A Survey: Approaches for Detecting the Autism Spectrum Disorder

A brain disease mean autism spectrum disorder affects a person's ability to connect, communicate, and remember. Though autism is capable of being diagnosed regardless of age, most of the disorder's signs begin to appear around its initial two years of life and increase as time goes on. People with autism suffer from a wide range of difficulties, such sensory problems, action impairments, intellectual disabilities, and psychological disorders including depression and anxiety. Autism has been rising at an unacceptably rapid pace surrounding around the globe. Autism detection involves an enormous amount of time and money. The early detection of autism might be highly advantageous in regards to treating patients with the right medical treatments at the correct moment in time. It could prevent the individual's illnesses before developing severe and could help in decreasing future expenses associated to a diagnosis that was delayed. Thereby, the requirement to develop a rapid, trustworthy, and simple examination device that can make predictions is essential. Autism Spectrum Disorder (ASD) has been gaining momentum presently more quickly than at any time earlier. Diagnostic evaluation of autistic characteristics is extremely expensive and time-consuming as well. The advancement of algorithms for machine learning (ML) and Artificial intelligence (AI) have made it achievable to identify autism fairly earlier. Although the reality of numerous studies have been carried out performed utilising different techniques, these studies have not contributed to any definitive conclusions regarding the capacity of predicting autism attributes in regards to different age categories. Thereby, the objective of this research is to predict Autism among people of all ages and to provide an effective model for prediction using various ML approaches

Building Cyberspace. Information, Place and Policy

Information and place have always been linked. From prehistoric forest and hydraulic expire to canal network and the networked knowledge economy, the space of flows gives rise to the way human beings perceive the world as well as to the objects they perceive. The historical relationship between information and place is important in understanding Cyberspace as a space of information that reshapes our engagement with the physical world

Sensing reality? New monitoring technologies for global sustainability standards

In the 1990s, civil society organizations partnered with business to “green” global supply chains by setting up formal sustainability standard-setting organizations (SSOs) in sectors including organic food, fair trade, forestry, and fisheries. Although SSOs have withstood the long-standing allegations that they are unnecessary, costly, nondemocratic, and trade-distorting, they must now respond to a new challenge, arising from recent developments in technology. Conceived in the pre-Internet era, SSOs are discovering that verification systems that utilize annual, expert-led, low-tech field audits are under pressure from new information and communication technologies that collect, aggregate, interpret, and display open-source “Big Data” in almost real time. Drawing on the concept of governmentality and on interviews with experts in sustainability certification and natural capital accounting, we argue that while these technological developments offer many positive opportunities, they also enable competing alternatives to the prevailing “truth” or governing rationality about what is happening “on the ground,” which is of critical existential importance to SSOs as guarantors of trust in claims about sustainable production. While SSOs are not helpless in the face of this challenge, we conclude that they will need to do more than take incremental action: rather, they should respond actively to the disintermediation challenge from new virtual monitoring technologies if they are to remain relevant in the coming decade

Sensing reality? New monitoring technologies for global sustainability standards

In the 1990s, civil society organizations partnered with business to “green” global supply chains by setting up formal sustainability standard-setting organizations (SSOs) in secwtors including organic food, fair trade, forestry, and fisheries. Although SSOs have withstood the long-standing allegations that they are unnecessary, costly, nondemocratic, and trade-distorting, they must now respond to a new challenge, arising from recent developments in technology. Conceived in the pre-Internet era, SSOs are discovering that verification systems that utilize annual, expert-led, low-tech field audits are under pressure from new information and communication technologies that collect, aggregate, interpret, and display open-source “Big Data” in almost real time. Drawing on the concept of governmentality and on interviews with experts in sustainability certification and natural capital accounting, we argue that while these technological developments offer many positive opportunities, they also enable competing alternatives to the prevailing “truth” or governing rationality about what is happening “on the ground,” which is of critical existential importance to SSOs as guarantors of trust in claims about sustainable production. While SSOs are not helpless in the face of this challenge, we conclude that they will need to do more than take incremental action: rather, they should respond actively to the disintermediation challenge from new virtual monitoring technologies if they are to remain relevant in the coming decade. © 2017 by the Massachusetts Institute of Technology

Conciliating accuracy and efficiency to empower engineering based on performance: a short journey

This paper revisits the different arts of engineering. The art of modeling for describing the behavior of complex systems from the solution of partial differential equations that are expected to govern their responses. Then, the art of simulation concerns the ability of solving these complex mathematical objects expected to describe the physical reality as accurately as possible (accuracy with respect to the exact solution of the models) and as fast as possible. Finally, the art of decision making needs to ensure accurate and fast predictions for efficient diagnosis and prognosis. For that purpose physics-informed digital twins (also known as Hybrid Twins) will be employed, allying real-time physics (where complex models are solved by using advanced model order reduction techniques) and physics-informed data-driven models for filling the gap between the reality and the physics-based model predictions. The use of physics-aware data-driven models in tandem with physics-based reduced order models allows us to predict very fast without compromising accuracy. This is compulsory for diagnosis and prognosis purposes

Multi-Person Brain Activity Recognition via Comprehensive EEG Signal Analysis

An electroencephalography (EEG) based brain activity recognition is a fundamental field of study for a number of significant applications such as intention prediction, appliance control, and neurological disease diagnosis in smart home and smart healthcare domains. Existing techniques mostly focus on binary brain activity recognition for a single person, which limits their deployment in wider and complex practical scenarios. Therefore, multi-person and multi-class brain activity recognition has obtained popularity recently. Another challenge faced by brain activity recognition is the low recognition accuracy due to the massive noises and the low signal-to-noise ratio in EEG signals. Moreover, the feature engineering in EEG processing is time-consuming and highly re- lies on the expert experience. In this paper, we attempt to solve the above challenges by proposing an approach which has better EEG interpretation ability via raw Electroencephalography (EEG) signal analysis for multi-person and multi-class brain activity recognition. Specifically, we analyze inter-class and inter-person EEG signal characteristics, based on which to capture the discrepancy of inter-class EEG data. Then, we adopt an Autoencoder layer to automatically refine the raw EEG signals by eliminating various artifacts. We evaluate our approach on both a public and a local EEG datasets and conduct extensive experiments to explore the effect of several factors (such as normalization methods, training data size, and Autoencoder hidden neuron size) on the recognition results. The experimental results show that our approach achieves a high accuracy comparing to competitive state-of-the-art methods, indicating its potential in promoting future research on multi-person EEG recognition.Comment: 10 page

Customer Perspective On The Purchase and Use Of Sustainable And Innovative Furniture In A Co-Creation Process

For developing a European industrial cooperation and involvement in the furniture industry, the international research project INEDIT conducted a survey for furniture customers. By finding out the needs and wishes of the customer regarding innovative products and the production process the project will establish a new way for designing and producing furniture. Within INEDIT a platform is built on which customized, technologically innovative and sustainable furniture can be created and produced in a co-creation process. The furniture industry should thus become significantly more flexible, transparent and sustainable. Following the "do-it-together" approach, a business ecosystem will be generated which creates added value not only for customers but also for designers, suppliers and manufacturing companies. In order to involve the customer even more actively in the design process and the production, the platform will provide access to a mix of digital and physical services and is linked to all other stakeholders in the value chain. To match the platform and the process to the needs, wishes and demands of the customer an anonymous survey with 300 participants was developed and conducted. By analyzing the survey, important factors were found for buying and for using furniture considering new technological inventions (e.g. 3D-printing or smart objects), sustainability of the products and the production process. Furthermore, the potential customer-group and their usage of the do-it-together process and additional activities can be tightened

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science

IoT big data value map : how to generate value from IoT data

Huge sources of business value are emerging due to big data generated by the Internet of Things (IoT) technologies paired with Machine Learning (ML) and Data Mining (DM) techniques' ability to harness and extract hidden knowledge from data and consequently learning and improving spontaneously. This paper reviews different examples of analyzing big data generated through IoT in previous studies and in various domains; then claims their business Value Proposition Map deploying Value Proposition Canvas as a novel conceptual tool. As a result, the proposed unprecedented framework of this paper entitled "IoT Big Data Value Map" shows a roadmap from raw data to real-world business value creation, blossomed out of a kind of three-pillar structure: IoT, Data Mining, and Value Proposition Map. The result of this study paves the way for prototyping business models in this field based on value invention from huge data analysis generated by IoT devices in different industries. Furthermore, researchers may complete this map by associating proposed framework with potential customers' profile and their expectations