4,200 research outputs found

    Unveiling the frontiers of deep learning: innovations shaping diverse domains

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    Deep learning (DL) enables the development of computer models that are capable of learning, visualizing, optimizing, refining, and predicting data. In recent years, DL has been applied in a range of fields, including audio-visual data processing, agriculture, transportation prediction, natural language, biomedicine, disaster management, bioinformatics, drug design, genomics, face recognition, and ecology. To explore the current state of deep learning, it is necessary to investigate the latest developments and applications of deep learning in these disciplines. However, the literature is lacking in exploring the applications of deep learning in all potential sectors. This paper thus extensively investigates the potential applications of deep learning across all major fields of study as well as the associated benefits and challenges. As evidenced in the literature, DL exhibits accuracy in prediction and analysis, makes it a powerful computational tool, and has the ability to articulate itself and optimize, making it effective in processing data with no prior training. Given its independence from training data, deep learning necessitates massive amounts of data for effective analysis and processing, much like data volume. To handle the challenge of compiling huge amounts of medical, scientific, healthcare, and environmental data for use in deep learning, gated architectures like LSTMs and GRUs can be utilized. For multimodal learning, shared neurons in the neural network for all activities and specialized neurons for particular tasks are necessary.Comment: 64 pages, 3 figures, 3 table

    Towards an aligned South African National Cybersecurity Policy Framework

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    This thesis measured and aligned factors that contribute to the misalignment of the South African National Cybersecurity Policy Framework (SA-NCPF). The exponential growth rate of cyber-attacks and threats has caused more headaches for cybersecurity experts, law enforcement agents, organisations and the global business economy. The emergence of the global Corona Virus Disease-2019 has also contributed to the growth of cyber-attacks and threats thus, requiring concerted efforts from everyone in society to devise appropriate interventions that mitigate unacceptable user behaviour in the reality of cyberspace. In this study, various theories were identified and pooled together into an integrative theoretical framework to provide a better understanding of various aspects of the law-making process more comprehensively. The study identified nine influencing factors that contributed to misalignment of the South African National Cybersecurity Policy Framework. These influencing factors interact with each other continuously producing complex relationships, therefore, it is difficult to measure the degree of influence of each factor, hence the need to look at and measure the relationships as Gestalts. Gestalts view individual interactions between pairs of constructs only as a part of the overall pattern. Therefore, the integrative theoretical framework and Gestalts approach were used to develop a conceptual framework to measure the degree of alignment of influencing factors. This study proposed that the stronger the coherence among the influencing factors, the more aligned the South African National Security Policy Framework. The more coherent the SA-NCPF is perceived, the greater would be the degree of alignment of the country's cybersecurity framework to national, regional and global cyberlaws. Respondents that perceived a strong coherence among the elements also perceived an effective SA-NCPF. Empirically, this proposition was tested using nine constructs. Quantitative data was gathered from respondents using a survey. A major contribution of this study was that it was the first attempt in South Africa to measure the alignment of the SA-NCPF using the Gestalts approach as an effective approach for measuring complex relationships. The study developed the integrative theoretical framework which integrates various theories that helped to understand and explain the South African law making process. The study also made a significant methodological contribution by adopting the Cluster-based perspective to distinguish, describe and predict the degree of alignment of the SA-NCPF. There is a dearth of information that suggests that past studies have adopted or attempted to address the challenge of alignment of the SA-NCPF using the cluster-based and Gestalts perspectives. Practical implications from the study include a review of the law-making process, skills development strategy, a paradigm shift to address the global Covid-19 pandemic and sophisticated cybercrimes simultaneously. The study asserted the importance of establishing an independent cybersecurity board comprising courts, legal, cybersecurity experts, academics and law-makers to provide cybersecurity expertise and advice. From the research findings, government and practitioners can draw lessons to review the NCPF to ensure the country develops an effective national cybersecurity strategy. Limitations and recommendations for future research conclude the discussions of this study

    Visual Anomaly Detection in Event Sequence Data

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    Anomaly detection is a common analytical task that aims to identify rare cases that differ from the typical cases that make up the majority of a dataset. When applied to the analysis of event sequence data, the task of anomaly detection can be complex because the sequential and temporal nature of such data results in diverse definitions and flexible forms of anomalies. This, in turn, increases the difficulty in interpreting detected anomalies. In this paper, we propose an unsupervised anomaly detection algorithm based on Variational AutoEncoders (VAE) to estimate underlying normal progressions for each given sequence represented as occurrence probabilities of events along the sequence progression. Events in violation of their occurrence probability are identified as abnormal. We also introduce a visualization system, EventThread3, to support interactive exploration and interpretations of anomalies within the context of normal sequence progressions in the dataset through comprehensive one-to-many sequence comparison. Finally, we quantitatively evaluate the performance of our anomaly detection algorithm and demonstrate the effectiveness of our system through a case study

    An Examination of E-Banking Fraud Prevention and Detection in Nigerian Banks

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    E-banking offers a number of advantages to financial institutions, including convenience in terms of time and money. However, criminal activities in the information age have changed the way banking operations are performed. This has made e-banking an area of interest. The growth of cybercrime – particularly hacking, identity theft, phishing, Trojans, service denial attacks and account takeover– has created several challenges for financial institutions, especially regarding how they protect their assets and prevent their customers from becoming victims of cyber fraud. These criminal activities have remained prevalent due to certain features of cyber, such as the borderless nature of the internet and the continuous growth of the computer networks. Following these identified challenges for financial institutions, this study examines e-banking fraud prevention and detection in the Nigerian banking sector; particularly the current nature, impacts, contributing factors, and prevention and detection mechanisms of e-banking fraud in Nigerian banking institutions. This study adopts mixed research methods with the aid of descriptive and inferential analysis, which comprised exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) for the quantitative data analysis, whilst thematic analysis was used for the qualitative data analysis. The theoretical framework was informed by Routine Activity Theory (RAT) and Fraud Management Lifecycle Theory (FMLT). The findings show that the factors contributing to the increase in e-banking fraud in Nigeria include ineffective banking operations, internal control issues, lack of customer awareness and bank staff training and education, inadequate infrastructure, presence of sophisticated technological tools in the hands of fraudsters, negligence of banks’ customers concerning their e-banking account devices, lack of compliance with the banking rules and regulations, and ineffective legal procedure and law enforcement. In addition, the enforcement of rules and regulations in relation to the prosecution of financial fraudsters has been passive in Nigeria. Moreover, the findings also show that the activities of each stage of fraud management lifecycle theory are interdependent and have a collective and considerable influence on combating e-banking fraud. The results of the findings confirm that routine activity theory is a real-world theoretical framework while applied to e-banking fraud. Also, from the analysis of the findings, this research offers a new model for e-banking fraud prevention and detection within the Nigerian banking sector. This new model confirms that to have perfect prevention and detection of e-banking fraud, there must be a presence of technological mechanisms, fraud monitoring, effective internal controls, customer complaints, whistle-blowing, surveillance mechanisms, staff-customer awareness and education, legal and judicial controls, institutional synergy mechanisms of in the banking systems. Finally, the findings from the analyses of this study have some significant implications; not only for academic researchers or scholars and accounting practitioners, but also for policymakers in the financial institutions and anti-fraud agencies in both the private and public sectors

    Machine learning methods for the characterization and classification of complex data

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    This thesis work presents novel methods for the analysis and classification of medical images and, more generally, complex data. First, an unsupervised machine learning method is proposed to order anterior chamber OCT (Optical Coherence Tomography) images according to a patient's risk of developing angle-closure glaucoma. In a second study, two outlier finding techniques are proposed to improve the results of above mentioned machine learning algorithm, we also show that they are applicable to a wide variety of data, including fraud detection in credit card transactions. In a third study, the topology of the vascular network of the retina, considering it a complex tree-like network is analyzed and we show that structural differences reveal the presence of glaucoma and diabetic retinopathy. In a fourth study we use a model of a laser with optical injection that presents extreme events in its intensity time-series to evaluate machine learning methods to forecast such extreme events.El presente trabajo de tesis desarrolla nuevos métodos para el análisis y clasificación de imágenes médicas y datos complejos en general. Primero, proponemos un método de aprendizaje automático sin supervisión que ordena imágenes OCT (tomografía de coherencia óptica) de la cámara anterior del ojo en función del grado de riesgo del paciente de padecer glaucoma de ángulo cerrado. Luego, desarrollamos dos métodos de detección automática de anomalías que utilizamos para mejorar los resultados del algoritmo anterior, pero que su aplicabilidad va mucho más allá, siendo útil, incluso, para la detección automática de fraudes en transacciones de tarjetas de crédito. Mostramos también, cómo al analizar la topología de la red vascular de la retina considerándola una red compleja, podemos detectar la presencia de glaucoma y de retinopatía diabética a través de diferencias estructurales. Estudiamos también un modelo de un láser con inyección óptica que presenta eventos extremos en la serie temporal de intensidad para evaluar diferentes métodos de aprendizaje automático para predecir dichos eventos extremos.Aquesta tesi desenvolupa nous mètodes per a l’anàlisi i la classificació d’imatges mèdiques i dades complexes. Hem proposat, primer, un mètode d’aprenentatge automàtic sense supervisió que ordena imatges OCT (tomografia de coherència òptica) de la cambra anterior de l’ull en funció del grau de risc del pacient de patir glaucoma d’angle tancat. Després, hem desenvolupat dos mètodes de detecció automàtica d’anomalies que hem utilitzat per millorar els resultats de l’algoritme anterior, però que la seva aplicabilitat va molt més enllà, sent útil, fins i tot, per a la detecció automàtica de fraus en transaccions de targetes de crèdit. Mostrem també, com en analitzar la topologia de la xarxa vascular de la retina considerant-la una xarxa complexa, podem detectar la presència de glaucoma i de retinopatia diabètica a través de diferències estructurals. Finalment, hem estudiat un làser amb injecció òptica, el qual presenta esdeveniments extrems en la sèrie temporal d’intensitat. Hem avaluat diferents mètodes per tal de predir-los.Postprint (published version

    Saber: window-based hybrid stream processing for heterogeneous architectures

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    Modern servers have become heterogeneous, often combining multicore CPUs with many-core GPGPUs. Such heterogeneous architectures have the potential to improve the performance of data-intensive stream processing applications, but they are not supported by current relational stream processing engines. For an engine to exploit a heterogeneous architecture, it must execute streaming SQL queries with sufficient data-parallelism to fully utilise all available heterogeneous processors, and decide how to use each in the most effective way. It must do this while respecting the semantics of streaming SQL queries, in particular with regard to window handling. We describe SABER, a hybrid high-performance relational stream processing engine for CPUs and GPGPUs. SABER executes windowbased streaming SQL queries in a data-parallel fashion using all available CPU and GPGPU cores. Instead of statically assigning query operators to heterogeneous processors, SABER employs a new adaptive heterogeneous lookahead scheduling strategy, which increases the share of queries executing on the processor that yields the highest performance. To hide data movement costs, SABER pipelines the transfer of stream data between different memory types and the CPU/GPGPU. Our experimental comparison against state-ofthe-art engines shows that SABER increases processing throughput while maintaining low latency for a wide range of streaming SQL queries with small and large windows sizes
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