Big Data and Cognitive Computing最新文献

{"title":"Toward Morphologic Atlasing of the Human Whole Brain at the Nanoscale","authors":"W. Nowinski","doi":"10.3390/bdcc7040179","DOIUrl":"https://doi.org/10.3390/bdcc7040179","url":null,"abstract":"Although no dataset at the nanoscale for the entire human brain has yet been acquired and neither a nanoscale human whole brain atlas has been constructed, tremendous progress in neuroimaging and high-performance computing makes them feasible in the non-distant future. To construct the human whole brain nanoscale atlas, there are several challenges, and here, we address two, i.e., the morphology modeling of the brain at the nanoscale and designing of a nanoscale brain atlas. A new nanoscale neuronal format is introduced to describe data necessary and sufficient to model the entire human brain at the nanoscale, enabling calculations of the synaptome and connectome. The design of the nanoscale brain atlas covers design principles, content, architecture, navigation, functionality, and user interface. Three novel design principles are introduced supporting navigation, exploration, and calculations, namely, a gross neuroanatomy-guided navigation of micro/nanoscale neuroanatomy; a movable and zoomable sampling volume of interest for navigation and exploration; and a nanoscale data processing in a parallel-pipeline mode exploiting parallelism resulting from the decomposition of gross neuroanatomy parcellated into structures and regions as well as nano neuroanatomy decomposed into neurons and synapses, enabling the distributed construction and continual enhancement of the nanoscale atlas. Numerous applications of this atlas can be contemplated ranging from proofreading and continual multi-site extension to exploration, morphometric and network-related analyses, and knowledge discovery. To my best knowledge, this is the first proposed neuronal morphology nanoscale model and the first attempt to design a human whole brain atlas at the nanoscale.","PeriodicalId":36397,"journal":{"name":"Big Data and Cognitive Computing","volume":" May","pages":""},"PeriodicalIF":3.7,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138610890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Managing Cybersecurity Threats and Increasing Organizational Resilience","authors":"Peter R. J. Trim, Yang-Im Lee","doi":"10.3390/bdcc7040177","DOIUrl":"https://doi.org/10.3390/bdcc7040177","url":null,"abstract":"Cyber security is high up on the agenda of senior managers in private and public sector organizations and is likely to remain so for the foreseeable future. [...]","PeriodicalId":36397,"journal":{"name":"Big Data and Cognitive Computing","volume":"87 ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139250668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A New Approach to Data Analysis Using Machine Learning for Cybersecurity","authors":"Shivashankar Hiremath, Eeshan Shetty, A. J. Prakash, S. Sahoo, Kiran Kumar Patro, Kandala N. V. P. S. Rajesh, Paweł Pławiak","doi":"10.3390/bdcc7040176","DOIUrl":"https://doi.org/10.3390/bdcc7040176","url":null,"abstract":"The internet has become an indispensable tool for organizations, permeating every facet of their operations. Virtually all companies leverage Internet services for diverse purposes, including the digital storage of data in databases and cloud platforms. Furthermore, the rising demand for software and applications has led to a widespread shift toward computer-based activities within the corporate landscape. However, this digital transformation has exposed the information technology (IT) infrastructures of these organizations to a heightened risk of cyber-attacks, endangering sensitive data. Consequently, organizations must identify and address vulnerabilities within their systems, with a primary focus on scrutinizing customer-facing websites and applications. This work aims to tackle this pressing issue by employing data analysis tools, such as Power BI, to assess vulnerabilities within a client’s application or website. Through a rigorous analysis of data, valuable insights and information will be provided, which are necessary to formulate effective remedial measures against potential attacks. Ultimately, the central goal of this research is to demonstrate that clients can establish a secure environment, shielding their digital assets from potential attackers.","PeriodicalId":36397,"journal":{"name":"Big Data and Cognitive Computing","volume":"1 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139253609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Empowering Propaganda Detection in Resource-Restraint Languages: A Transformer-Based Framework for Classifying Hindi News Articles","authors":"Deptii D. Chaudhari, Ambika Vishal Pawar","doi":"10.3390/bdcc7040175","DOIUrl":"https://doi.org/10.3390/bdcc7040175","url":null,"abstract":"Misinformation, fake news, and various propaganda techniques are increasingly used in digital media. It becomes challenging to uncover propaganda as it works with the systematic goal of influencing other individuals for the determined ends. While significant research has been reported on propaganda identification and classification in resource-rich languages such as English, much less effort has been made in resource-deprived languages like Hindi. The spread of propaganda in the Hindi news media has induced our attempt to devise an approach for the propaganda categorization of Hindi news articles. The unavailability of the necessary language tools makes propaganda classification in Hindi more challenging. This study proposes the effective use of deep learning and transformer-based approaches for Hindi computational propaganda classification. To address the lack of pretrained word embeddings in Hindi, Hindi Word2vec embeddings were created using the H-Prop-News corpus for feature extraction. Subsequently, three deep learning models, i.e., CNN (convolutional neural network), LSTM (long short-term memory), Bi-LSTM (bidirectional long short-term memory); and four transformer-based models, i.e., multi-lingual BERT, Distil-BERT, Hindi-BERT, and Hindi-TPU-Electra, were experimented with. The experimental outcomes indicate that the multi-lingual BERT and Hindi-BERT models provide the best performance, with the highest F1 score of 84% on the test data. These results strongly support the efficacy of the proposed solution and indicate its appropriateness for propaganda classification.","PeriodicalId":36397,"journal":{"name":"Big Data and Cognitive Computing","volume":"27 2","pages":""},"PeriodicalIF":3.7,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139274664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimization of Cryptocurrency Algorithmic Trading Strategies Using the Decomposition Approach","authors":"Sherin M. Omran, Wessam H. El-Behaidy, A. Youssif","doi":"10.3390/bdcc7040174","DOIUrl":"https://doi.org/10.3390/bdcc7040174","url":null,"abstract":"A cryptocurrency is a non-centralized form of money that facilitates financial transactions using cryptographic processes. It can be thought of as a virtual currency or a payment mechanism for sending and receiving money online. Cryptocurrencies have gained wide market acceptance and rapid development during the past few years. Due to the volatile nature of the crypto-market, cryptocurrency trading involves a high level of risk. In this paper, a new normalized decomposition-based, multi-objective particle swarm optimization (N-MOPSO/D) algorithm is presented for cryptocurrency algorithmic trading. The aim of this algorithm is to help traders find the best Litecoin trading strategies that improve their outcomes. The proposed algorithm is used to manage the trade-offs among three objectives: the return on investment, the Sortino ratio, and the number of trades. A hybrid weight assignment mechanism has also been proposed. It was compared against the trading rules with their standard parameters, MOPSO/D, using normalized weighted Tchebycheff scalarization, and MOEA/D. The proposed algorithm could outperform the counterpart algorithms for benchmark and real-world problems. Results showed that the proposed algorithm is very promising and stable under different market conditions. It could maintain the best returns and risk during both training and testing with a moderate number of trades.","PeriodicalId":36397,"journal":{"name":"Big Data and Cognitive Computing","volume":"17 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139276436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Semantic Adjacency Criterion in Time Intervals Mining","authors":"Alexander Shknevsky, Yuval Shahar, Robert Moskovitch","doi":"10.3390/bdcc7040173","DOIUrl":"https://doi.org/10.3390/bdcc7040173","url":null,"abstract":"We propose a new pruning constraint when mining frequent temporal patterns to be used as classification and prediction features, the Semantic Adjacency Criterion [SAC], which filters out temporal patterns that contain potentially semantically contradictory components, exploiting each medical domain’s knowledge. We have defined three SAC versions and tested them within three medical domains (oncology, hepatitis, diabetes) and a frequent-temporal-pattern discovery framework. Previously, we had shown that using SAC enhances the repeatability of discovering the same temporal patterns in similar proportions in different patient groups within the same clinical domain. Here, we focused on SAC’s computational implications for pattern discovery, and for classification and prediction, using the discovered patterns as features, by four different machine-learning methods: Random Forests, Naïve Bayes, SVM, and Logistic Regression. Using SAC resulted in a significant reduction, across all medical domains and classification methods, of up to 97% in the number of discovered temporal patterns, and in the runtime of the discovery process, of up to 98%. Nevertheless, the highly reduced set of only semantically transparent patterns, when used as features, resulted in classification and prediction models whose performance was at least as good as the models resulting from using the complete temporal-pattern set.","PeriodicalId":36397,"journal":{"name":"Big Data and Cognitive Computing","volume":" 94","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135191533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of Short-Term Rockburst Risk Severity Using Machine Learning Methods","authors":"Aibing Jin, Prabhat Basnet, Shakil Mahtab","doi":"10.3390/bdcc7040172","DOIUrl":"https://doi.org/10.3390/bdcc7040172","url":null,"abstract":"In deep engineering, rockburst hazards frequently result in injuries, fatalities, and the destruction of contiguous structures. Due to the complex nature of rockbursts, predicting the severity of rockburst damage (intensity) without the aid of computer models is challenging. Although there are various predictive models in existence, effectively identifying the risk severity in imbalanced data remains crucial. The ensemble boosting method is often better suited to dealing with unequally distributed classes than are classical models. Therefore, this paper employs the ensemble categorical gradient boosting (CGB) method to predict short-term rockburst risk severity. After data collection, principal component analysis (PCA) was employed to avoid the redundancies caused by multi-collinearity. Afterwards, the CGB was trained on PCA data, optimal hyper-parameters were retrieved using the grid-search technique to predict the test samples, and performance was evaluated using precision, recall, and F1 score metrics. The results showed that the PCA-CGB model achieved better results in prediction than did the single CGB model or conventional boosting methods. The model achieved an F1 score of 0.8952, indicating that the proposed model is robust in predicting damage severity given an imbalanced dataset. This work provides practical guidance in risk management.","PeriodicalId":36397,"journal":{"name":"Big Data and Cognitive Computing","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135433118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Social Trend Mining: Lead or Lag","authors":"Hossein Hassani, Nadejda Komendantova, Elena Rovenskaya, Mohammad Reza Yeganegi","doi":"10.3390/bdcc7040171","DOIUrl":"https://doi.org/10.3390/bdcc7040171","url":null,"abstract":"This research underscores the profound implications of Social Intelligence Mining, notably employing open access data and Google Search engine data for trend discernment. Utilizing advanced analytical methodologies, including wavelet coherence analysis and phase difference, hidden relationships and patterns within social data were revealed. These techniques furnish an enriched comprehension of social phenomena dynamics, bolstering decision-making processes. The study’s versatility extends across myriad domains, offering insights into public sentiment and the foresight for strategic approaches. The findings suggest immense potential in Social Intelligence Mining to influence strategies, foster innovation, and add value across diverse sectors.","PeriodicalId":36397,"journal":{"name":"Big Data and Cognitive Computing","volume":"2 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135433108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Arabic Toxic Tweet Classification: Leveraging the AraBERT Model","authors":"Amr Mohamed El Koshiry, Entesar Hamed I. Eliwa, Tarek Abd El-Hafeez, Ahmed Omar","doi":"10.3390/bdcc7040170","DOIUrl":"https://doi.org/10.3390/bdcc7040170","url":null,"abstract":"Social media platforms have become the primary means of communication and information sharing, facilitating interactive exchanges among users. Unfortunately, these platforms also witness the dissemination of inappropriate and toxic content, including hate speech and insults. While significant efforts have been made to classify toxic content in the English language, the same level of attention has not been given to Arabic texts. This study addresses this gap by constructing a standardized Arabic dataset specifically designed for toxic tweet classification. The dataset is annotated automatically using Google’s Perspective API and the expertise of three native Arabic speakers and linguists. To evaluate the performance of different models, we conduct a series of experiments using seven models: long short-term memory (LSTM), bidirectional LSTM, a convolutional neural network, a gated recurrent unit (GRU), bidirectional GRU, multilingual bidirectional encoder representations from transformers, and AraBERT. Additionally, we employ word embedding techniques. Our experimental findings demonstrate that the fine-tuned AraBERT model surpasses the performance of other models, achieving an impressive accuracy of 0.9960. Notably, this accuracy value outperforms similar approaches reported in recent literature. This study represents a significant advancement in Arabic toxic tweet classification, shedding light on the importance of addressing toxicity in social media platforms while considering diverse languages and cultures.","PeriodicalId":36397,"journal":{"name":"Big Data and Cognitive Computing","volume":"105 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134907884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment of Security KPIs for 5G Network Slices for Special Groups of Subscribers","authors":"Roman Odarchenko, Maksim Iavich, Giorgi Iashvili, Solomiia Fedushko, Yuriy Syerov","doi":"10.3390/bdcc7040169","DOIUrl":"https://doi.org/10.3390/bdcc7040169","url":null,"abstract":"It is clear that 5G networks have already become integral to our present. However, a significant issue lies in the fact that current 5G communication systems are incapable of fully ensuring the required quality of service and the security of transmitted data, especially in government networks that operate in the context of the Internet of Things, hostilities, hybrid warfare, and cyberwarfare. The use of 5G extends to critical infrastructure operators and special users such as law enforcement, governments, and the military. Adapting modern cellular networks to meet the specific needs of these special users is not only feasible but also necessary. In doing so, these networks must meet additional stringent requirements for reliability, performance, and, most importantly, data security. This scientific paper is dedicated to addressing the challenges associated with ensuring cybersecurity in this context. To effectively improve or ensure a sufficient level of cybersecurity, it is essential to measure the primary indicators of the effectiveness of the security system. At the moment, there are no comprehensive lists of these key indicators that require priority monitoring. Therefore, this article first analyzed the existing similar indicators and presented a list of them, which will make it possible to continuously monitor the state of cybersecurity systems of 5G cellular networks with the aim of using them for groups of special users. Based on this list of cybersecurity KPIs, as a result, this article presents a model to identify and evaluate these indicators. To develop this model, we comprehensively analyzed potential groups of performance indicators, selected the most relevant ones, and introduced a mathematical framework for their quantitative assessment. Furthermore, as part of our research efforts, we proposed enhancements to the core of the 4G/5G network. These enhancements enable data collection and statistical analysis through specialized sensors and existing servers, contributing to improved cybersecurity within these networks. Thus, the approach proposed in the article opens up an opportunity for continuous monitoring and, accordingly, improving the performance indicators of cybersecurity systems, which in turn makes it possible to use them for the maintenance of critical infrastructure and other users whose service presents increased requirements for cybersecurity systems.","PeriodicalId":36397,"journal":{"name":"Big Data and Cognitive Computing","volume":"42 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136381149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}