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pengelolaan multi-sig (multi-signature), #4
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Menggunakan TensorFlow untuk membangun model pembelajaran mesin yang menganalisis data transaksi.
Menggunakan teknik deteksi ancaman dengan integrasi keamanan berbasis pembelajaran mesin.
Menggabungkan seluruh komponen ini dalam backend berbasis NestJS.
Penjelasan Pertama, kita melakukan transaksi di Stellar. Setelah transaksi Stellar berhasil, kita melanjutkan untuk mengirimkan ETH di jaringan Ethereum dengan jumlah yang sama. Pendekatan ini bisa dikembangkan lebih lanjut dengan menggunakan smart contracts pada Ethereum untuk mengelola aspek cross-chain secara otomatis.
Penjelasan Peningkatan AI/ML LSTM digunakan untuk mengatasi masalah data sekuensial, misalnya transaksi berdasarkan waktu. Model ini dapat mendeteksi pola dan anomali dari urutan transaksi yang lebih panjang dan lebih kompleks dibandingkan model sederhana sebelumnya. Kami menggunakan Dropout untuk menghindari overfitting, yang sering terjadi pada data sekuensial.
Dengan integrasi ini, Anda dapat: 1. Melakukan transaksi lintas blockchain antara Stellar dan Ethereum menggunakan atomic swap. 2. Meningkatkan analisis transaksi dengan LSTM untuk mendeteksi anomali lebih akurat. 3. Mengintegrasikan analisis AI langsung dalam proses transaksi untuk mengidentifikasi ancaman sebelum transaksi diselesaikan.
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