Zulfikar Gutama, Suhita Whini Setyahuni, Maria Safitri, Diana Puspitasari
Introduction: This study examines the roles of gold and Bitcoin as safe-haven assets amid global financial uncertainties from 2014 to 2025. While gold is traditionally viewed as a stable asset during market turmoil, Bitcoin's highly volatile and speculative nature challenges its role as a safe-haven. The purpose is to compare the stability and effectiveness of these assets in preserving investment value during crises.Methods: This quantitative research uses 136 monthly return data points of gold and Bitcoin. Analytical methods include independent sample t-tests for comparing average returns and Dynamic Conditional Correlation Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) models to assess volatility and dynamic correlations.Results: Findings reveal gold as a more stable and reliable safe-haven with consistently lower volatility and steadier returns. Bitcoin shows significantly higher volatility and fluctuating dynamic correlations with gold, indicating its speculative behavior. The t-test confirms a significant difference in average returns, while no strong causal relationship exists between the two assets. These results suggest gold is preferable for conservative investors, whereas Bitcoin serves better as a high-risk diversification instrument. Keywords: Bitcoin, Gold, DCC-GARCH, Safe Haven Asset, Investment Asset
Bitcoin is one of the most prominent digital assets in the modern financial era due to its high volatility and huge profit potential. However, its extreme price volatility also makes it a high-risk asset, so a reliable forecasting approach is needed to help investors make more rational decisions. This study aims to forecast Bitcoin price using the Moving Average (MA) method, specifically MA3, by utilizing monthly historical data of Bitcoin price in USD currency obtained from investing.com website. The MA3 method was chosen for its ability to smooth out short-term fluctuations and identify the direction of price trends. The forecasting process is performed by calculating the average of the last three months' prices for each point in time and compared to the actual price to evaluate its accuracy. The evaluation is done using various prediction error metrics, namely Error, Absolute Error, Squared Error, and Percentage Error. The results of the analysis show that the MA method provides a fairly representative picture of price trends and can be used as an early indicator in short-term investment strategies. Thus, the Moving Average method proves to be a simple but effective prediction tool, especially for novice investors in the dynamic crypto asset market.
Volatilitas pasar yang tinggi serta potensi keuntungan besar dari cryptocurrency menjadikan prediksi harga sebagai topik penelitian yang menarik. Penelitian ini bertujuan untuk memprediksi harga cryptocurrency Stellar (XLM) dengan menerapkan metode Deep Learning, yaitu Long Short-Term Memory (LSTM) dan Gated Recurrent Unit (GRU). Data yang digunakan mencakup harga harian XLM selama beberapa tahun terakhir, serta indikator teknikal dan aktivitas perdagangan. Model LSTM dan GRU dievaluasi berdasarkan akurasi dalam memprediksi harga XLM menggunakan metrik MAPE, RMSE, dan MSE. Hasil menunjukkan bahwa meskipun keduanya mampu menangkap pola tren jangka pendek, model GRU memberikan hasil yang lebih unggul. GRU mencatat MAPE sebesar 3.6164%, RMSE sebesar 0.0206, dan MSE sebesar 0.0004. Sementara itu, LSTM mencatat MAPE sebesar 4.5638%, RMSE sebesar 0.0244, dan MSE sebesar 0.0005. Temuan ini menunjukkan bahwa GRU lebih efektif dalam memodelkan kompleksitas dan non-linearitas data harga XLM dibandingkan LSTM. Dengan demikian, GRU dapat dipertimbangkan sebagai metode yang lebih unggul dalam prediksi harga cryptocurrency. Hasil penelitian ini diharapkan dapat memberikan kontribusi dalam pengembangan model prediksi yang lebih akurat serta membantu pengambilan keputusan investasi yang lebih bijak. The high market volatility and significant profit potential of cryptocurrencies have made price prediction a compelling area of research. This study aims to predict the price of Stellar (XLM), a widely recognized cryptocurrency, by applying deep learning methods, namely Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The dataset includes daily XLM prices over the past few years, along with technical indicators and trading activity data. The LSTM and GRU models are evaluated based on their accuracy in predicting XLM prices using metrics such as MAPE, RMSE, and MSE. The results show that while both models are capable of capturing short-term trends, the GRU model outperforms LSTM. GRU achieved a MAPE of 3.6164%, RMSE of 0.0206, and MSE of 0.0004, whereas LSTM recorded a MAPE of 4.5638%, RMSE of 0.0244, and MSE of 0.0005. These findings indicate that GRU is more effective in modeling the complexity and non-linearity of XLM price data compared to LSTM. Therefore, GRU can be considered a superior approach for cryptocurrency price prediction. This study is expected to contribute to the development of more accurate forecasting models and to support better investment decision-making.
This study discusses phishing detection on the Ethereum network using machine learning methods, specifically Graph Convolutional Networks (GCNs) and Enhanced Graph Attention Networks (EGAT). The background of this research is based on the increasing number of phishing attacks in the blockchain ecosystem that can threaten the financial security of users. The research aims to analyze the incidence rate of phishing attacks and develop effective and efficient detection methods. The methodology includes data collection from Ethereum transactions and phishing activities, followed by feature extraction, machine learning model training, and evaluation using metrics such as accuracy, precision, recall, and F-score. The identified research gap is the lack of focus on early-stage phishing detection in the Ethereum network and the suboptimal performance of existing methods in recognizing complex transaction patterns. The results indicate that EGAT achieves an accuracy of 93.6%, outperforming GCNs, which reach 91.2%. The conclusion of this research is that the EGAT method is superior in detecting phishing activities, providing significant contributions to security in the Ethereum network.
Ahmad Jurnaidi Wahidin, Rayhan Maulana Sugiharto Putra
Penelitian ini mengkaji potensi investasi dalam cryptocurrency dengan menerapkan metode Net Present Value (NPV) sebagai sistem pendukung keputusan untuk menilai dan membandingkan tiga cryptocurrency yaitu Bitcoin (BTC), Ethereum (ETH), dan Binance Coin (BNB). Cryptocurrency telah muncul sebagai instrumen investasi yang menarik perhatian besar dalam beberapa tahun terakhir, terutama di kalangan milenial, karena dianggap sebagai mata uang masa depan. Dalam penelitian ini, kinerja ketiga cryptocurrency dianalisis berdasarkan tiga kriteria utama: Tingkat Pertumbuhan Tahunan (Annual Growth Rate), Volume Perdagangan (Trading Volume), dan Ketersediaan di Platform Pertukaran Terkemuka (Availability on Major Exchange Platforms). Data dikumpulkan dari berbagai sumber tepercaya seperti bursa cryptocurrency dan laporan keuangan. Perhitungan NPV dilakukan untuk mengukur nilai sekarang dari arus kas masa depan yang diharapkan dari masing-masing cryptocurrency. Hasil penelitian menunjukkan bahwa Binance Coin (BNB) memiliki nilai NPV tertinggi sebesar $29.416,91, diikuti oleh Ethereum (ETH) dengan NPV sebesar $26.085,74, dan Bitcoin (BTC) dengan NPV sebesar $22.948,93. Ini menunjukkan bahwa BNB menawarkan nilai investasi terbaik di antara ketiga cryptocurrency yang dianalisis, berdasarkan kriteria yang ditetapkan. Penelitian ini menjadi sistem pendukung bagi investor untuk membuat keputusan investasi yang lebih informasional dan beralasan dalam pasar cryptocurrency yang fluktuatif.
Blockchain technology has revolutionized secure and decentralized digital transactions. However, the emergence of quantum computing presents a significant threat to traditional cryptographic protocols, particularly public-key encryption mechanisms such as RSA and Elliptic Curve Cryptography (ECC). Quantum computers, leveraging Shor’s and Grover’s algorithms, can efficiently break these encryption schemes, compromising blockchain security. This paper explores quantum-secure blockchain protocols that integrate post-quantum cryptographic (PQC) techniques such as lattice-based, hash-based, and code-based cryptography to resist quantum attacks. Additionally, we evaluate quantum-resistant consensus mechanisms like Quantum-Secure Proof of Stake (QS-PoS) and Quantum-Protected Byzantine Fault Tolerance (Q-BFT). Through simulation-based performance analysis, we demonstrate that quantum-safe blockchain models can achieve robust security while maintaining efficient transaction processing. Our findings suggest that a hybrid approach, combining classical cryptographic elements with post-quantum algorithms, provides the best balance between security, performance, and scalability.
Proof-of-Context (PoC) protocols aim to ensure fairness and integrity in smart contract execution by cryptographically binding on-chain transactions to verifiable off-chain contextual data. Traditional consensus mechanisms (e.g., Proof-of-Work, Proof-of-Stake) focus on ordering and validation of transactions but do not address whether the contextual conditions that should govern contract execution are satisfied. In this manuscript, we propose a novel PoC framework that leverages decentralized oracles, zero-knowledge proofs, and time-stamped Merkle commitments to provide verifiable evidence that all pre-specified preconditions and environmental parameters were met at execution time. We detail the design of the protocol, implement a prototype on an Ethereum testnet using Chainlink oracles and zk-SNARKs, and conduct a performance evaluation under varying network and workload conditions. Our results show that PoC incurs a modest overhead—on average 5% additional gas cost and 200 ms added latency per proof generation—while dramatically enhancing auditability and reducing the risk of context-based manipulation or dispute. We conclude that PoC protocols offer a practical mechanism for enforcing fairness in a wide range of decentralized applications, from DeFi loans conditioned on real-world data to NFT minting events gated by dynamic criteria. Finally, we discuss the scope, limitations, and future research directions for broader deployment.
One type of insurance that people purchase to cover unexpected medical expenses is health insurance. In exchange for premium payments, the insurance company may cover a portion of the insured person's medical expenses, such as prescription medications, hospital stays, and doctor visits. This method makes access to healthcare easier and less expensive. Health insurance systems do, however, have a number of issues, including fair insurance premium calculation, automation, data verification, privacy and security, and cost effectiveness. These issues are starting to be addressed by blockchain technology, particularly with the help of smart contracts. Using a comparison analysis between Ethereum and Optimism smart contracts, this paper demonstrates the performance of health insurance. Simulation of these BC technologies was carried out both on the Sepolia testnet and using Alchemy. Tools and metrics provided to monitor the performance of Alchemy applications, detect errors, and analyze user interactions were used in the measurements. While Ethereum's well-established ecosystem offers robust support for smart contracts, Optimism distinguishes itself as a scalable substitute that delivers quicker transaction speeds and more affordable options. According to the analysis results, the advantages and disadvantages of Ethereum and Optimism are highlighted when it comes to health insurance.
M Santosa, Ni Luh Wiwik Sri Rahayu Ginantra, Ida Bagus Ary Indra Iswara, Desak Made Dwi Utami Putra
Cryptocurrencies, which are digital assets intended to function as a medium of electronic exchange, have garnered significant global attention, with Bitcoin standing out as the most recognized example. A variety of neural network models, including Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU), have been employed in the quest to predict Bitcoin’s price movements. Numerous experiments have been conducted using different training configurations, specifically with epochs set at $100,200,300$, and 500, along with batch sizes of 32,64, and 128. These tests have consistently demonstrated that the GRU model surpasses both the RNN and LSTM models in terms of prediction accuracy and overall consistency. The results indicate that GRU presents a more reliable framework for forecasting trends in Bitcoin prices, offering greater stability and precision. Consequently, the GRU model is emerging as a preferred choice for those aiming to refine predictive analytics within the cryptocurrency market.
As machine learning spreads into fields of use that demand secure and private authentication, ensuring such authentication is becoming increasingly critical.Zero Knowledge Proofs (ZKPs) have been presented as a cryptographic technique of transforming authentication without data leakage [1].In this research, the use of ZKPs in the AI authentication frameworks is looking into privacy, security and scalability.The model predictions are verified by the proposed system using advanced ZKP protocols like zkSNARKs and zkSTARKs without revealing model parameters or user inputs [3].Our system is able to reach better computational efficiency and lower computation overhead through incorporation of Mystique conversion protocols [7] and fast ZK inference protocols such as ezDPS [6].Results of experiments [5] show that frameworks with ZKP integrated authentication perform better than the standard encryption with respect to both security and performance in decentralized machine learning regimes.Moreover, the solution facilitates verifiability in Federated Learning by integrating blockchain, which helps to increase transparency and trust [4].To overcome the data leakage issue, ZKPs are explored for use in decentralized AI frameworks where secure model deployment is required to generate personalized advice [4].As this research shows, ZKPs offer transformative properties which can be used for authentication in AI systemssuch as in healthcare, finance or IoT network and thus increase the trust in AI driven solutions.
R. Ramani, S. Gangadhar, A. Balaganesh, M. Ganganathan
Rise of digital technologies, the safety and reliability of online tests have been emerged as a critical concerned. To enhance the trust worthiness and reliability of online tested services, “Crypto Proctor” stands out as an innovative solution that employs blockchain technology. This abstract provides an overview of the project's key components and may have an impact on online education as a whole. Traditional online assessment platforms often have challenges with authenticating users, ensured data accuracy and combating concern-cheat. Blockchain technology enhances the safety of online exams with a distributed and immutable ledger called “Crypto Proctor.”. This work's overarching objective is to provide the groundwork for an unbreakable testing environment. By utilizing blockchain technology, “Crypto Proctor” aimed to enhance the credibility and safety of online exams. Distributed and guaranteed immutability is the platform's primary goal in prevented fraudulent behavior, protected user identities, and preserved the integrity of examination data. The “Crypto Proctor” solution incorporates blockchain protocols for the following purposes: a) Questioned delivery b) answered submission c) User identity verification. Smart contracts which are automated and confirm certain aspects of the inspection process as well as ensure that all records are securely and publicly record on the blockchain. An auditable and immutable recorded of every test session is intended to produce by the used methodologies. When it has tested in online platforms, Crypto Proctor is a huge leap forward. This initiative leverages blockchain technology to be addressed prevalent issues of data security and cheated, establishing a sensed of confidence and reliability in the online assessment procedure. As the educational system adapts to digital transformation, “Crypto Proctor” served as a beacon, showed how blockchain technology may fortify the foundations of online learning process and evaluations.
Analisis sentimen terhadap cryptocurrency telah menjadi topik penting dalam riset dan pengembangan di bidang keuangan dan teknologi informasi. Twitter, sebagai platform media sosial yang populer, menjadi sumber data yang berharga untuk memahami sentimen pengguna terhadap cryptocurrency. Penelitian ini bertujuan untuk melakukan analisis sentimen terhadap cryptocurrency berdasarkan data dari Twitter menggunakan algoritma Naive Bayes dan Decision Tree. Metode yang digunakan melibatkan pengumpulan data dari Twitter yang berisi percakapan terkait cryptocurrency. Data tersebut kemudian dibersihkan, diproses, dan dianalisis menggunakan algoritma Naive Bayes dan Decision Tree. Naive Bayes digunakan untuk mengklasifikasikan sentimen menjadi positif, negatif, atau netral berdasarkan fitur-fitur teks dari tweet. Sementara itu, Decision Tree digunakan untuk membangun model prediktif yang dapat mengidentifikasi pola sentiment terhadap cryptocurrency..Hasil penelitian menunjukkan bahwa akurasi Naïve Bayes mencapai 80.222%, sedangkan Decision Tree mencapai 65.03%. Dari hasil ini, dapat disimpulkan bahwa Naïve Bayes lebih baik dalam mengklasifikasikan text mining dengan akurasi tertinggi. Perbandingan antara kedua metode menunjukkan perbedaan akurasi yang tidak signifikan, yaitu untuk Naïve Bayes dengan akurasi 80.22%, Presisi 96.90%, dan Recall 62.54%, serta Decision Tree dengan akurasi 65.03%, Presisi 52.02%, dan Recall 98.94%. Analisis opini publik terhadap cryptocurrency mengungkapkan bahwa masyarakat Indonesia cenderung memberikan tanggapan positif terhadap mata uang digital ini setelah dilakukan penelitian ini.
Teknologi blockchain telah menjadi fokus utama dalam pengembangan aplikasi web terdesentralisasi, membuka era baru dalam evolusi internet yang dikenal sebagai Web3. Di tengah perkembangan ini, perusahaan startup PT. Pintar Media Teknologi memperkenalkan aplikasi launchpad cryptocurrency yang mengandalkan teknologi blockchain untuk memberikan solusi terdesentralisasi dalam perdagangan aset crypto. Penulis mendapatkan kesempatan untuk melakukan penelitian pada PT. Pintar Media Teknologi untuk membangun aplikasi launchpad cryptocurrency. Metodologi pengembangan aplikasi blockchain yang diterapkan adalah metodologi Agile Scrum. Melalui analisis fitur-fitur yang dikembangkan, seperti perancangan Entity Relationship Diagram, desain basis data, hingga implementasi fitur-fitur utama seperti autentikasi menggunakan Web3, manajemen token, dan manajemen launchpad. Hasil penelitian ini memberikan wawasan tentang praktik pengembangan aplikasi blockchain dan kontribusi penulis pada penelitian ini adalah merancang dan mengimplementasi sistem informasi launchpad cryptocurrency sesuai dengan kebutuhan para stakeholders sehingga dapat mempercepat proses pengembangan produk.
The feature of Blockchain as distributed ledger that are shared among nodes within a computer network, renowned for its pivotal role in cryptocurrency systems by ensuring a secure and decentralized the role of transaction record which is ensured for maintenance of security and decentralization in cryptocurrency systems.The Linux Foundation host the open-source framework of private blockchain, the Hyperledger Fabric (HLF).Smart contracts are utilized for transaction management and a modular architecture of blockchain framework, providing a foundation for the development of blockchain-based applications through plug-and-play components.In the realm of distributed systems, scalability emerges as a crucial design goal for developers.The most appropriate blockchain platform for the operations of the business industry, which need for the seamless addition of more users and resources without perceptible performance loss.An assessment of scalability is required as a large number of nodes involvement in the implementation of blockchain frameworks.In this paper, the impact of system configurations such as, transaction volume, node types is focused in the transition of V2.2.4 with the various significant issues with the architecture.The throughput, latency, processor, and memory usages are mainly analyzed based on the different number of transactions.According to the performance results of the proposed system, the scalability of the possible number of transactions and the different peer nodes can be supported in the implementation of blockchain-based system for HLF blockchain.
Book Title: Exploring the Frontiers of Artificial Intelligence and Machine Learning Technologies Editors: Mr. Agha Urfi Mirza and Dr. Balraj Kumar ISBN: 978-81-970457-9-0 Chapter: 6 DOI: https://doi.org/10.59646/efaimltC6/133 Authors: Dr. D. Poornima1 and Dr Vijayalakshmi chintamaneni2 1Assistant Professor, Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India.2Associate Professor, Department of Electronics and Communication Engineering, […]
ABSTRAK Penelitian ini berfokus pada masalah keamanan jaringan yang menjadi krusial bagi perusahaan teknologi blockchain dan Non-Fungible Token (NFT) yang rentan terhadap serangan siber seperti DDoS, injeksi SQL, dan malware. Serangan ini tidak hanya menyebabkan kerugian finansial tetapi juga merusak reputasi dan kepercayaan pengguna. Suricata, sebagai sistem deteksi dan pencegahan intrusi open-source, menawarkan berbagai fitur untuk memonitor dan menganalisis lalu lintas jaringan secara real-time. Penelitian ini mengevaluasi efektivitas Suricata dalam mendeteksi ancaman pada web server NFT melalui pendekatan eksperimental. Pengujian dilakukan dengan metode scanning port, web penetration testing, DDoS, dan identifikasi kerentanan sistem web server menggunakan alat seperti NMap, Hping3, Nikto, dan Metasploit. Hasil menunjukkan bahwa Suricata mampu mencatat aktivitas mencurigakan dan mencegah anomali dengan integrasi firewall PFsense. Implementasi Suricata memberikan informasi deteksi serangan web scanning, meskipun tidak memiliki aturan shared object seperti perangkat lunak intrusi lainnya. Penelitian ini memberikan rekomendasi bagi pengembang dan operator platform NFT untuk melindungi aset digital mereka dari serangan siber, serta berkontribusi pada peningkatan keamanan jaringan di sektor NFT. ABSTRACT This research focuses on the critical issue of network security for blockchain technology and Non-Fungible Token (NFT) companies, which are vulnerable to cyberattacks such as DDoS, SQL injection, and malware. These attacks not only cause financial losses but also damage reputation and user trust. Suricata, an open-source intrusion detection and prevention system, offers various features to monitor and analyze network traffic in real-time. This study evaluates the effectiveness of Suricata in detecting threats on NFT web servers through an experimental approach. Testing methods include port scanning, web penetration testing, DDoS, and identifying web server vulnerabilities using tools such as NMap, Hping3, Nikto, and Metasploit. The results show that Suricata can log suspicious activities and prevent anomalies when integrated with the PFsense firewall. While Suricata provides information on web scanning attacks, it lacks shared object rules found in other intrusion software. This research offers recommendations for NFT platform developers and operators to protect their digital assets from cyberattacks and contributes to improving network security in the NFT sector. Thus, this study is highly relevant in the digital era, where information and data security are top priorities for business continuity and user privacy protection.
Ni Ketut Novia Nilasari, Made Sudarma, Nyoman Gunantara
Semakin pesatnya perkembangan teknologi saat ini, dapat memudahkan seluruh kegiatan manusia, sehingga mengakibatkan seluruh aspek tidak bisa lepas dari teknologi tanpa terkecuali bidang keuangan. Dengan berkembangnya teknologi diiringi juga dengan dikenalnya berbagai instrument investasi. Setiap melaksanakan investasi tentu akan selalu ada berbagai resiko yang menyertainya termasuk investasi cryptocurrency salah satunya bitcoin. Tidak seperti mata uang konvensional, bitcoin bersifat tidak desentralisasi sehingga perkembangan harganya tidak dalam pengawasan atau kontrol pihak manapun, dimana jika uang konvensional ada lembaga tertentu yang mengawasi dan mengontrol pergerakannya. Hal tersebut mengakibatkan harga nilai tukar dari bitcoin menjadi tidak konsisten atau tidak stabil. Dengan terdapatnya metode prediksi, pengguna bitcoin bisa menetapkan waktu yang pas untuk menjalankan transaksi. Penelitian ini memiliki tujuan guna memprediksi harga bitcoin dengan menggunakan metode LSTM serta Bi-LSTM. Berdasarkan hasil penelitian diperoleh hasil prediksi terbaik menggunakan metode Bi-LSTM dengan RMSE 1482.73 sedangkan dengan LSTM menghasilkan RMSE sebesar 1768.69 sehingga dapat disimpulkan dari sisi akurasi Bi-LSTM memberikan hasil yang lebih akurat hanya saja dengan Bi-LSTM membutuhkan resourse yang lebih banyak.
The implementation of blockchain technology alongside Artificial Intelligence features that strengthen Internet of Things cloud-based systems through extended data protection, enhanced robotic trust, and decentralized intelligence capabilities.Both potential benefits and obstacles of building blockchain-empowered collaborative AI systems that perform secure computations across multiple parties and present architectural guidelines for privacy protection.Digital transformation now drives various industries forward because of the power combination between IoT and distributed ledger technology and their alignment with AI and edge-fog-cloud computing environments.Blockchain integration with IoT networks protects data integrity by remedying vital privacy and security problems, which creates a robust system that handles decentralized, secure data management.Blockchain technology makes financial operations secure and faster across all payment transactions, trade finance, and asset management operations to build complete trust with banking institutions.Through their mutual partnership, blockchain and robotic technologies develop advanced robotic systems that exhibit better operational performance and use strengthened security systems to address blockchain weaknesses.This leads to better dependability of AIdriven service operations.Multiple forces drive blockchain integration with AI applications because users need stronger data security basics to protect confidential data from unauthorized use or tampering, and they want more reliable robot decision authentication.
The rise of Web 3.0, which is based on independent technologies like blockchain and smart contracts, marks a big change in the financial technology field. This research looks at all the different ways that Web 3.0 can be used in FinTech by looking at real-life examples from Ethereum, Betterment, Wealthfront, DeversiFi, Synthetix, Kyber Network, and Curve Finance. By combining ideas from McKinsey's research, the study shows that Web3 lending sites are growing quickly. In 2021 alone, they gave out over $200 billion in loans. The study shows how Ethereum can be used for smart contracts, how Betterment and Wealthfront's robo-advisory services use AI and machine learning, how DeversiFi's decentralized exchange handles privacy issues, how Synthetix creates on-chain digital assets, how Kyber Network's blockchain-based liquidity protocol is put into use, and how Curve Finance's decentralized platform handles stablecoin transactions. Although there has been success, integrating these technologies is still very hard. The main problems are unclear regulations and technical issues with security, scalability, and interoperability. The final success of Web 3.0 in FinTech will depend on how well these problems are solved, which will help find a good balance between fast technological progress and strong risk management.
The International Journal of Computer Engineering in Research Trends (IJCERT) is a peer-reviewed, open access journal that publishes high-quality research papers, reviews, short communications, and notes in the field of computer science engineering and its research trends. The journal covers a wide range of topics in computer science and engineering, including: Welcome to the International Journal of Computer Engineering in Research Trends (IJCERT), is a peer-reviewed, open access journal dedicated to publishing innovative research papers, reviews, short communications, and notes in the field of computer science engineering and related disciplines. IJCERT encourages conceptual, state-of-the-art, research, standard, implementation, experimental, application, and industrial case study discussions in various areas, including: computer architecture, computer networks, software engineering, information security, artificial intelligence, machine learning, data science, robotics, cyber-physical systems, the internet of things, and other areas of computer science engineering and Its Applications.
The International Journal of Computer Science Engineering and Its Research Trends (IJCERT) is a peer-reviewed, open access journal that publishes high-quality research papers, reviews, short communications, and notes in the field of computer science engineering and its research trends. The journal covers a wide range of topics in computer science and engineering, including: Welcome to the International Journal of Computer Science Engineering in Research Trends (IJCERT), is a peer-reviewed, open access journal dedicated to publishing innovative research papers, reviews, short communications, and notes in the field of computer science engineering and related disciplines. IJCERT encourages conceptual, state-of-the-art, research, standard, implementation, experimental, application, and industrial case study discussions in various areas, including: computer architecture, computer networks, software engineering, information security, artificial intelligence, machine learning, data science, robotics, cyber-physical systems, the internet of things, and other areas of computer science engineering and Its Applications.
Muhammad Sahi, Muhammad Faisal, Yunifa Miftachul Arif, Cahyo Crysdian
Bitcoin is one of the fastest-growing digital currencies or cryptocurrencies in the world. However, the highly volatile Bitcoin price poses a very extreme risk for traders investing in cryptocurrencies, especially Bitcoin. To anticipate these risks, a prediction system is needed to predict the fluctuations in cryptocurrency prices. Artificial Neural Network (ANN) is a relatively new model discovered and can solve many complex problems because the way it works mimics human nerve cells. ANN has the advantage of being able to describe both linear and non-linear models with a fairly wide range. This research aims to determine the best performance and level of accuracy of the ANN model using the Back-Propagation Neural Network (BPNN) algorithm in predicting Bitcoin prices. This study uses Bitcoin price data for the period 2020 to 2023 taken from the CoinDesk market. The results of this study indicate that the ANN model produces the best performance in the form of four input nodes, 12 hidden nodes, and one output node (4-12-1) with an accuracy rate of around 3.0617175%.
Concerts or fan meetings usually attract lots of attention, generating huge demand for tickets for these events. But, existing ticket purchase systems are unable to efficiently and transparently accommodate this. We often hear scandals of bot-controlled clients gobbling up tickets within seconds after sales open or celebrities acquiring prime tickets exceeding individual quota. These problems arise because existing systems are centralized. This can be a single point of failure and the controlling authority can ban or give privileges to certain users. This work sets out to remedy these problems using a blockchain-base solution that, by nature, is highly decentralized. We have created and deployed EVM (Ethereum Virtual Machine)-based smart contracts for ticket sales platform on two EVM-compatible blockchains, Ethereum (ETH) and Avalanche (AVAX). These contracts inherit heavily from the ERC-721 standard for NFT (Non-Fungible Token). The platforms on both blockchains engender the desirable transparency. However, the platform on Avalanche is much more efficient and economical to deploy and utilize. We have open-sourced the code for our platform on Github. Visit the following link to fork or check it out: https://github.com/JesperBerben/TicketNFT In addition, you can now interact with our platform on Avalanche which has been deployed and verified at the address linked to below: https://snowtrace.io/address/0x17fd3f6cf6cbff75604b63f1ca12c6db29730a9c