Developing an Explainable Machine Learning-Based Thyroid Disease Prediction Model

Healthcare and medicine are key areas where machine learning algorithms are widely used. The medical decision support systems thus created are accurate enough, however, they suffer from the lack of transparency in decision making and shows a black box behavior. However, transparency and trust are significant in the field of health and medicine and hence, a black box system is sub optimal in terms of widespread applicability and reach. Hence, the explainablility of the research make the system reliable and understandable, thereby enhancing its social acceptability. The presented work explores a thyroid disease diagnosis system. SHAP, a popular method based on coalition game theory is used for interpretability of results. The work explains the system behavior both locally and globally and shows how machine leaning can be used to ascertain the causality of the disease and support doctors to suggest the most effective treatment of the disease. The work not only demonstrates the results of machine learning algorithms but also explains related feature importance and model insights.

Machine Learning Methods for Automatic Silent Speech Recognition Using a Wearable Graphene Strain Gauge Sensor

Silent speech recognition is the ability to recognise intended speech without audio information. Useful applications can be found in situations where sound waves are not produced or cannot be heard. Examples include speakers with physical voice impairments or environments in which audio transference is not reliable or secure. Developing a device which can detect non-auditory signals and map them to intended phonation could be used to develop a device to assist in such situations. In this work, we propose a graphene-based strain gauge sensor which can be worn on the throat and detect small muscle movements and vibrations. Machine learning algorithms then decode the non-audio signals and create a prediction on intended speech. The proposed strain gauge sensor is highly wearable, utilising graphene’s unique and beneficial properties including strength, flexibility and high conductivity. A highly flexible and wearable sensor able to pick up small throat movements is fabricated by screen printing graphene onto lycra fabric. A framework for interpreting this information is proposed which explores the use of several machine learning techniques to predict intended words from the signals. A dataset of 15 unique words and four movements, each with 20 repetitions, was developed and used for the training of the machine learning algorithms. The results demonstrate the ability for such sensors to be able to predict spoken words. We produced a word accuracy rate of 55% on the word dataset and 85% on the movements dataset. This work demonstrates a proof-of-concept for the viability of combining a highly wearable graphene strain gauge and machine leaning methods to automate silent speech recognition.

Novel Technique for Estimation of Cell Parameters Using MATLAB/Simulink

The main source of power in Electric Vehicles (EVs) is derived from batteries. An efficient cell model is extremely important for the development of complex algorithms like core temperature estimation, State of Health (SOH) estimation and State of Charge (SOC) estimation. In this paper, a new methodology for improving the SOC estimation using Equivalent Cell Model (ECM) approach is proposed. The modeling and simulations were performed using MATLAB/Simulink software. In this regard, a Li polymer cell was modeled as a single Resistor-Capacitor (RC) pair (R0, R1 and C1) model using PowerTrain blockset in MATLAB/Simulink software. To validate the developed model, a NASA dataset was used as the reference dataset. The cell model was tuned against the NASA dataset for different currents in such a way that the error in the terminal voltages (difference in terminal voltage between the dataset and the ECM) is <±0.2 V. The mean error and the standard deviation of the error were 0.0529 and 0.0310 respectively. This process was performed by tuning the cell parameters. It was found that the cell parameters were independent of the nominal capacity of the cell. The cell parameters of Li polymer and the Li ion cells (NASA dataset) were found be almost identical. These parameters showed dependence on SOC and temperature. The major challenge in a battery management system is the parameter estimation and prediction of SOC, this is because the degradation of battery is highly nonlinear in nature. This paper presents the parameter estimation and prediction of state of charge of Li ion batteries by implementing different machine learning techniques. The selection of the best suited algorithm is finalized through the performance indices mainly by evaluating the values of R- Squared. The parameters were trained using various Machine Leaning (ML) techniques for regression data analysis using Simulink. A study on Support Vector Machine (SVM) technique was carried out for the simulated and tuned data. It is concluded that the SVM algorithm was best suited. A detailed analysis on the errors associated with the algorithms was also carried out. Later, these parameters were trained using various Machine Leaning (ML) techniques for regression data analysis using Simulink. A study on SVM technique was carried out for the simulated and tuned data. It is concluded that the SVM algorithm was best suited. A detailed analysis on the errors associated with the algorithms was also carried out.

Machine Leaning Fundamentals

Machine Learning is one of the fastest-growing fields which has witnessed exponential growth in the technical world. But in this fast-growing field, the question is how to get started? Briefly this paper introduces various languages popular for machine learning and next it introduces a few math concepts that helps us understand what exactly is happening and to improve our model further we need to understand that. And finally, there is an overview of various IDEs that can be used to implement these languages for machine learning.

Implicitly perturbed Hamiltonian: a class of versatile and general-purpose molecular representations for machine leaning

Unraveling challenging problems by machine learning has recently become a hot topic in many scientific disciplines. For developing rigorous machine-learning models to study problems of interest in molecular sciences, translating molecular structures to quantitative representations as suitable machine-learning inputs plays a central role. Many different molecular representations and the state-ofthe- art ones, although efficient in studying numerous molecular features, still are sub-optimal in many challenging cases, as discussed in the context of present research. The main aim of the present study is to introduce the Implicitly Perturbed Hamiltonian (ImPerHam) as a class of versatile representations for more efficient machine learning of challenging problems in molecular sciences. ImPerHam representations are defined as energy attributes of the molecular Hamiltonian, implicitly perturbed by a number of hypothetic or real arbitrary solvents based on continuum solvation models. We demonstrate outstanding performance of machine-learning models based on ImPerHam representations for three diverse and challenging cases of predicting inhibition of the CYP450 enzyme, high precision and transferrable evaluation of conformational energy of molecular systems and accurately reproducing solvation free energies for large benchmark sets.

EVERYDAY DATA CULTURES

This panel deploys a range of qualitative methodologies to investigate how processes of datafication meet with the subjective experiences of ordinary people, and the practices of everyday life. We draw on the model of ‘everyday data cultures’ proposed by Burgess (2017) to explore the ways diverse data practices – including the production and circulation of data visualisations, modes of data storage and vernacular engagements with data literacy – can be understood as aspects of culture. Following Burgess, we define everyday data cultures as the practices that form around and in response to the social media and other data (and data trails) that people generate as we go about our daily lives. These practices form from our diverse engagements with, experiences of, and approaches to understanding and negotiating these data Across these four papers, we address the everyday politics of social media platforms; the development of vernacular pedagogies of AI and machine leaning practices; the historical datafication of sex and gender, and mundane workplace practices of storing, concealing and revealing personal data. In doing so, we seek to highlight and amplify everyday human agency, as well as explore its limits and uneven distribution, and consider how it is being transformed through the logics of data and the machines that feed on them.

A wave prediction framework based on machine learning and the third generation wave model

To predict the evolution of wave spectrum in real ocean, a machine leaning framework is developed by training a long short-term memory (LSTM) neural network on a physics-based third generation wave model (Simulating WAve Nearshore, SWAN). Considering the realistic ocean waves are usually mixtures of windsea and swells, the wave spectrum is partitioned using a watershed algorithm, such that the windsea and swells are analysed and predicted separately. Four parameters are selected to capture the wave spectrum of each systems, including the significant wave height Hs, peaked wave period Tp, mean propagation direction Qm and directional spreading width sq. The results demonstrate the machine leaning model can achieve accurate prediction of wave condition, the MAEPs (mean absolute error percentage) of 1-hour prediction are less than 5.9%, 3.3%, 3.5% and 3.3% for Hs, Tp, Qm and σθ respectively, and accurate prediction of wave spectra is achieved. Even for 10-hour prediction, satisfactory results are obtained, e.g. the MAEP of Hs is less than 15.5%. The effects of output size (i.e. prediction duration), input data size (i.e. number of delays), as well as different combinations of input features on predictions of wave condition are examined.

SIGNIFICANT ROLE OF AI & ML TECHNIQUES USED IN IMPROVEMENT OF CYBER SECURITY

Cyber Security has become an arising challenge for business information system in current era. AI (Artificial Intelligence) is broadly utilized in various field, however it is still generally new in cyber security. Nonetheless, the applications in network protection are significant for everybody`s day by day life. In this paper, we present the current status of AI in cyber security field, and afterward portray a few contextual investigations and uses of AI to help the community including engineering managers, teachers, educators, business people, and understudies to more readily comprehend this field, for example, the difficulties and uncertain issues of AI in online protection. According to the new challenges, the expert community has two main approaches: to adopt the philosophy and methods of Military Intelligence, and to use Artificial Intelligence methods for counteraction of Cyber Attacks. Cyber security is a vital danger for any business as the quantity of attacks is expanding. Developing of attacks on cyber security is undermining our reality. AI (Artificial Intelligence) and ML (Machine Leaning) can help identify dangers and give proposals to cyber Analyst. Advancement of appropriation of AI/ML applied to cyber security requires banding together of industry, the scholarly community, and government on a worldwide scale. We also discuss future research opportunities associated with the development of AI techniques in the cyber security ?eld across a scope of utilization areas.