With the development of social media, such as Twitter and MicroBlog, sentiment analysis becomes a quite useful tool for mining the subjective emotion information from the text, which may have important applications in...
详细信息
The development of information technology (IT) infrastructure currently has a vast influence on the economic growth of cities and districts in Indonesia, especially rural areas. This has resulted in government organiz...
详细信息
This paper presents a processed phytoplankton dataset from Lake Zerendi and other Kokshetau Upland lakes, Kazakhstan. The study aimed to develop an automated method for controlling the distribution of phytoplankton in...
详细信息
The tremors caused by the COVID-19 epidemic have ushered in a new era of healthcare problems for people all across the globe. Significant problems and challenges have been observed in areas such as victim assistance, ...
详细信息
Money laundering is a financial crime that obscures the origin of illicit funds, necessitating the development and enforcement of anti-money laundering (AML) policies by governments and organizations. The proliferatio...
详细信息
Money laundering is a financial crime that obscures the origin of illicit funds, necessitating the development and enforcement of anti-money laundering (AML) policies by governments and organizations. The proliferation of mobile payment platforms and smart IoT devices has significantly complicated AML investigations. As payment networks become more interconnected, there is an increasing need for efficient real-time detection to process large volumes of transaction data on heterogeneous payment systems by different operators such as digital currencies, cryptocurrencies and account-based payments. Most of these mobile payment networks are supported by connected devices, many of which are considered loT devices in the FinTech space that constantly generate data. Furthermore, the growing complexity and unpredictability of transaction patterns across these networks contribute to a higher incidence of false positives. While machine learning solutions have the potential to enhance detection efficiency, their application in AML faces unique challenges, such as addressing privacy concerns tied to sensitive financial data and managing the real-world constraint of limited data availability due to data regulations. Existing surveys in the AML literature broadly review machine learning approaches for money laundering detection, but they often lack an in-depth exploration of advanced deep learning techniques—an emerging field with significant potential. To address this gap, this paper conducts a comprehensive review of deep learning solutions and the challenges associated with their use in AML. Additionally, we propose a novel framework that applies the least-privilege principle by integrating machine learning techniques, codifying AML red flags, and employing account profiling to provide context for predictions and enable effective fraud detection under limited data availability. Specifically, our approach defines AML-relevant financial profile characteristics and risk indicator
The target of this research is reducing the size of a dataset which is usually needed before starting the data analysis in scientific research, this can be done by removing attributes that do not affect the accuracy o...
详细信息
information security remains one of the major challenges faced by organizations and individuals in the current technological era. With the growing popularity of smart devices, the frequency of cyber-attacks targeted a...
详细信息
ISBN:
(数字)9798331542573
ISBN:
(纸本)9798331542580
information security remains one of the major challenges faced by organizations and individuals in the current technological era. With the growing popularity of smart devices, the frequency of cyber-attacks targeted at these devices are increasing at a rapid pace. Among these types of attacks on informationsystems is malicious software also known as malware. A cyber-attack based on malware has the potential to cause loss or manipulation of confidential information. Currently, malware creators who use polymorphic techniques to evade detection can bypass traditional signature-based methods of detecting malware. Thus, various researchers have turned to Machine Learning (ML) techniques to enhance the detection of malware. However, despite the promising results, ML-based classification of malware remains a challenging task that the research community is yet to solve. Therefore, in this study, we propose an ML-based malware detection system that incorporates the AdaBoost, Random Forest, Decision Tree, and K-Nearest Neighbors Algorithm (KNN) algorithms into an ensemble stack with gradient boost as the ensemble boost for the stack meta estimator. We also use Extra Trees Classifier for feature selection. Multiple performance measures are utilized to assess the system's performance while using the Kaggle Malware Dataset. Our findings demonstrate that the proposed method successfully detects malware samples with remarkable accuracy, precision, recall, F1, and area under the curve (AUC) scores of 99.4%, 99.3%, 99.4%, 99.4%, and 99.6%, respectively.
Urban congestion has been a known problem since the first urban revolution throughout the world. Today's major metropolises are synonymous with traffic congestion and complicated urban circulation. This paper intr...
详细信息
World Wide Web enables its users to connect among themselves through social networks,forums,review sites,and blogs and these interactions produce huge volumes of data in various forms such as emotions,sentiments,views...
详细信息
World Wide Web enables its users to connect among themselves through social networks,forums,review sites,and blogs and these interactions produce huge volumes of data in various forms such as emotions,sentiments,views,*** Analysis(SA)is a text organization approach that is applied to categorize the sentiments under distinct classes such as positive,negative,and ***,Sentiment Analysis is challenging to perform due to inadequate volume of labeled data in the domain of Natural Language Processing(NLP).Social networks produce interconnected and huge data which brings complexity in terms of expanding SA to an extensive array of ***,there is a need exists to develop a proper technique for both identification and classification of sentiments in social *** get rid of these problems,Deep Learning methods and sentiment analysis are consolidated since the former is highly efficient owing to its automatic learning *** current study introduces a Seeker Optimization Algorithm with Deep Learning enabled SA and Classification(SOADL-SAC)for social *** presented SOADL-SAC model involves the proper identification and classification of sentiments in social *** order to attain this,SOADL-SAC model carries out data preprocessing to clean the input *** addition,Glove technique is applied to generate the feature ***,Self-Head Multi-Attention based Gated Recurrent Unit(SHMA-GRU)model is exploited to recognize and classify the ***,Seeker Optimization Algorithm(SOA)is applied to fine-tune the hyperparameters involved in SHMA-GRU model which in turn enhances the classifier *** order to validate the enhanced outcomes of the proposed SOADL-SAC model,various experiments were conducted on benchmark *** experimental results inferred the better performance of SOADLSAC model over recent state-of-the-art approaches.
Abstract— Scientific research is all the actions undertaken to produce and develop scientific knowledge. The representation of this knowledge can take various forms: it can be publications, reports, patents, etc. Thi...
ISBN:
(纸本)9798400709036
Abstract— Scientific research is all the actions undertaken to produce and develop scientific knowledge. The representation of this knowledge can take various forms: it can be publications, reports, patents, etc. This knowledge can be incorporated into scientific social networks and applications. The scientific social networks in their current form depend on the title, the keywords, and the ontology to compare and link the relationship between different scientific research; there may be different scientific research with the same keywords; the same scientific research may have different titles. In addition, scientific research may be written in different languages, but current scientific social networks do not take into account the multiple languages of researchers and research. they cannot link the relationship and compare scientific research written in different languages. To solve these problems, this paper proposes the use of a multi-lingual ontology to determine and describe scientific research. This work uses ontology in the context of a conceptual indexation, by separating the concept from the term, thus, the result of this work according to the proposed approach will make it possible to express the concept (knowledge extra-linguistic), in different languages, this will allow comparing and linking the relationship between scientific research written in different languages and measuring the percentage of similarity and difference between them. Dealing with multilingualism in scientific research is a very important contribution to this field.
暂无评论