour are involved in building a structure. There arises a need to periodically check whether the built structure is strong and flawless, also how long it will be strong and if not how much it is damaged. These information are needed so that the precautions can be made accordingly. Otherwise, it may result in disastrous accidents which may take away even human lives. There are various methods to evaluate a structure. In this paper, we apply various classification algorithms like J48, Naive Bayes and many other classifiers available, to the dataset to check on the accuracy of the prediction determined by all of these classification algorithms and ar-rive at the conclusion of the best possible classifier to say whether a structure is damaged or not.  

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