Python: The Most Advanced Programming Language for Computer Science Applications

Dhruv, Akshit J; Patel, Reema; Doshi, Nishant

doi:10.5220/0010307902920299

Cited by 13 publications

(7 citation statements)

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“…Python is the programming language with many useful libraries in comparison to other programming languages and available at [30]. The features make python able to decrease the code to one-third for developers and programmers especially in ML area [31].…”

Section: Pythonmentioning

confidence: 99%

Predict Diabetes Using Voting Classifier and Hyper Tuning Technique

Kamal

Atiyah

2023

KJAR

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Today, diabetes is one of the most common chronic diseases in the world due to the people’s sedentary lifestyle which led to many health issues like heart attack, kidney frailer and blindness. Additionally, most of the people are unrealizable about the early-stage diabetes symptoms to prevent it. The above reasons were encouraging to develop a diabetes prediction system using machine learning techniques. The Pima Indian Diabetes Dataset (PIDD) was utilized for this framework as it is common and appropriate dataset in .CSV format. While there were not any duplicate or null values, however, some zero values were replaced, four outlier records were removed and data standardization were performed in the dataset. In addition, this project methodology divided into two phases of model selection. In the first phase, two different hyper parameter techniques (Randomized Search and TPOT(autoML)) were used to increase the accuracy level for each algorithm. Then six different algorithms (Logistic Regression, Decision Tree, Random Forest, K-nearest neighbor, Support Vector Machine and Naïve Bayes) were applied. In the second phase, the four best performed algorithms (with best estimated parameters for each of them) were chosen and used as an input for the voting classifier, because it applies to find the best algorithm between a group of multiple options. The result was satisfying, and Random Forest was achieved 98.69% in second stage, while its accuracy level was 81.04% in the previous one and it utilized to predict diabetes via a simple graphic user interface.

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Section: Pythonmentioning

confidence: 99%

Predict Diabetes Using Voting Classifier and Hyper Tuning Technique

Kamal

Atiyah

2023

KJAR

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“…We implemented a visualization tool for two alignment algorithms with the use of TKinter, an official Python library used with the Tk GUI toolkit. This Python implementation is potentially beneficial for further development since Python is becoming the most well-suited programming language for bioinformatics and deep learning applications [ 8 ].…”

Section: Implementation Details: the Step-by-step Visualization Proce...mentioning

confidence: 99%

A Python-based educational software tool for visualizing bioinformatics alignment algorithms

Khatizah¹,

Nam²,

Park³

2023

Genomics Inform

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Bioinformatics education can be defined as the teaching and learning of how to use software tools, along with mathematical and statistical analysis, to solve biological problems. Although many resources are available, most students still struggle to understand even the simplest sequence alignment algorithms. Applying visualizations to these topics benefits both lecturers and students. Unfortunately, educational software for visualizing step-by-step processes in the user experience of sequence alignment algorithms is rare. In this article, an educational visualization tool for biological sequence alignment is presented, and the source code is released in order to encourage the collaborative power of open-source software, with the expectation of further contributions from the community in the future. Two different modules are integrated to enable a student to investigate the characteristics of alignment algorithms.

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“…Python yang berorientasi objek, lalu ditafsirkan, dan menjadi bahasa pemrograman interaktif. Ini menyediakan struktur data tingkat tinggi seperti daftar, tupel, set, array asosiatif (disebut juga kamus), dinamis mengetik dan mengikat, modul, kelas, pengecualian, manajemen memori otomatis, dll [2].…”

Section: Pendahuluanunclassified

Sistem Klasifikasi Limbah Menggunakan Metode Convolutional Neural Network (CNN) Pada Webservice Berbasis Framework Flask

Dacipta

Putra²

2022

JINACS

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Abstrak— Di berbagai negara sampah merupakan permasalahan yang dihadapi sehari-hari seluruh aktivitas tidak bisa terhindar dari kata sampah. banyak sekali jenis–jenis sampah di lingkungan kita, dan kebanyakan masyarakat Indonesia masih belum bisa membedakan jenis sampah apa yang setiap hari mereka buang sehingga banyak sekali sampah yang tidak diklasifikasikan. Yang menyebabkan proses klasifikasi limbah pada pabrik daur ulang sangat susah untuk dilakukan. Maka dari itu bagaimana jika limbah yang ada diklasifikasikan menjadi 9 macam dan setiap masyarakat dapat membuang limbah sampah mereka sesuai dengan 9 macam class yang telah dibedakan yaitu battery, pakaian, e-limbah, kaca, bola lampu, metal, organic, kertas, plastic. sehingga mempermudah pabrik daur ulang dalam proses klasifikasi sampah. Oleh karena itu Sistem Klasifikasi Limbah Pada Web Service Berbasis Framework Flask akan sangat membantu masyarakat dalam mengklasifikasikan limbah sampah yang dihasilkan. Sistem klasifikasi limbah pada aplikasi berbasis web ini menggunakan metode Convolutional Neural Network (CNN). Metode CNN merupakan suatu metode deep learning yang dapat mengidentifikasi dan mengklasifikasi sebuah object pada citra digital. Penelitian ini menggunakan arsitektur VGGNet. VGGNet merupakan arsitektur dari Convolutional Neural Network. Penelitian ini menggunakan 9 class limbah sampah. Dataset yang digunakan yaitu 8371 citra limbah sampah. Dimana dataset tersebut digunakan untuk melakukan training data yang telah dibagi menjadi 1.122 citra battery, 729 pakaian, 624 e-limbah, 773 kaca, 651 bola lampu, 1092 metal, 671 organic, 1468 kertas dan 1241 plastic. Pada proses training dilakukan sebanyak 28 epoch, yang mendapatkan akurasi tertinggi 69,77% dengan loss terendah 0,34. Untuk data testing didapatkan hasil 64,45% accuracy.

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Python: The Most Advanced Programming Language for Computer Science Applications

Cited by 13 publications

References 0 publications

Predict Diabetes Using Voting Classifier and Hyper Tuning Technique

Predict Diabetes Using Voting Classifier and Hyper Tuning Technique

A Python-based educational software tool for visualizing bioinformatics alignment algorithms

Sistem Klasifikasi Limbah Menggunakan Metode Convolutional Neural Network (CNN) Pada Webservice Berbasis Framework Flask

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