IoT Assisted Machine Learning Model for Warehouse Management

2021 ◽  
Author(s):  
Lanjing Wang ◽  
Abdulsattar Abdullah Hamad ◽  
V. Sakthivel
Keyword(s):  
Machine Learning ◽  
Organizational Performance ◽  
Fair Use ◽  
Management Systems ◽  
Warehouse Management ◽  
Dangerous Goods ◽  
Machine Performance ◽  
Digital World ◽  
Machine Learning Model ◽  
The Internet Of Things

In the digital world of today, any enterprise that deals with the amounts of data in Warehouse Management Systems (WMS) are an important component. Furthermore, the amount of data being raisedand its complexity have become more challenging to maintain the WMS efficiency. Therefore, a device is required, which can manage such complexities autonomously with no human intervention. In this paper, Hybrid Machine Learning with the Internet of Things (HML-IoT) improves isolated doors. Furthermore, operating machine performance in the factory of hazardous goods. Decision-Making Algorithm (DMA) Data from the customer’s holding space’s dangerous goods warehouses shall be checked using separated doors. Thispaper’s significant aspect is that inventory and inventory operation’s organizational performance can be increased, further logistics costs minimized utilizing the fair use of isolated doors. Finally, the HML-IoT model integrated hazardous goods warehouse with isolated doors has been contrasted with the current one, demonstrating that the previous one has greater efficacy.

Adapting Warehouse Management Systems to the Requirements of the Evolving Era of Industry 4.0

2017 ◽  
Author(s):  
Cyril Alias ◽  
Udo Salewski ◽  
Viviana Elizabeth Ortiz Ruiz ◽  
Frank Eduardo Alarcón Olalla ◽  
José do Egypto Neirão Reymão ◽  
...  
Keyword(s):  
Information Systems ◽  
Technological Innovation ◽  
Processing Speed ◽  
Industrial Revolution ◽  
Heterogeneous Data ◽  
Management Systems ◽  
Sources Of Information ◽  
Warehouse Management ◽  
Data Types ◽  
Digital World

With global megatrends like automation and digitization changing societies, economies, and ultimately businesses, shift is underway, disrupting current business plans and entire industries. Business actors have accordingly developed an instinctive fear of economic decline and realized the necessity of taking adequate measures to keep up with the times. Increasingly, organizations find themselves in an evolve-or-die race with their success depending on their capability of recognizing the requirements for serving a specific market and adopting those requirements accurately into their own structure. In the transportation and logistics sector, emerging technological and information challenges are reflected in fierce competition from within and outside. Especially, processes and supporting information systems are put to the test when technological innovation start to spread among an increasing number of actors and promise higher performance or lower cost. As to warehousing, technological innovation continuously finds its way into the premises of the heterogeneous warehouse operators, leading to modifications and process improvements. Such innovation can be at the side of the hardware equipment or in the form of new software solutions. Particularly, the fourth industrial revolution is globally underway. Same applies to Future Internet technologies, a European term for innovative software technologies and the research upon them. On the one hand, new hardware solutions using robotics, cyber-physical systems and sensors, and advanced materials are constantly put to widespread use. On the other one, software solutions based on intensified digitization including new and more heterogeneous sources of information, higher volumes of data, and increasing processing speed are also becoming an integral part of popular information systems for warehouses, particularly for warehouse management systems. With a rapidly and dynamically changing environment and new legal and business requirements towards processes in the warehouses and supporting information systems, new performance levels in terms of quality and cost of service are to be obtained. For this purpose, new expectations of the functionality of warehouse management systems need to be derived. While introducing wholly new solutions is one option, retrofitting and adapting existing systems to the new requirements is another one. The warehouse management systems will need to deal with more types of data from new and heterogeneous data sources. Also, it will need to connect to innovative machines and represent their respective operating principles. In both scenarios, systems need to satisfy the demand for new features in order to remain capable of processing information and acting and, thereby, to optimize logistics processes in real time. By taking a closer look at an industrial use case of a warehouse management system, opportunities of incorporating such new requirements are presented as the system adapts to new data types, increased processing speed, and new machines and equipment used in the warehouse. Eventually, the present paper proves the adaptability of existing warehouse management systems to the requirements of the new digital world, and viable methods to adopt the necessary renovation processes.

scholarly journals Agrocompanion: A Smart Farming Approach Based on Iot and Machine Learning

2020 ◽  
Vol 9 (12) ◽  
pp. 254-262
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Random Forest ◽  
Weather Conditions ◽  
The Internet ◽  
Machine Learning Model ◽  
Smart Farming ◽  
Water Tanks ◽  
And Control ◽  
The Internet Of Things

Agriculture is one of the cardinal sectors of the Indian Economy. The proposed system offers a methodology to efficiently monitor and control various attributes that affect crop growth and production. The system also uses machine learning along with the Internet of Things (IoT) to predict the crop yield. Various weather conditions such as temperature, humidity, and soil moisture are monitored in real-time using IoT sensors. IoT is also used to regulate the water level in the water tanks, which helps in reducing the wastage of water resources. A machine learning model is developed to predict the yield of the crop based on parameters taken from these sensors. The model uses Random Forest Regressor and gives an accuracy of 87.5%. Such a system provides a simple and efficient way to maintain and monitor the health of the crop.

WAREHOUSE MANAGEMENT SYSTEMS: RESOURCE COMMITMENT, CAPABILITIES, AND ORGANIZATIONAL PERFORMANCE

2005 ◽  
Vol 26 (2) ◽  
pp. 165-183 ◽  
Author(s):  
Chad W. Autry ◽  
Stanley E. Griffis ◽  
Thomas J. Goldsby ◽  
L. Michelle Bobbitt
Keyword(s):  
Organizational Performance ◽  
Management Systems ◽  
Warehouse Management ◽  
Resource Commitment

Anomaly Detection in IoT Frameworks Using Machine Learning

2020 ◽  
pp. 88-112
Author(s):  
Phidahunlang Chyne ◽  
Parag Chatterjee ◽  
Sugata Sanyal ◽  
Debdatta Kandar
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Anomaly Detection ◽  
Physical World ◽  
The Internet ◽  
Network Transport ◽  
Machine Learning Model ◽  
The World ◽  
Iot Devices ◽  
The Internet Of Things

Rapid advancements in hardware programming and communication innovations have encouraged the development of internet-associated sensory devices that give perceptions and information measurements from the physical world. According to the internet of things (IoT) analytics, more than 100 IoT devices across the world connect to the internet every second, which in the coming years will sharply increase the number of IoT devices by billions. This number of IoT devices incorporates new dynamic associations and does not totally replace the devices that were purchased before yet are not utilized any longer. As an increasing number of IoT devices advance into the world, conveyed in uncontrolled, complex, and frequently hostile conditions, securing IoT frameworks displays various challenges. As per the Eclipse IoT Working Group's 2017 IoT engineer overview, security is the top worry for IoT designers. To approach the challenges in securing IoT devices, the authors propose using unsupervised machine learning model at the network/transport level for anomaly detection.

Machine Learning Accelerated Genetic Algorithms for Computational Materials Search

2018 ◽  
Author(s):  
Steen Lysgaard ◽  
Paul C. Jennings ◽  
Jens Strabo Hummelshøj ◽  
Thomas Bligaard ◽  
Tejs Vegge
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Au Nanoparticles ◽  
Learning Model ◽  
Energy Calculations ◽  
Atomic Distribution ◽  
Machine Learning Model ◽  
Fold Reduction ◽  
Computational Materials

A machine learning model is used as a surrogate fitness evaluator in a genetic algorithm (GA) optimization of the atomic distribution of Pt-Au nanoparticles. The machine learning accelerated genetic algorithm (MLaGA) yields a 50-fold reduction of required energy calculations compared to a traditional GA.

BAND NN: A Deep Learning Framework For Energy Prediction and Geometry Optimization of Organic Small Molecules

2019 ◽  
Author(s):  
Siddhartha Laghuvarapu ◽  
Yashaswi Pathak ◽  
U. Deva Priyakumar
Keyword(s):  
Machine Learning ◽  
Density Functional ◽  
Computational Cost ◽  
Geometry Optimization ◽  
Dft Methods ◽  
Energy Prediction ◽  
Machine Learning Model ◽  
Equilibrium Structures ◽  
High Level ◽  
Non Equilibrium

Recent advances in artificial intelligence along with development of large datasets of energies calculated using quantum mechanical (QM)/density functional theory (DFT) methods have enabled prediction of accurate molecular energies at reasonably low computational cost. However, machine learning models that have been reported so far requires the atomic positions obtained from geometry optimizations using high level QM/DFT methods as input in order to predict the energies, and do not allow for geometry optimization. In this paper, a transferable and molecule-size independent machine learning model (BAND NN) based on a chemically intuitive representation inspired by molecular mechanics force fields is presented. The model predicts the atomization energies of equilibrium and non-equilibrium structures as sum of energy contributions from bonds (B), angles (A), nonbonds (N) and dihedrals (D) at remarkable accuracy. The robustness of the proposed model is further validated by calculations that span over the conformational, configurational and reaction space. The transferability of this model on systems larger than the ones in the dataset is demonstrated by performing calculations on select large molecules. Importantly, employing the BAND NN model, it is possible to perform geometry optimizations starting from non-equilibrium structures along with predicting their energies.

Detecting Abnormal Behavior of an IoT Device in the Network Based on a Traffic Model

Telecom IT ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 50-55
Author(s):  
D. Saharov ◽  
D. Kozlov
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Mobile Networks ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Abnormal Behavior ◽  
Traffic Model ◽  
The Internet ◽  
Wide Spread ◽  
The Internet Of Things

The article deals with the СoAP Protocol that regulates the transmission and reception of information traf-fic by terminal devices in IoT networks. The article describes a model for detecting abnormal traffic in 5G/IoT networks using machine learning algorithms, as well as the main methods for solving this prob-lem. The relevance of the article is due to the wide spread of the Internet of things and the upcoming update of mobile networks to the 5g generation.

Tendencies of development of modern innovative technologies and the digital economy

2018 ◽  
Vol 35 (2) ◽  
pp. 40-47
Author(s):  
S. M. Doguchaeva
Keyword(s):  
Internet Of Things ◽  
Digital Economy ◽  
Digital Transformation ◽  
The Internet ◽  
Successful Development ◽  
Innovative Technologies ◽  
Digital World ◽  
Blockchain Technology ◽  
The Internet Of Things

The era of digital transformation provides the opportunity for leading companies to change priorities - to begin to take care of the support environment using innovative technologies and become a leading creative platform open for innovation. The successful development of the digital world, the blockchain technology, the Internet of things – the mechanism which will change the financial world. 

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