Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Mohammad DIQI"'
Publikováno v:
Compiler, Vol 13, Iss 1, Pp 1-10 (2024)
Recent advancements in the field of information security have underscored the imperative to fine-tune Bcrypt parameters, particularly focusing on the optimal number of rounds as the objective of research. The method of research is a Brute Force Searc
Externí odkaz:
https://doaj.org/article/e0d5373f89d54337a8713e4d4673ad6e
Autor:
Marselina Endah Hiswati, Mohammad Diqi, Izattul Azijah, Yeyen Subandi, Azzah Fathinah, Rahayu Cahya Ariani
Publikováno v:
Teknika, Vol 13, Iss 3 (2024)
This study focuses on improving how we classify fetal health using machine learning by fine-tuning the CatBoostClassifier with Grid Search. Our main achievement in this research is significantly boosting the accuracy of fetal health classification ba
Externí odkaz:
https://doaj.org/article/59c20a9963144bfb9101ea86c8a4048f
Publikováno v:
Techne, Vol 23, Iss 1 (2024)
This study investigates the application of the ConcaveLSTM model, a novel machine learning approach combining the strengths of Stacked Long Short-Term Memory (LSTM) and Bidirectional LSTM, for predicting natural gas prices. Given the inherent volatil
Externí odkaz:
https://doaj.org/article/c205a01ef4dd41478dc90d1a1455196c
Publikováno v:
Jurnal Informatika dan Rekayasa Perangkat Lunak, Vol 5, Iss 2, Pp 113-121 (2023)
This study explores the application of the Transformer model in sentiment analysis of tweets generated by ChatGPT. We used a Kaggle dataset consisting of 217,623 instances labeled as "Good", "Bad", and "Neutral". The Transformer model demonstrated hi
Externí odkaz:
https://doaj.org/article/0411e49fee8d4502ba900f2a5fd90f4f
Autor:
Mohammad Diqi, Hamzah Hamzah
Publikováno v:
JISKA (Jurnal Informatika Sunan Kalijaga), Vol 9, Iss 1 (2024)
This research aimed to enhance stock price prediction accuracy using the Stacked Bidirectional Long Short-Term Memory (StacBi LSTM) model. The study addressed the challenge of capturing long-term dependencies and temporal patterns inherent in stock p
Externí odkaz:
https://doaj.org/article/5d97508aad9144da8326471afe6c8597
Publikováno v:
JOIN: Jurnal Online Informatika, Vol 8, Iss 1, Pp 107-114 (2023)
This research paper proposes a novel method, Exponential Moving Average Long Short-Term Memory (EMA LSTM), for multi-step vector output prediction of time series data using deep learning. The method combines the LSTM with the exponential moving avera
Externí odkaz:
https://doaj.org/article/b6c6b651c95b4999abfc45e07f2e9f47
Autor:
Erizal ERIZAL, Mohammad DIQI
Publikováno v:
Applied Computer Science, Vol 19, Iss 3 (2023)
Stock prediction is an exciting issue and is very much needed by investors and business people to develop their assets. The main difficulties in predicting stock prices are dynamic movements, high volatility, and noises caused by company performance
Externí odkaz:
https://doaj.org/article/c535d714a091467ea042805c026a0cee
Publikováno v:
SN Computer Science. 4
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::023d7f1adb15abf07859d3b385fbb296
https://doi.org/10.1007/s42979-023-01834-w
https://doi.org/10.1007/s42979-023-01834-w
Publikováno v:
International Journal of Information Technology. 14:2309-2315
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e48a6d816f832196a4de7516b79a73b4
https://doi.org/10.1007/s41870-022-00929-6
https://doi.org/10.1007/s41870-022-00929-6
Autor:
Mohammad Diqi
Publikováno v:
International Conference on Information Science and Technology Innovation (ICoSTEC). 1:130-135
One of the cornerstones to efficient waste management is proper and accurate waste classification. However, people find it challenging to categorize such a big and diverse amount of waste. As a result, we employ deep learning to classify waste effici
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::009290a358883cee84ca2dedc770599a
https://doi.org/10.35842/icostec.v1i1.17
https://doi.org/10.35842/icostec.v1i1.17