Integration of Multiple-Modality Sensor Data and Artificial Intelligence

2021 ◽  
Author(s):  
Hong Lu ◽  
Jintao Liu
Keyword(s):  
Artificial Intelligence ◽  
Sensor Data ◽  
Multiple Modality

Object Detection in Fog Computing Using Machine Learning Algorithms

2020 ◽  
pp. 90-107
Author(s):  
Peyakunta Bhargavi ◽  
Singaraju Jyothi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Object Detection ◽  
Fog Computing ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Core Network ◽  
The Core ◽  
The Moment ◽  
Algorithmic Approaches

The moment we live in today demands the convergence of the cloud computing, fog computing, machine learning, and IoT to explore new technological solutions. Fog computing is an emerging architecture intended for alleviating the network burdens at the cloud and the core network by moving resource-intensive functionalities such as computation, communication, storage, and analytics closer to the end users. Machine learning is a subfield of computer science and is a type of artificial intelligence (AI) that provides machines with the ability to learn without explicit programming. IoT has the ability to make decisions and take actions autonomously based on algorithmic sensing to acquire sensor data. These embedded capabilities will range across the entire spectrum of algorithmic approaches that is associated with machine learning. Here the authors explore how machine learning methods have been used to deploy the object detection, text detection in an image, and incorporated for better fulfillment of requirements in fog computing.

scholarly journals Alkalmazott mesterséges intelligencia felhasználási területei és biztonsági kérdései – Mesterséges intelligencia a gyakorlatban

2020 ◽  
Vol 1 (1) ◽  
pp. 35-42
Author(s):  
Péter Ekler ◽  
Dániel Pásztor
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Anomaly Detection ◽  
Real Time ◽  
Learning Algorithm ◽  
Sensor Data ◽  
Test Field ◽  
Real Time System ◽  
Data Set ◽  
Wide Range

Összefoglalás. A mesterséges intelligencia az elmúlt években hatalmas fejlődésen ment keresztül, melynek köszönhetően ma már rengeteg különböző szakterületen megtalálható valamilyen formában, rengeteg kutatás szerves részévé vált. Ez leginkább az egyre inkább fejlődő tanulóalgoritmusoknak, illetve a Big Data környezetnek köszönhető, mely óriási mennyiségű tanítóadatot képes szolgáltatni. A cikk célja, hogy összefoglalja a technológia jelenlegi állapotát. Ismertetésre kerül a mesterséges intelligencia történelme, az alkalmazási területek egy nagyobb része, melyek központi eleme a mesterséges intelligencia. Ezek mellett rámutat a mesterséges intelligencia különböző biztonsági réseire, illetve a kiberbiztonság területén való felhasználhatóságra. A cikk a jelenlegi mesterséges intelligencia alkalmazások egy szeletét mutatja be, melyek jól illusztrálják a széles felhasználási területet. Summary. In the past years artificial intelligence has seen several improvements, which drove its usage to grow in various different areas and became the focus of many researches. This can be attributed to improvements made in the learning algorithms and Big Data techniques, which can provide tremendous amount of training. The goal of this paper is to summarize the current state of artificial intelligence. We present its history, introduce the terminology used, and show technological areas using artificial intelligence as a core part of their applications. The paper also introduces the security concerns related to artificial intelligence solutions but also highlights how the technology can be used to enhance security in different applications. Finally, we present future opportunities and possible improvements. The paper shows some general artificial intelligence applications that demonstrate the wide range usage of the technology. Many applications are built around artificial intelligence technologies and there are many services that a developer can use to achieve intelligent behavior. The foundation of different approaches is a well-designed learning algorithm, while the key to every learning algorithm is the quality of the data set that is used during the learning phase. There are applications that focus on image processing like face detection or other gesture detection to identify a person. Other solutions compare signatures while others are for object or plate number detection (for example the automatic parking system of an office building). Artificial intelligence and accurate data handling can be also used for anomaly detection in a real time system. For example, there are ongoing researches for anomaly detection at the ZalaZone autonomous car test field based on the collected sensor data. There are also more general applications like user profiling and automatic content recommendation by using behavior analysis techniques. However, the artificial intelligence technology also has security risks needed to be eliminated before applying an application publicly. One concern is the generation of fake contents. These must be detected with other algorithms that focus on small but noticeable differences. It is also essential to protect the data which is used by the learning algorithm and protect the logic flow of the solution. Network security can help to protect these applications. Artificial intelligence can also help strengthen the security of a solution as it is able to detect network anomalies and signs of a security issue. Therefore, the technology is widely used in IT security to prevent different type of attacks. As different BigData technologies, computational power, and storage capacity increase over time, there is space for improved artificial intelligence solution that can learn from large and real time data sets. The advancements in sensors can also help to give more precise data for different solutions. Finally, advanced natural language processing can help with communication between humans and computer based solutions.

scholarly journals Data-Driven Modeling of Smartphone-Based Electrochemiluminescence Sensor Data Using Artificial Intelligence

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 625 ◽  
Author(s):  
Elmer Ccopa Rivera ◽  
Jonathan J. Swerdlow ◽  
Rodney L. Summerscales ◽  
Padma P. Tadi Uppala ◽  
Rubens Maciel Filho ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Point Of Care ◽  
Low Cost ◽  
Data Driven ◽  
Sensor Data ◽  
Detection Range ◽  
Multimodal Data ◽  
Key Features ◽  
Predicted Values ◽  
Data Driven Modeling

Understanding relationships among multimodal data extracted from a smartphone-based electrochemiluminescence (ECL) sensor is crucial for the development of low-cost point-of-care diagnostic devices. In this work, artificial intelligence (AI) algorithms such as random forest (RF) and feedforward neural network (FNN) are used to quantitatively investigate the relationships between the concentration of   Ru ( bpy ) 3 2 + luminophore and its experimentally measured ECL and electrochemical data. A smartphone-based ECL sensor with   Ru ( bpy ) 3 2 + /TPrA was developed using disposable screen-printed carbon electrodes. ECL images and amperograms were simultaneously obtained following 1.2-V voltage application. These multimodal data were analyzed by RF and FNN algorithms, which allowed the prediction of   Ru ( bpy ) 3 2 + concentration using multiple key features. High correlation (0.99 and 0.96 for RF and FNN, respectively) between actual and predicted values was achieved in the detection range between 0.02 µM and 2.5 µM. The AI approaches using RF and FNN were capable of directly inferring the concentration of   Ru ( bpy ) 3 2 + using easily observable key features. The results demonstrate that data-driven AI algorithms are effective in analyzing the multimodal ECL sensor data. Therefore, these AI algorithms can be an essential part of the modeling arsenal with successful application in ECL sensor data modeling.

scholarly journals From Trash to Cash: How Blockchain and Multi-Sensor-Driven Artificial Intelligence Can Transform Circular Economy of Plastic Waste?

2020 ◽  
Vol 10 (2) ◽  
pp. 23 ◽  
Author(s):  
Aditya Chidepatil ◽  
Prabhleen Bindra ◽  
Devyani Kulkarni ◽  
Mustafa Qazi ◽  
Meghana Kshirsagar ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Data Fusion ◽  
Circular Economy ◽  
Plastic Waste ◽  
Sensor Data ◽  
Chemical Parameters ◽  
Multi Sensor Data Fusion ◽  
Physico Chemical ◽  
Share Data ◽  
Recycled Plastics

Virgin polymers based on petrochemical feedstock are mainly preferred by most plastic goods manufacturers instead of recycled plastic feedstock. Major reason for this is the lack of reliable information about the quality, suitability, and availability of recycled plastics, which is partly due to lack of proper segregation techniques. In this paper, we present our ongoing efforts to segregate plastics based on its types and improve the reliability of information about recycled plastics using the first-of-its-kind blockchain smart contracts powered by multi-sensor data-fusion algorithms using artificial intelligence. We have demonstrated how different data-fusion modes can be employed to retrieve various physico-chemical parameters of plastic waste for accurate segregation. We have discussed how these smart tools help in efficiently segregating commingled plastics and can be reliably used in the circular economy of plastic. Using these tools, segregators, recyclers, and manufacturers can reliably share data, plan the supply chain, execute purchase orders, and hence, finally increase the use of recycled plastic feedstock.

Liquid Pipeline Rupture Detection Using Multiple Artificial Intelligence Classifiers During Steady-State and Transient Operations

2020 ◽  
Author(s):  
Christopher MacDonald ◽  
Michael Yang ◽  
Shawn Learn ◽  
Ron Hugo ◽  
Simon Park
Keyword(s):  
Artificial Intelligence ◽  
Training Data ◽  
Sensor Data ◽  
Data Sets ◽  
Data Set ◽  
Transient Model ◽  
Rule Based ◽  
Detection Systems ◽  
Pump Station ◽  
Neuro Fuzzy

Abstract There are several challenges associated with existing rupture detection systems such as their inability to accurately detect during transient (such as pump dynamics) conditions, delayed responses and their inability to transfer models to different pipeline configurations easily. To address these challenges, we employ multiple Artificial Intelligence (AI) classifiers that rely on pattern recognitions instead of traditional operator-set thresholds. AI techniques, consisting of two-dimensional (2D) Convolutional Neural Networks (CNN) and Adaptive Neuro Fuzzy Interface Systems (ANFIS), are used to mimic processes performed by operators during a rupture event. This includes both visualization (using CNN) and rule-based decision making (using ANFIS). The system provides a level of reasoning to an operator through the use of the rule-based AI system. Pump station sensor data is non-dimensionalized prior to AI processing, enabling application to pipeline configurations outside of the training data set. AI algorithms undergo testing and training using two data sets: laboratory-collected data that mimics transient pump-station operations and real operator data that includes Real Time Transient Model (RTTM) simulated ruptures. The use of non-dimensional sensor data enables the system to detect ruptures from pipeline data not used in the training process.

Artificial Intelligence in Psychomotor Learning: Modeling Human Motion from Inertial Sensor Data

2019 ◽  
Vol 28 (04) ◽  
pp. 1940006 ◽  
Author(s):  
Olga C. Santos
Keyword(s):  
Artificial Intelligence ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Musical Instrument ◽  
Human Motion ◽  
Learning Performance ◽  
Sensor Data ◽  
Psychomotor Skills ◽  
Motion Modeling ◽  
Psychomotor Learning

Recent trends in educational technology focus on designing systems that can support students while learning complex psychomotor skills, such as those required when practicing sports and martial arts, dancing or playing a musical instrument. In this context, artificial intelligence can be key to personalize the development of these psychomotor skills by enabling the provision of effective feedback when the instructor is not present, or scaling up to a larger pool of students the feedback that an instructor would typically provide one-on-one. This paper presents the modeling of human motion gathered with inertial sensors aimed to offer a personalized support to students when learning complex psychomotor skills. In particular, when comparing learner data with those of an expert during the psychomotor learning process, artificial intelligence algorithms can allow to: (i) recognize specific motion learning units and (ii) assess learning performance in a motion unit. However, it seems that this field is still emerging, since when reviewed systematically, search results hardly included the motion modeling with artificial intelligence techniques of complex human activities measured with inertial sensors.

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