Salam, rekan Nawala! Semoga kalian selalu dalam keadaan sehat.
Ini adalah IAES Nawala dari Institute of Advanced Engineering and Science. Hari ini kami akan berbagi kabar mengenai peran machine learning. Telah sempat disinggung pada post sebelumnya, bahwasannya machine learning (ML) mencakup algoritma dan teknik yang memungkinkan komputer untuk belajar (learning) dari kumpulan data untuk menghasilkan deduksi. Algoritma ML andal dalam menyelesaikan task manusia, seperti untuk memprediksi kondisi kesehatan manusia (seperti stress hingga sakit jantung), mengenali fraud, dan masih banyak lagi. Saxena dan Robila (2023) menjelaskan di dalam penelitian mereka yang berfokus pada pengembangan sistem ML otomatis untuk menganalisis dan memprediksi kecelakaan kendaraan di New York dengan menggunakan dataset kecelakaan yang mereka miliki. Penelitian ini bertujuan untuk membuat antarmuka yang ramah pengguna dan mengimplementasikan model support vector machine (SVM) untuk menilai probabilitas kecelakaan, cedera, dan kematian di seluruh kota. Dengan memberikan perspektif seluruh kota dan tidak hanya berfokus pada segmen jalan atau wilayah tertentu, penelitian ini memberikan wawasan yang berharga bagi para ahli transportasi, pembuat kebijakan pemerintah, dan industri asuransi dengan mengukur risiko kecelakaan di lokasi tertentu.
Automated machine learning for analysis and prediction of vehicle crashes
Abhishek Saxena, Stefan A. Robila
This work discusses the study and development of a graphical interface and implementation of a machine learning model for vehicle traffic injury and fatality prediction for a specified date range and for a certain zip (US postal) code based on the New York City’s (NYC) vehicle crash data set. While previous studies focused on accident causes, little insight has been offered into how such data may be utilized to forecast future incidents, Studies that have historically concentrated on certain road segment types, such as highways and other streets, and a specific geographic region, this study offers a citywide review of collisions. Using cutting-edge database and networking technology, a user-friendly interface was created to display vehicle crash series. Following this, a support vector machine learning model was built to evaluate the likelihood of an accident and the consequent injuries and deaths at the zip code level for all of NYC and to better mitigate such events. Using the visualization and prediction approach, the findings show that it is efficient and accurate. Aside from transportation experts and government policymakers, the machine learning approach deliver useful insights to the insurance business since it quantifies collision risk data collected at specific places.
Penelitian selanjutnya dilakukan oleh Mahomodally dkk. (2023) yang berfokus pada penggunaan deep learning untuk mendeteksi penyakit pada tanaman secara otomatis. Mereka mengumpulkan dataset gambar yang mencakup 32 tanaman dan 74 jenis penyakit. Penelitian mereka menggunakan empat model computer vision yang telah dilatih sebelumnya, yaitu VGG-16, VGG-19, ResNet-50, dan ResNet-101. Hasil eksperimen yang mereka dapatkan menunjukkan model VGG-16 dan VGG-19 lebih efisien dibandingkan dengan ResNet-50 dan ResNet-101.
Machine learning techniques for plant disease detection: an evaluation with a customized dataset
Amatullah Fatwimah Humairaa Mahomodally, Geerish Suddul, Sandhya Armoogum
Diseases in edible and industrial plants remains a major concern, affecting producers and consumers. The problem is further exacerbated as there are different species of plants with a wide variety of diseases that reduce the effectiveness of certain pesticides while increasing our risk of illness. A timely, accurate and automated detection of diseases can be beneficial. Our work focuses on evaluating deep learning (DL) approaches using transfer learning to automatically detect diseases in plants. To enhance the capabilities of our approach, we compiled a novel image dataset containing 87,570 records encompassing 32 different plants and 74 types of diseases. The dataset consists of leaf images from both laboratory setups and cultivation fields, making it more representative. To the best of our knowledge, no such datasets have been used for DL models. Four pre[1]trained computer vision models, namely VGG-16, VGG-19, ResNet-50, and ResNet-101 were evaluated on our dataset. Our experiments demonstrate that both VGG-16 and VGG-19 models proved more efficient, yielding an accuracy of approximately 86% and a f1-score of 87%, as compared to ResNet-50 and ResNet-101. ResNet-50 attains an accuracy and a f1-score of 46.9% and 45.6%, respectively, while ResNet-101 reaches an accuracy of 40.7% and a f1-score of 26.9%.
Chronic kidney disease (CKD) dan diabetes mellitus (DM) merupakan dua penyakit kronis yang menjadi tantangan besar bagi masyarakat pada umumnya. Chitra dkk. (2023) melakukan penelitian yang berfokus pada prediksi CKD dengan menggunakan algoritma machine learning, seperti regresi logistik, random forest, conditional random forest, dan recurrent neural network. Dengan menggunakan data kode medis sebagai input, algoritma ini bertujuan untuk mengidentifikasi CKD pada tahap awal, mencegah perkembangannya dan meningkatkan kualitas hidup pasien.
Chronic kidney disease prediction model using machine learning approach
Munusamy Chitra, Abdul Kuthus Parveen, Murugadoss Elavarasi, Jayamoorthy Sangeetha, Ramalingam Vaittilingame
Chronic disease (CD) such as kidney disease and causes severe challenging issues to the people all around the world. Chronic kidney disease (CKD) and diabetes mellitus (DM) are considered in this paper. Predicting the diseases in earlier stage, gives better preventive measures to the people. Healthcare domain leads to tremendous cost savings and improved health status of the society. The main objective of this paper is to develop an algorithm to predict CKD occurrence using machine learning (ML) technique. The commonly used classification algorithms namely logistic regression (LR), random forest (RF), conditional random forest (CRF), and recurrent neural networks (RNN) are considered to predict the disease at an earlier stage. The proposed algorithm in this paper uses medical code data to predict disease at an earlier stage.
Beberapa artikel di atas merupakan bagian kecil dari penelitian mengenai machine learning. Untuk mendapatkan informasi lebih lanjut, pembaca dapat mengunjungi laman dan membaca artikel secara GRATIS melalui tautan berikut: https://ijict.iaescore.com/.
Redaksi: I. Busthomi
