Follow blockchain research across journals, conferences, and preprint repositories.
850 results · page 3 of 36
M Lakshmanan, G. S. Anandha Mala, K. M. Anandkumar
No abstract is available for this record.
Suja A. Alex, Ângela Maria Alves, Gabriel Caumo Vaz, Gabriel Gomes de Oliveira
No abstract is available for this record.
R. Manivannan, S. Subashree, G Venkateshwaran, G. Pushpa · 6 authors
No abstract is available for this record.
Jaijit Singh Rehal, Jude Praneet Maria, Ishika Singh Chauhan, S. D. Deshpande · 5 authors
No abstract is available for this record.
Meduri V N S S R K Sai Somayajulu, Muqeem Ahmed, Bonthu Kotaiah
No abstract is available for this record.
Çağlayan Sancaktar, Ahmet Sayar
No abstract is available for this record.
Ilham Laabab, Abdellatif Ezzouhairi, Muhammad Haris Khan
No abstract is available for this record.
Priyanka Ghosh, Ashwitha Shetty, K. A. Thanuja, K. Vinodha Devi
No abstract is available for this record.
Muthu Ramachandran
No abstract is available for this record.
L. Yeswanth, M. Gunasekaran
No abstract is available for this record.
Parijata Majumdar, Shankar Debnath, Sanjoy Mitra, Joyjit Dhar · 5 authors
No abstract is available for this record.
P. H. V. Sesha Talpa Sai, Ganesh Acharya, S Pramod, Srinivasan Ganesan · 8 authors
No abstract is available for this record.
Amrutanshu Panigrahi, Abhilash Pati, Santosh Reddy Addula, Ashis Pati · 6 authors
No abstract is available for this record.
Elnaz Radmand, Jamshid Pirgazi, Ali Ghanbari Sorkhi
In the digital currency market, including Bitcoin, price prediction using artificial intelligence (AI) and machine learning (ML) is critical but challenging. Conventional methods such as technical analysis (based on historical market data) and fundamental analysis (based on economic variables) suffer from data noise, processing delays, and insufficient data. To make predictions more accurate, faster, and able to handle more data, the suggested method combines several steps: extracting important information, labeling it, choosing the best features, merging different models, and fine‐tuning the model settings. Based on the price data, this approach initially generates 5 labels with a new labeling method based on the percentage of average price changes in several days and generates signals (hold, buy, sell, strong sell, and strong buy). Thereafter, it extracts 768 features from technical studies using the TA‐Lib library and from an authoritative site. The TLBOA algorithm, which does not get stuck in the local optimum with two updates, was used to select and reduce features to 15 to avoid overfitting. A variety of ML models, including support vector machine and Naive Bayes, use these selected features for training. By using the evolutionary DE algorithm to optimize the XGBoost meta‐parameters, we increased the accuracy by 1%–4%. The proposed strategy has performed better than other models, such as XGBoost with 85.66% and gradient boosting with 84.15%, and has achieved an accuracy of 91%–92%.
Mingdong Tang, Xingyu Feng, Weili Chen
No abstract is available for this record.
Mustafa Yalçın
No abstract is available for this record.
Kayla Ramdass, Maria Chano, Mohamed Rahouti, Thaier Hayajneh
No abstract is available for this record.
Ch. V. Raghavendran, K. Chandra Mouli, Manu Hajari, A. Anil Kumar Reddy · 6 authors
Predictive modeling has emerged as a key focus for cryptocurrency market asset valuation due to its complex nature and high market volatility. The research looks into Ethereum price forecasting with the methods of autoregressive integrated moving average (ARIMA) and Facebook Prophet model and long short‐term memory (LSTM) networks. These models operate on historical Ethereum prices and show their efficiency regarding temporal pattern recognition and prediction accuracy. The ARIMA model helps reveal trends as well as seasonal patterns and irregularities within Ethereum price fluctuations. The Facebook Prophet model serves as a forecasting tool because it automatically handles peculiarities present within cryptocurrency price data. Time series forecasting with LSTMs becomes an advanced technique used to detect intricate patterns along with sustained dependency relationships between data points. The systematic process of preparing data and constructing models and assessing results enables proper utilization of LSTMs for predicting time series data with accuracy. Ethereum price datasets are applied to train the models which undergo performance evaluation using MPE alongside MAPE and RMSE along with MAE to reveal strengths and weaknesses during Ethereum price predictions. The evaluation shows that ARIMA and Facebook Prophet together with LSTM demonstrate success in modeling Ethereum price fluctuations. This research explores the effectiveness of time series forecasting methods for cryptocurrency price prediction yielding vital knowledge about reliable tools for financial market trend modeling. Current research findings will provide knowledge to investors and risk management professionals making decisions within the volatile digital asset space.
T. Vairam, M Srijeimathy
Incorporating blockchain technology into vehicle classification systems shows potential progress in data security, transparency, and decentralization. This work examines how blockchain technology can improve vehicle classification procedures, emphasizing the advantages and hurdles of various consensus mechanisms. Conventional methods of categorizing vehicles typically depend on centralized databases susceptible to data tampering and breaches. Using blockchain ensures data integrity by utilizing decentralized and immutable ledgers. We assess different consensus algorithms such as Proof of Stake (PoS), Delegated Proof of Stake (DPoS), Practical Byzantine Fault Tolerance (PBFT), Federated Byzantine Agreement (FBA), and DAG (Directed Acyclic Graph) to determine their appropriateness for vehicle categorization. Our aim is to determine the most effective and secure method for incorporating blockchain technology into vehicle classification systems by analyzing these consensus mechanisms.
Marco Corazza, Giovanni Fasano
No abstract is available for this record.
Tanju Açi, Hakan Kekül
In the financial sector, as past economic and social events have shaken trust, this trust is being regained through the internet and computer technologies. Emerging in the 19th century, financial technology has led to a new economic understanding with digital money and especially bitcoin. The decentralized structure of bitcoin and the encryption systems used for security play an important role in preventing fraud and have become the center of attention of investors. As its value has increased, studies on price predictions have naturally increased. This study aims to predict the impact of data obtained from digital economy news sites on bitcoin price using natural language processing and machine learning techniques. In line with this goal, text vectorization was performed with the TF-IDF statistical method. Synthetic Minority Oversampling Technique (SMOTE) was applied to eliminate the imbalance in the vectorized data set. Classification models such as Logistic Regression, Decision Trees, Random Forest, Support Vector Machines, K-Nearest Neighbor, Extra Trees, Bernoulli Naive Bayes and Multilayer Perceptron were applied to the obtained output.According to the results of the performance of different machine learning models in predicting the direction of bitcoin price fluctuation, the Extra Trees Classifier model showed the highest performance with an Accuracy of 86.71%, recall of 86.71%, precision of 86.99% and F1 score of 86.59%.
Coşkun Parim, Tuğba Güz, Erhan Çene
No abstract is available for this record.
Wenjing Wang, Ziwu Jiang, Xiangyu Zhang, Chengyue Hu · 5 authors
Bitcoin is the world's first decentralized cryptocurrency, using blockchain technology to secure and verify transactions. A hybrid model based on stochastic configuration network (SCN) with conformal prediction is proposed in this study. Initially, an SCN model is built, and predictions are generated by the model. Subsequently, the predicted values from SCN are fed into the conformal prediction model, resulting in the generation of confidence intervals that validate the reliability of these values. Finally, the dataset of historical Bitcoin prices sourced from Wikipedia have been utilized. The results indicate that the SCN-conformal prediction combination enhances prediction reliability.
Navid Parvini, Davood Ahmadian, Luca Vincenzo Ballestra
No abstract is available for this record.