Bytecode-based approach for Ethereum smart contract classification by Dan LIN, Kaixin LIN, Jiajing WU, Zibin ZHENG

“…In recent years, blockchain technology has been widely used and concerned in many fields, including finance, medical care and government affairs.However, due to the immutability of smart contracts and the particularity of the operating environment, various security issues occur frequently.On the one hand, the code security problems of contract developers when writing contracts, on the other hand, there are many high-risk smart contracts in Ethereum, and ordinary users are easily attracted by the high returns provided by high-risk contracts, but they have no way to know the risks of the contracts.However, the research on smart contract security mainly focuses on code security, and there is relatively little research on the identification of contract functions.If the smart contract function can be accurately classified, it will help people better understand the behavior of smart contracts, while ensuring the ecological security of smart contracts and reducing or recovering user losses.Existing smart contract classification methods often rely on the analysis of the source code of smart contracts, but contracts released on Ethereum only mandate the deployment of bytecode, and only a very small number of contracts publish their source code.Therefore, an Ethereum smart contract classification method based on bytecode was proposed.Collect the Ethereum smart contract bytecode and the corresponding category label, and then extract the opcode frequency characteristics and control flow graph characteristics.The characteristic importance is analyzed experimentally to obtain the appropriate graph vector dimension and optimal classification model, and finally the multi-classification task of smart contract in five categories of exchange, finance, gambling, game and high risk is experimentally verified, and the F1 score of the XGBoost classifier reaches 0.913 8.Experimental results show that the algorithm can better complete the classification task of Ethereum smart contracts, and can be applied to the prediction of smart contract categories in reality.…”
Get full text

Rock image classification based on improved EfficientNet by Kai Bai, Zhaoshuo Zhang, Siyi Jin, Shengsheng Dai

“…To address these issues, we propose an enhanced rock classification model based on EfficientNet-B0. …”
Get full text

Genre Classification and the Current State of Turkmen Musical Folklore by Джаміля Курбанова

“… The aim of the article is to characterise the existing classification systems of Turkmen musical folklore, as well as to outline new ways of studying folklore genres to determine their typological characteristics. …”
Get full text

Flood Image Classification using Convolutional Neural Networks by Olusogo Julius Adetunji, Ibrahim Adepoju Adeyanju, Adebimpe Omolayo Esan, Adedayo Aladejobi Sobowale Sobowale

“…The model leverage on the unique features of region of Interest aligns to resolve the issues of misalignments caused by the use of region of Interest pooling engaged in the traditional Faster-RCNN. …”
Get full text

PSORIATIC ARTHRITIS: CLASSIFICATION, CLINICAL PRESENTATION, DIAGNOSIS, TREATMENT by T. V. Korotaeva

“…It describes the epidemiology of the disease and considers current ideas on its pathogenesis and factors influencing the development of PsA in psoriatic patients. The classification and clinical forms of PsA are presented. …”
Get full text

ICRSSD: Identification and Classification for Railway Structured Sensitive Data by Yage Jin, Hongming Chen, Rui Ma, Yanhua Wu, Qingxin Li

“…However, existing methods for identifying and classifying often suffer from limitations such as overly coarse identification granularity and insufficient flexibility in classification. To address these issues, we propose ICRSSD, a two-stage method for identification and classification in terms of the railway domain. …”
Get full text

Pneumocentrifugal Classification of Dispersed Particles during Grain Milling by Olga N. Terekhova, Yana S. Duyunova

Get full text

Chaos-enhanced metaheuristics: classification, comparison, and convergence analysis by Abdelhadi Limane, Farouq Zitouni, Saad Harous, Rihab Lakbichi, Aridj Ferhat, Abdulaziz S. Almazyad, Pradeep Jangir, Ali Wagdy Mohamed

“…To respond to this challenge, this paper comprehensively analyzes recent advances in CMAs from 2013 to 2024, proposing a novel classification scheme that systematically organizes prevalent and practical approaches for integrating chaos theory into metaheuristic algorithms based on their strategic roles. …”
Get full text

Adaptive pixel attention network for hyperspectral image classification by Yuefeng Zhao, Chengmin Zai, Nannan Hu, Lu Shi, Xue Zhou, Jingqi Sun

“…To tackle the above issues, we propose a novel Adaptive Pixel Attention Network, which can improve HSI classification by further mining the connections between pixels in patch features. …”
Get full text

Deep learning approach for automated hMPV classification by Sivarama Prasad Tera, Ravikumar Chinthaginjala, Irum Shahzadi, Priya Natha, Safia Obaidur Rab

“…To mitigate this, the framework incorporates advanced techniques such as data augmentation, weighted loss functions, and dropout regularization, which help to balance the dataset, improve model robustness, and enhance classification accuracy. These techniques are crucial in addressing issues such as overfitting and generalization, which are common when working with limited datasets in medical imaging tasks. …”
Get full text

Pyoderma in dogs (classification, pathogenesis, main pathogens, treatment) by V. M. Bubnov, Ya. V. Kisera, Yu. V. Martyniv

Get full text

Fractures around Trochanteric Nails: The “Vergilius Classification System” by Giuseppe Toro, Antimo Moretti, Daniele Ambrosio, Raffaele Pezzella, Annalisa De Cicco, Giovanni Landi, Nicola Tammaro, Pasquale Florio, Antonio Benedetto Cecere, Adriano Braile, Antonio Medici, Antonio Siano, Bruno Di Maggio, Giampiero Calabrò, Nicola Gagliardo, Ciro Di Fino, Gaetano Bruno, Achille Pellegrino, Giacomo Negri, Vincenzo Monaco, Michele Gison, Antonio Toro, Alfredo Schiavone Panni, Umberto Tarantino, Giovanni Iolascon

“…Subsequently, a panel of experts was involved in a consensus meeting proposing a specific classification system and formulated treatment statements for PNFs. …”
Get full text

New opportunities of the domestic universal classifications (LBC case) by N. Yu. Sokolova

“…The article deals with the problem of further development of the library-bibliographic classification (LBC), in particular, the issue of methodological development of Department 1 «General scientific and interdisciplinary knowledge». …”
Get full text

CLASSIFICATION OF TASKS FOR GROUP ROBOTICS FOR METHODS LABOR DIVISION by V. I. Petrenko, F. B. Tebueva, V. O. Antonov

“…In the process of studying the literature on this issue, a pool of tasks obtained that solve certain methods. …”
Get full text

Segmentation Techniques Applied to CNNs for Cervical Cancer Classification by Ana Ortiz-Gonzalez, Raquel Martinez-Espana, Juan Morales-Garcia, Baldomero Imbernon, Jose Martinez-Mas, Mauricio A. Alvarez, Oscar David Romero, Juan Pedro Martinez-Cendan, Andres Bueno-Crespo

“…Cervical cancer continues to be a significant global health issue, ranking as the fourth most prevalent cancer affecting women. …”
Get full text

Adaptive feature interaction enhancement network for text classification by Rui Su, Shangbing Gao, Kefan Zhao, Junqiang Zhang

“…Abstract Text classification aims to establish text distinctions, which face difficulty in capturing global text semantics and local details. …”
Get full text

ENSEMBLE CNN WITH ADASYN FOR MULTICLASS CLASSIFICATION ON CABBAGE PESTS by Nabila Ayunda Sovia, Ni Wayan Surya Wardhani

“…Image classification is a complex process influenced by various factors, one of which is the amount of image data. …”
Get full text

Classification of User Behavior Patterns for Indoor Navigation Problem by Aleksandra Borsuk, Andrzej Chybicki, Michał Zieliński

“…The developed model achieved 75% accuracy for individual activity type classification within one-second time windows, and 98.6% for full-sequence classification through majority voting. …”
Get full text

Text classification model based on GNN and attention mechanism by ZENG Shuifei, MENG Yao, LIU Jing

“…Addressing the issue of low classification accuracy raised by the poor performance of the model, which is caused by the difficulty in learning from dynamic aggregation unknown neighboring nodes of graph data and insufficient fusion of semantic features, a model named graph attention text classification(GATC) based on graph neural network (GNN) and attention mechanism was proposed. …”
Get full text

Comparative Study of Deep Learning-Based Sentiment Classification by Seungwan Seo, Czangyeob Kim, Haedong Kim, Kyounghyun Mo, Pilsung Kang

“…However, one practical issue occurring in deep-learning-based sentiment classification is that the best model structure depends on the characteristics of the dataset on which the deep learning model is trained; moreover, it is manually determined based on the domain knowledge of an expert or selected from a grid search of possible candidates. …”
Get full text