2,858 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

    Fractal feature selection model for enhancing high-dimensional biological problems

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    The integration of biology, computer science, and statistics has given rise to the interdisciplinary field of bioinformatics, which aims to decode biological intricacies. It produces extensive and diverse features, presenting an enormous challenge in classifying bioinformatic problems. Therefore, an intelligent bioinformatics classification system must select the most relevant features to enhance machine learning performance. This paper proposes a feature selection model based on the fractal concept to improve the performance of intelligent systems in classifying high-dimensional biological problems. The proposed fractal feature selection (FFS) model divides features into blocks, measures the similarity between blocks using root mean square error (RMSE), and determines the importance of features based on low RMSE. The proposed FFS is tested and evaluated over ten high-dimensional bioinformatics datasets. The experiment results showed that the model significantly improved machine learning accuracy. The average accuracy rate was 79% with full features in machine learning algorithms, while FFS delivered promising results with an accuracy rate of 94%

    Data-assisted modeling of complex chemical and biological systems

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    Complex systems are abundant in chemistry and biology; they can be multiscale, possibly high-dimensional or stochastic, with nonlinear dynamics and interacting components. It is often nontrivial (and sometimes impossible), to determine and study the macroscopic quantities of interest and the equations they obey. One can only (judiciously or randomly) probe the system, gather observations and study trends. In this thesis, Machine Learning is used as a complement to traditional modeling and numerical methods to enable data-assisted (or data-driven) dynamical systems. As case studies, three complex systems are sourced from diverse fields: The first one is a high-dimensional computational neuroscience model of the Suprachiasmatic Nucleus of the human brain, where bifurcation analysis is performed by simply probing the system. Then, manifold learning is employed to discover a latent space of neuronal heterogeneity. Second, Machine Learning surrogate models are used to optimize dynamically operated catalytic reactors. An algorithmic pipeline is presented through which it is possible to program catalysts with active learning. Third, Machine Learning is employed to extract laws of Partial Differential Equations describing bacterial Chemotaxis. It is demonstrated how Machine Learning manages to capture the rules of bacterial motility in the macroscopic level, starting from diverse data sources (including real-world experimental data). More importantly, a framework is constructed though which already existing, partial knowledge of the system can be exploited. These applications showcase how Machine Learning can be used synergistically with traditional simulations in different scenarios: (i) Equations are available but the overall system is so high-dimensional that efficiency and explainability suffer, (ii) Equations are available but lead to highly nonlinear black-box responses, (iii) Only data are available (of varying source and quality) and equations need to be discovered. For such data-assisted dynamical systems, we can perform fundamental tasks, such as integration, steady-state location, continuation and optimization. This work aims to unify traditional scientific computing and Machine Learning, in an efficient, data-economical, generalizable way, where both the physical system and the algorithm matter

    Innovation in Energy Security and Long-Term Energy Efficiency Ⅱ

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    The sustainable development of our planet depends on the use of energy. The increasing world population inevitably causes an increase in the demand for energy, which, on the one hand, threatens us with the potential to encounter a shortage of energy supply, and, on the other hand, causes the deterioration of the environment. Therefore, our task is to reduce this demand through different innovative solutions (i.e., both technological and social). Social marketing and economic policies can also play their role by affecting the behavior of households and companies and by causing behavioral change oriented to energy stewardship, with an overall switch to renewable energy resources. This reprint provides a platform for the exchange of a wide range of ideas, which, ultimately, would facilitate driving societies toward long-term energy efficiency

    Towards the ideal vascular implant: Use of machine learning and statistical approaches to optimise manufacturing parameters

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    Introduction: Electrospinning is a manufacturing technique that creates a net of nano and microfibres able to mimic the natural extracellular matrix (ECM) of biological tissue. Electrospun scaffolds' morphology and mechanical behaviour can be tailored by modifying the environmental, solution and process parameters. This study aims to produce biomimetic vascular implants optimising the manufacturing set up through two machine learning techniques and statistical approaches. Methods: Polyvinyl alcohol (PVA) based scaffolds were produced by modifying the concentration of the polymer, flow rate, voltage, type of collector, diameter of the needle, distance between needle and collector and revolutions of the mandrel. The scaffolds were morphologically and mechanically characterised using scanning electron microscopy and mechanical testing respectively to inform the morphological model (simultaneously predicting diameter of the fibres and inter-fibre separation) and mechanical model (predicting strain at rupture and ultimate tensile strength). Results: Prediction and traditional techniques led to an optimum set up of: 12% PVA, 1 ml/h flow rate, 20 kV, 8 cm between the needle, 18 G gauge needle, rotational mandrel of 15 cm and 2000 rpm. Optimised PVA scaffolds replicated the mechanical properties and morphology of the vascular tissue with an ultimate tensile strength of 6.17 ± 0.18 MPa, a strain at break of 97.39 ± 5.06, fibre diameters of 126 ± 6.11 nm and inter-fibre separation of 1488 ± 91.99 nm. Discussion: This work revealed for the first time that machine learning Chi-squared Automatic Interaction Detection (CHAID) models are a novel and visual route to elect the optimum manufacturing set up to develop biomimetic vascular implants. Novel two-output Artificial Neural Networks (ANN) and multivariate analysis of variance and covariance (MANOVA, MANCOVA) models presented comparable prediction results (R2=0.91); however, two-output ANN predicted models demonstrated to be the most powerful tool for non-parametric conditions, showing cross-validation mean squared errors (MSE) of 0.0001943. Multi Linear Regression models (MLR) exhibited the lowest accuracy in their predictions (R2=0.6). Machine learning, statistical approaches and traditional characterisation methods were studied to successfully achieve vascular substitutes with analogous biomechanical behaviour and physical structure to the native vascular tissue

    Sensing Collectives: Aesthetic and Political Practices Intertwined

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    Are aesthetics and politics really two different things? The book takes a new look at how they intertwine, by turning from theory to practice. Case studies trace how sensory experiences are created and how collective interests are shaped. They investigate how aesthetics and politics are entangled, both in building and disrupting collective orders, in governance and innovation. This ranges from populist rallies and artistic activism over alternative lifestyles and consumer culture to corporate PR and governmental policies. Authors are academics and artists. The result is a new mapping of the intermingling and co-constitution of aesthetics and politics in engagements with collective orders

    The Slightest Attachment: When Psychiatric Spaces Enact Affinities

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    While the disciplinary architecture of hospitals has long prevailed in psychiatry, many care teams now work in smaller structures, within communities. The author explores one of these places: Drawing on fieldwork in a psychiatric day center for teenagers, she traces how spatial arrangements matter in the care practice. From a corner in which one can withdraw, to a kitchen inviting to hang around, or displayed artworks that pique one's curiosity, caregivers use the material environment to stir up the slightest affinity from teenagers. This study thus expands our idea of what attachment is, and makes us more able to recognize the subtle dynamics between care, things, and spaces. With a preface by Jeannette Pols

    Artificial Intelligence for Cognitive Health Assessment: State-of-the-Art, Open Challenges and Future Directions

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    The subjectivity and inaccuracy of in-clinic Cognitive Health Assessments (CHA) have led many researchers to explore ways to automate the process to make it more objective and to facilitate the needs of the healthcare industry. Artificial Intelligence (AI) and machine learning (ML) have emerged as the most promising approaches to automate the CHA process. In this paper, we explore the background of CHA and delve into the extensive research recently undertaken in this domain to provide a comprehensive survey of the state-of-the-art. In particular, a careful selection of significant works published in the literature is reviewed to elaborate a range of enabling technologies and AI/ML techniques used for CHA, including conventional supervised and unsupervised machine learning, deep learning, reinforcement learning, natural language processing, and image processing techniques. Furthermore, we provide an overview of various means of data acquisition and the benchmark datasets. Finally, we discuss open issues and challenges in using AI and ML for CHA along with some possible solutions. In summary, this paper presents CHA tools, lists various data acquisition methods for CHA, provides technological advancements, presents the usage of AI for CHA, and open issues, challenges in the CHA domain. We hope this first-of-its-kind survey paper will significantly contribute to identifying research gaps in the complex and rapidly evolving interdisciplinary mental health field

    New perspectives on A.I. in sentencing. Human decision-making between risk assessment tools and protection of humans rights.

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    The aim of this thesis is to investigate a field that until a few years ago was foreign to and distant from the penal system. The purpose of this undertaking is to account for the role that technology could plays in the Italian Criminal Law system. More specifically, this thesis attempts to scrutinize a very intricate phase of adjudication. After deciding on the type of an individual's liability, a judge must decide on the severity of the penalty. This type of decision implies a prognostic assessment that looks to the future. It is precisely in this field and in prognostic assessments that, as has already been anticipated in the United, instruments and processes are inserted in the pre-trial but also in the decision-making phase. In this contribution, we attempt to describe the current state of this field, trying, as a matter of method, to select the most relevant or most used tools. Using comparative and qualitative methods, the uses of some of these instruments in the supranational legal system are analyzed. Focusing attention on the Italian system, an attempt was made to investigate the nature of the element of an individual's ‘social dangerousness’ (pericolosità sociale) and capacity to commit offences, types of assessments that are fundamental in our system because they are part of various types of decisions, including the choice of the best sanctioning treatment. It was decided to turn our attention to this latter field because it is believed that the judge does not always have the time, the means and the ability to assess all the elements of a subject and identify the best 'individualizing' treatment in order to fully realize the function of Article 27, paragraph 3 of the Constitution

    An approach for classification of Alzheimer’s disease using deep neural network and brain magnetic resonance imaging (MRI)

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    Alzheimer’s disease (AD) is a deadly cognitive condition in which people develop severe dementia symptoms. Neurologists commonly use a series of physical and mental tests to diagnose AD that may not always be effective. Damage to brain cells is the most significant physical change in AD. Proper analysis of brain images may assist in the identification of crucial bio-markers for the disease. Because the development of brain cells is so intricate, traditional image processing algorithms sometimes fail to perceive important bio-markers. The deep neural network (DNN) is a machine learning technique that helps specialists in making appropriate decisions. In this work, we used brain magnetic resonance scans to implement some commonly used DNN models for AD classification. According to the classification results, where the average of multiple metrics is observed, which includes accuracy, precision, recall, and an F1 score, it is found that the DenseNet-121 model achieved the best performance (86.55%). Since DenseNet-121 is a computationally expensive model, we proposed a hybrid technique incorporating LeNet and AlexNet that is light weight and also capable of outperforming DenseNet. To extract important features, we replaced the traditional convolution Layers with three parallel small filters (1 × 1, 3 × 3, and 5 × 5). The model functions effectively, with an overall performance rate of 93.58%. Mathematically, it is observed that the proposed model generates significantly fewer convolutional parameters, resulting in a lightweight model that is computationally effective.Web of Science123art. no. 67
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