5,856 research outputs found

    A novel IoT intrusion detection framework using Decisive Red Fox optimization and descriptive back propagated radial basis function models.

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    The Internet of Things (IoT) is extensively used in modern-day life, such as in smart homes, intelligent transportation, etc. However, the present security measures cannot fully protect the IoT due to its vulnerability to malicious assaults. Intrusion detection can protect IoT devices from the most harmful attacks as a security tool. Nevertheless, the time and detection efficiencies of conventional intrusion detection methods need to be more accurate. The main contribution of this paper is to develop a simple as well as intelligent security framework for protecting IoT from cyber-attacks. For this purpose, a combination of Decisive Red Fox (DRF) Optimization and Descriptive Back Propagated Radial Basis Function (DBRF) classification are developed in the proposed work. The novelty of this work is, a recently developed DRF optimization methodology incorporated with the machine learning algorithm is utilized for maximizing the security level of IoT systems. First, the data preprocessing and normalization operations are performed to generate the balanced IoT dataset for improving the detection accuracy of classification. Then, the DRF optimization algorithm is applied to optimally tune the features required for accurate intrusion detection and classification. It also supports increasing the training speed and reducing the error rate of the classifier. Moreover, the DBRF classification model is deployed to categorize the normal and attacking data flows using optimized features. Here, the proposed DRF-DBRF security model's performance is validated and tested using five different and popular IoT benchmarking datasets. Finally, the results are compared with the previous anomaly detection approaches by using various evaluation parameters

    Enhancing feature selection with a novel hybrid approach incorporating genetic algorithms and swarm intelligence techniques

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    Computing advances in data storage are leading to rapid growth in large-scale datasets. Using all features increases temporal/spatial complexity and negatively influences performance. Feature selection is a fundamental stage in data preprocessing, removing redundant and irrelevant features to minimize the number of features and enhance the performance of classification accuracy. Numerous optimization algorithms were employed to handle feature selection (FS) problems, and they outperform conventional FS techniques. However, there is no metaheuristic FS method that outperforms other optimization algorithms in many datasets. This motivated our study to incorporate the advantages of various optimization techniques to obtain a powerful technique that outperforms other methods in many datasets from different domains. In this article, a novel combined method GASI is developed using swarm intelligence (SI) based feature selection techniques and genetic algorithms (GA) that uses a multi-objective fitness function to seek the optimal subset of features. To assess the performance of the proposed approach, seven datasets have been collected from the UCI repository and exploited to test the newly established feature selection technique. The experimental results demonstrate that the suggested method GASI outperforms many powerful SI-based feature selection techniques studied. GASI obtains a better average fitness value and improves classification performance

    Optimized Dictionaries: A Semi-Automated Workflow of Concept Identification in Text-Data

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    Identifying social science concepts and measuring their prevalence and framing in text data has been a key task of scientists ever since. Whereas debates about text classifications typically contrast different approaches with each other, we propose a workflow that generates optimized dictionaries that are based on the complementary use of expert dictionaries, machine learning, and topic modeling. We demonstrate our case by identifying the concept of "territorial politics" in leading newspapers vis-Ă -vis parliamentary speeches in Spain (1976-2018) and the UK (1900-2018). We show that our optimized dictionaries outperform singular text-identification techniques with F1-scores around 0.9 for unseen data, even if the unseen data comes from a different political domain (media vs. parliaments). Optimized dictionaries have increasing returns and should be developed as a common good for researchers overcoming costly particularism

    On the Generation of Realistic and Robust Counterfactual Explanations for Algorithmic Recourse

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    This recent widespread deployment of machine learning algorithms presents many new challenges. Machine learning algorithms are usually opaque and can be particularly difficult to interpret. When humans are involved, algorithmic and automated decisions can negatively impact people’s lives. Therefore, end users would like to be insured against potential harm. One popular way to achieve this is to provide end users access to algorithmic recourse, which gives end users negatively affected by algorithmic decisions the opportunity to reverse unfavorable decisions, e.g., from a loan denial to a loan acceptance. In this thesis, we design recourse algorithms to meet various end user needs. First, we propose methods for the generation of realistic recourses. We use generative models to suggest recourses likely to occur under the data distribution. To this end, we shift the recourse action from the input space to the generative model’s latent space, allowing to generate counterfactuals that lie in regions with data support. Second, we observe that small changes applied to the recourses prescribed to end users likely invalidate the suggested recourse after being nosily implemented in practice. Motivated by this observation, we design methods for the generation of robust recourses and for assessing the robustness of recourse algorithms to data deletion requests. Third, the lack of a commonly used code-base for counterfactual explanation and algorithmic recourse algorithms and the vast array of evaluation measures in literature make it difficult to compare the per formance of different algorithms. To solve this problem, we provide an open source benchmarking library that streamlines the evaluation process and can be used for benchmarking, rapidly developing new methods, and setting up new experiments. In summary, our work contributes to a more reliable interaction of end users and machine learned models by covering fundamental aspects of the recourse process and suggests new solutions towards generating realistic and robust counterfactual explanations for algorithmic recourse

    Multi-epoch machine learning for galaxy formation

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    In this thesis I utilise a range of machine learning techniques in conjunction with hydrodynamical cosmological simulations. In Chapter 2 I present a novel machine learning method for predicting the baryonic properties of dark matter only subhalos taken from N-body simulations. The model is built using a tree-based algorithm and incorporates subhalo properties over a wide range of redshifts as its input features. I train the model using a hydrodynamical simulation which enables it to predict black hole mass, gas mass, magnitudes, star formation rate, stellar mass, and metallicity. This new model surpasses the performance of previous models. Furthermore, I explore the predictive power of each input property by looking at feature importance scores from the tree-based model. By applying the method to the LEGACY N-body simulation I generate a large volume mock catalog of the quasar population at z=3. By comparing this mock catalog with observations, I demonstrate that the IllustrisTNG subgrid model for black holes is not accurately capturing the growth of the most massive objects. In Chapter 3 I apply my method to investigate the evolution of galaxy properties in different simulations, and in various environments within a single simulation. By comparing the Illustris, EAGLE, and TNG simulations I show that subgrid model physics plays a more significant role than the choice of hydrodynamics method. Using the CAMELS simulation suite I consider the impact of cosmological and astrophysical parameters on the buildup of stellar mass within the TNG and SIMBA models. In the final chapter I apply a combination of neural networks and symbolic regression methods to construct a semi-analytic model which reproduces the galaxy population from a cosmological simulation. The neural network based approach is capable of producing a more accurate population than a previous method of binning based on halo mass. The equations resulting from symbolic regression are found to be a good approximation of the neural network

    Leveraging technology-driven strategies to untangle omics big data: circumventing roadblocks in clinical facets of oral cancer

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    Oral cancer is one of the 19most rapidly progressing cancers associated with significant mortality, owing to its extreme degree of invasiveness and aggressive inclination. The early occurrences of this cancer can be clinically deceiving leading to a poor overall survival rate. The primary concerns from a clinical perspective include delayed diagnosis, rapid disease progression, resistance to various chemotherapeutic regimens, and aggressive metastasis, which collectively pose a substantial threat to prognosis. Conventional clinical practices observed since antiquity no longer offer the best possible options to circumvent these roadblocks. The world of current cancer research has been revolutionized with the advent of state-of-the-art technology-driven strategies that offer a ray of hope in confronting said challenges by highlighting the crucial underlying molecular mechanisms and drivers. In recent years, bioinformatics and Machine Learning (ML) techniques have enhanced the possibility of early detection, evaluation of prognosis, and individualization of therapy. This review elaborates on the application of the aforesaid techniques in unraveling potential hints from omics big data to address the complexities existing in various clinical facets of oral cancer. The first section demonstrates the utilization of omics data and ML to disentangle the impediments related to diagnosis. This includes the application of technology-based strategies to optimize early detection, classification, and staging via uncovering biomarkers and molecular signatures. Furthermore, breakthrough concepts such as salivaomics-driven non-invasive biomarker discovery and omics-complemented surgical interventions are articulated in detail. In the following part, the identification of novel disease-specific targets alongside potential therapeutic agents to confront oral cancer via omics-based methodologies is presented. Additionally, a special emphasis is placed on drug resistance, precision medicine, and drug repurposing. In the final section, we discuss the research approaches oriented toward unveiling the prognostic biomarkers and constructing prediction models to capture the metastatic potential of the tumors. Overall, we intend to provide a bird’s eye view of the various omics, bioinformatics, and ML approaches currently being used in oral cancer research through relevant case studies

    Does Precision-Based Medicine Hold the Promise of a New Approach to Predicting and Treating Spontaneous Preterm Birth?

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    Background: Preterm birth (PTB) is a leading cause of childhood disability, and it has become a key public health priority recognized by the World Health Organization and the United Nations. Objectives: This review will: (1) summarize current practice in the diagnosis and management of PTB, (2) outline developments in precision-based medicine for diagnostics to improve the care provided to pregnant women at risk of PTB, and (3) discuss the implications of current research in personalized medicine and the potential of future advances to influence the clinical care of women at risk of PTB. Methodology: This is a narrative literature review. Relevant journal articles were identified following searches of computerized databases. Key Results: Current and emerging technologies for the utility of personalized medicine in the context of PTB have the potential for applications in: (1) direct diagnostics to identify and target infection as one of the main known causes of PTB, (2) identifying novel maternal and fetal biomarkers, (3) the use of artificial intelligence and computational modeling, and (4) combining methods to enhance diagnosis and treatment. Conclusions: In this paper, we show how current research has moved in the direction of the targeted use of biomarkers in the context of PTB, with many novel approaches

    An embodied approach to informational interventions: using conceptual metaphors to promote sustainable healthy diets

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    Poor diet quality and environmental degradation are two major challenges of our times. Unhealthy and unsustainable dietary practices, such as the overconsumption of meat and consumer food waste behaviour, contribute greatly to both issues. Across seventeen online and field experiments, in two different cultures (US and China), this thesis investigates if the embodied cognition approach, and more specifically, research on conceptual metaphors, can be used to develop interventions to promote sustainable healthy diets. Interventions relying on conceptual metaphors have been shown to stimulate attitudinal and behavioural changes in other fields (e.g., marketing and political communications), but are rarely adopted to encourage sustainable healthy diets. To fill in this gap in the literature, I conducted five sets of experimental studies examining the effects of different metaphors on specific sustainable healthy dietary practices, each of which forms an independent empirical paper (Chapters 2-6 of the thesis). After introducing the current perspectives on embodied cognition and conceptual metaphors in the context of this research (Chapter 1), Chapter 2 looks into the conceptual metaphor “Healthy is Up”, demonstrating that US people implicitly associate healthiness with verticality, and offering recommendations for healthy eating guidelines. Chapter 3 extends this research to Chinese samples and partially replicates the results. Chapter 4 shows that the anthropomorphic metaphor “Animals are Friends” discourages meat consumption by inducing anticipatory guilt among US omnivores, whereas Chapter 5 reveals that Chinese omnivores are more responsive to another anthropomorphic metaphor, namely, “Animals are Family”. Bringing lab insights 6 to the real world, Chapter 6 demonstrates with a longitudinal field experiment that anthropomorphic metaphors together with environmental feedback result in a higher reduction in food waste as compared to other feedback interventions. The strengths, limitations and implications of those empirical papers are discussed in the conclusive part of the thesis

    Placental origins of health & disease:Therapeutic opportunities

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    Placental origins of health & disease:Therapeutic opportunities

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