This document proposes a methodology to enhance traffic prediction accuracy by combining historical traffic data, real-time traffic updates, and estimated time of arrival (ETA) information. The methodology utilizes machine learning techniques, ARIMA modeling, nonparametric methods, and deep neural networks to analyze the data. While the methodology lays out a framework for collecting raw traffic congestion data from online maps and transportation departments, the research focuses on establishing a theoretical model rather than conducting empirical experiments. The goal is to develop a comprehensive solution for traffic prediction by leveraging different data sources and analytical techniques.
Big data traffic management in vehicular ad-hoc network IJECEIAES
Today, the world has experienced a new trend with regard to data system management, traditional database management tools have become outdated and they will no longer be able to process the mass of data generated by different systems, that's why big data is there to process this mass of data to bring out crucial information hidden in this data, and without big data technologies the treatment is very difficult to manage; among the domains that uses big data technologies is vehicular ad-hoc network to manage their voluminous data. In this article, we establish in the first step a method that allow to detect anomalies or accidents within the road and compute the time spent in each road section in real time, which permit us to obtain a database having the estimated time spent in all sections in real time, this will serve us to send to the vehicles the right estimated time of arrival all along their journey and the optimal route to attain their destination. This database is useful to utilize it like inputs for machine learning to predict the places and times where the probability of accidents is higher. The experimental results prove that our method permits us to avoid congestions and apportion the load of vehicles in all roads effectively, also it contributes to road safety.
Big data traffic management in vehicular ad-hoc network IJECEIAES
Today, the world has experienced a new trend with regard to data system management, traditional database management tools have become outdated and they will no longer be able to process the mass of data generated by different systems, that's why big data is there to process this mass of data to bring out crucial information hidden in this data, and without big data technologies the treatment is very difficult to manage; among the domains that uses big data technologies is vehicular ad-hoc network to manage their voluminous data. In this article, we establish in the first step a method that allow to detect anomalies or accidents within the road and compute the time spent in each road section in real time, which permit us to obtain a database having the estimated time spent in all sections in real time, this will serve us to send to the vehicles the right estimated time of arrival all along their journey and the optimal route to attain their destination. This database is useful to utilize it like inputs for machine learning to predict the places and times where the probability of accidents is higher. The experimental results prove that our method permits us to avoid congestions and apportion the load of vehicles in all roads effectively, also it contributes to road safety.
APPLICABILITY OF CROWD SOURCING TO DETERMINE THE BEST TRANSPORTATION METHOD B...IJDKP
Traffic is one of the most significant problem in Sri Lanka. Valuable time can be saved if there is a proper way to predict the traffic and recommend the best route considering the time factor and the people’s satisfaction on various transportation methods. Therefore, in this research using location awareness applications installed in mobile devices, data related to user mobility were collected by using crowdsourcing techniques and studied. Based on these observations an algorithm has been developed to overcome the problem. By using this, the best transportation method can be predicted as the results of the research. Therefore, people can choose what will be the best time slots & transportation methods when planning journeys. Throughout this research it has been proven that for the Sri Lankan context, the data mining concepts together with crowdsourcing can be applied to determine the best transportation method.
APPLICABILITY OF CROWD SOURCING TO DETERMINE THE BEST TRANSPORTATION METHOD B...IJDKP
Traffic is one of the most significant problem in Sri Lanka. Valuable time can be saved if there is a proper
way to predict the traffic and recommend the best route considering the time factor and the people’s
satisfaction on various transportation methods. Therefore, in this research using location awareness
applications installed in mobile devices, data related to user mobility were collected by using
crowdsourcing techniques and studied. Based on these observations an algorithm has been developed to
overcome the problem. By using this, the best transportation method can be predicted as the results of the
research. Therefore, people can choose what will be the best time slots & transportation methods when
planning journeys. Throughout this research it has been proven that for the Sri Lankan context, the data mining concepts together with crowdsourcing can be applied to determine the best transportation method.
Approximation of regression-based fault minimization for network trafficTELKOMNIKA JOURNAL
This research associates three distinct approaches for computer network traffic prediction. They are the traditional stochastic gradient descent (SGD) using a few random samplings instead of the complete dataset for each iterative calculation, the gradient descent algorithm (GDA) which is a well-known optimization approach in deep learning, and the proposed method. The network traffic is computed from the traffic load (data and multimedia) of the computer network nodes via the Internet. It is apparent that the SGD is a modest iteration but can conclude suboptimal solutions. The GDA is a complicated one, can function more accurate than the SGD but difficult to manipulate parameters, such as the learning rate, the dataset granularity, and the loss function. Network traffic estimation helps improve performance and lower costs for various applications, such as an adaptive rate control, load balancing, the quality of service (QoS), fair bandwidth allocation, and anomaly detection. The proposed method confirms optimal values out of parameters using simulation to compute the minimum figure of specified loss function in each iteration.
Abstract—This paper provides a brief overview of the Intelligent Traffic Management System based on Artificial
Neural Networks (ANN). It is being utilized to enhance the present traffic management system and human resource
reliance. The most basic problem with the current traffic lights is their dependency on humans for their working.
The technologies used in the making of this automated traffic lights are Internet of Things, Machine Learning and
Artificial Intelligence. The basic steps used in Internet of Things are reported along with different ANN trainings.
This ANN model can be used for the minimization of traffic on roads and less waiting time at traffic lights. As a
result, we can make traffic lights more automated which in turn eventually deceases our dependency on human
resources
Adaptive traffic lights based on traffic flow prediction using machine learni...IJECEIAES
Traffic congestion prediction is one of the essential components of intelligent transport systems (ITS). This is due to the rapid growth of population and, consequently, the high number of vehicles in cities. Nowadays, the problem of traffic congestion attracts more and more attention from researchers in the field of ITS. Traffic congestion can be predicted in advance by analyzing traffic flow data. In this article, we used machine learning algorithms such as linear regression, random forest regressor, decision tree regressor, gradient boosting regressor, and K-neighbor regressor to predict traffic flow and reduce traffic congestion at intersections. We used the public roads dataset from the UK national road traffic to test our models. All machine learning algorithms obtained good performance metrics, indicating that they are valid for implementation in smart traffic light systems. Next, we implemented an adaptive traffic light system based on a random forest regressor model, which adjusts the timing of green and red lights depending on the road width, traffic density, types of vehicles, and expected traffic. Simulations of the proposed system show a 30.8% reduction in traffic congestion, thus justifying its effectiveness and the interest of deploying it to regulate the signaling problem in intersections.
A novel hybrid deep learning approach for tourism demand forecasting IJECEIAES
This paper proposes a new hybrid deep learning framework that combines search query data, autoencoders (AE) and stacked long-short term memory (staked LSTM) to enhance the accuracy of tourism demand prediction. We use data from Google Trends as an additional variable with the monthly tourist arrivals to Marrakech, Morocco. The AE is applied as a feature extraction procedure to dimension reduction, to extract valuable information and to mine the nonlinear information incorporated in data. The extracted features are fed into stacked LSTM to predict tourist arrivals. Experiments carried out to analyze performance in forecast results of proposed method compared to individual models, and different principal component analysis (PCA) based and AE based hybrid models. The experimental results show that the proposed framework outperforms other models.
The increasing need for traffic detection system has become a vital area in both developing and developed
countries. However, it is more important to get the accurate and valuable data to give the better result
about traffic condition. For this reason, this paper proposes an approach of tracking traffic data as cheap
as possible in terms of communication, computation and energy efficient ways by using mobile phone
network. This system gives the information of which vehicles are running on which location and how much
speed for the Traffic Detection System. The GPS sensor of mobile device will be mainly utilized to guess a
user’s transportation mode, then it integrates cloud environment to enhance the limitation of mobile device,
such as storage, energy and computing power. This system includes three main components: Client
Interface, Server process and Cloud Storage. Some tasks are carried out on the Client. Therefore, it greatly
reduces the bottleneck situation on Server side in efficient way. Most of tasks are executed on the Server
and history data are stored on the Cloud Storage. Moreover, the paper mainly uses the distance based
clustering algorithm in grouping mobile devices on the same bus to get the accurate data.
International Journal of Engineering and Science Invention (IJESI)inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online
Trend-Based Networking Driven by Big Data Telemetry for Sdn and Traditional N...josephjonse
Organizations face a challenge of accurately analyzing network data and providing automated action based on the observed trend. This trend-based analytics is beneficial to minimize the downtime and improve the performance of the network services, but organizations use different network management tools to understand and visualize the network traffic with limited abilities to dynamically optimize the network. This research focuses on the development of an intelligent system that leverages big data telemetry analysis in Platform for Network Data Analytics (PNDA) to enable comprehensive trendbased networking decisions. The results include a graphical user interface (GUI) done via a web application for effortless management of all subsystems, and the system and application developed in this research demonstrate the true potential for a scalable system capable of effectively benchmarking the network to set the expected behavior for comparison and trend analysis. Moreover, this research provides a proof of concept of how trend analysis results are actioned in both a traditional network and a software-defined network (SDN) to achieve dynamic, automated load balancing.
TREND-BASED NETWORKING DRIVEN BY BIG DATA TELEMETRY FOR SDN AND TRADITIONAL N...ijngnjournal
Organizations face a challenge of accurately analyzing network data and providing automated action
based on the observed trend. This trend-based analytics is beneficial to minimize the downtime and
improve the performance of the network services, but organizations use different network management
tools to understand and visualize the network traffic with limited abilities to dynamically optimize the
network. This research focuses on the development of an intelligent system that leverages big data
telemetry analysis in Platform for Network Data Analytics (PNDA) to enable comprehensive trendbased networking decisions. The results include a graphical user interface (GUI) done via a web
application for effortless management of all subsystems, and the system and application developed in
this research demonstrate the true potential for a scalable system capable of effectively benchmarking
the network to set the expected behavior for comparison and trend analysis. Moreover, this research
provides a proof of concept of how trend analysis results are actioned in both a traditional network and
a software-defined network (SDN) to achieve dynamic, automated load balancing.
Trend-Based Networking Driven by Big Data Telemetry for Sdn and Traditional N...josephjonse
Organizations face a challenge of accurately analyzing network data and providing automated action based on the observed trend. This trend-based analytics is beneficial to minimize the downtime and improve the performance of the network services, but organizations use different network management tools to understand and visualize the network traffic with limited abilities to dynamically optimize the network. This research focuses on the development of an intelligent system that leverages big data telemetry analysis in Platform for Network Data Analytics (PNDA) to enable comprehensive trendbased networking decisions. The results include a graphical user interface (GUI) done via a web application for effortless management of all subsystems, and the system and application developed in this research demonstrate the true potential for a scalable system capable of effectively benchmarking the network to set the expected behavior for comparison and trend analysis. Moreover, this research provides a proof of concept of how trend analysis results are actioned in both a traditional network and a software-defined network (SDN) to achieve dynamic, automated load balancing
APPLICABILITY OF CROWD SOURCING TO DETERMINE THE BEST TRANSPORTATION METHOD B...IJDKP
Traffic is one of the most significant problem in Sri Lanka. Valuable time can be saved if there is a proper way to predict the traffic and recommend the best route considering the time factor and the people’s satisfaction on various transportation methods. Therefore, in this research using location awareness applications installed in mobile devices, data related to user mobility were collected by using crowdsourcing techniques and studied. Based on these observations an algorithm has been developed to overcome the problem. By using this, the best transportation method can be predicted as the results of the research. Therefore, people can choose what will be the best time slots & transportation methods when planning journeys. Throughout this research it has been proven that for the Sri Lankan context, the data mining concepts together with crowdsourcing can be applied to determine the best transportation method.
APPLICABILITY OF CROWD SOURCING TO DETERMINE THE BEST TRANSPORTATION METHOD B...IJDKP
Traffic is one of the most significant problem in Sri Lanka. Valuable time can be saved if there is a proper
way to predict the traffic and recommend the best route considering the time factor and the people’s
satisfaction on various transportation methods. Therefore, in this research using location awareness
applications installed in mobile devices, data related to user mobility were collected by using
crowdsourcing techniques and studied. Based on these observations an algorithm has been developed to
overcome the problem. By using this, the best transportation method can be predicted as the results of the
research. Therefore, people can choose what will be the best time slots & transportation methods when
planning journeys. Throughout this research it has been proven that for the Sri Lankan context, the data mining concepts together with crowdsourcing can be applied to determine the best transportation method.
Approximation of regression-based fault minimization for network trafficTELKOMNIKA JOURNAL
This research associates three distinct approaches for computer network traffic prediction. They are the traditional stochastic gradient descent (SGD) using a few random samplings instead of the complete dataset for each iterative calculation, the gradient descent algorithm (GDA) which is a well-known optimization approach in deep learning, and the proposed method. The network traffic is computed from the traffic load (data and multimedia) of the computer network nodes via the Internet. It is apparent that the SGD is a modest iteration but can conclude suboptimal solutions. The GDA is a complicated one, can function more accurate than the SGD but difficult to manipulate parameters, such as the learning rate, the dataset granularity, and the loss function. Network traffic estimation helps improve performance and lower costs for various applications, such as an adaptive rate control, load balancing, the quality of service (QoS), fair bandwidth allocation, and anomaly detection. The proposed method confirms optimal values out of parameters using simulation to compute the minimum figure of specified loss function in each iteration.
Abstract—This paper provides a brief overview of the Intelligent Traffic Management System based on Artificial
Neural Networks (ANN). It is being utilized to enhance the present traffic management system and human resource
reliance. The most basic problem with the current traffic lights is their dependency on humans for their working.
The technologies used in the making of this automated traffic lights are Internet of Things, Machine Learning and
Artificial Intelligence. The basic steps used in Internet of Things are reported along with different ANN trainings.
This ANN model can be used for the minimization of traffic on roads and less waiting time at traffic lights. As a
result, we can make traffic lights more automated which in turn eventually deceases our dependency on human
resources
Adaptive traffic lights based on traffic flow prediction using machine learni...IJECEIAES
Traffic congestion prediction is one of the essential components of intelligent transport systems (ITS). This is due to the rapid growth of population and, consequently, the high number of vehicles in cities. Nowadays, the problem of traffic congestion attracts more and more attention from researchers in the field of ITS. Traffic congestion can be predicted in advance by analyzing traffic flow data. In this article, we used machine learning algorithms such as linear regression, random forest regressor, decision tree regressor, gradient boosting regressor, and K-neighbor regressor to predict traffic flow and reduce traffic congestion at intersections. We used the public roads dataset from the UK national road traffic to test our models. All machine learning algorithms obtained good performance metrics, indicating that they are valid for implementation in smart traffic light systems. Next, we implemented an adaptive traffic light system based on a random forest regressor model, which adjusts the timing of green and red lights depending on the road width, traffic density, types of vehicles, and expected traffic. Simulations of the proposed system show a 30.8% reduction in traffic congestion, thus justifying its effectiveness and the interest of deploying it to regulate the signaling problem in intersections.
A novel hybrid deep learning approach for tourism demand forecasting IJECEIAES
This paper proposes a new hybrid deep learning framework that combines search query data, autoencoders (AE) and stacked long-short term memory (staked LSTM) to enhance the accuracy of tourism demand prediction. We use data from Google Trends as an additional variable with the monthly tourist arrivals to Marrakech, Morocco. The AE is applied as a feature extraction procedure to dimension reduction, to extract valuable information and to mine the nonlinear information incorporated in data. The extracted features are fed into stacked LSTM to predict tourist arrivals. Experiments carried out to analyze performance in forecast results of proposed method compared to individual models, and different principal component analysis (PCA) based and AE based hybrid models. The experimental results show that the proposed framework outperforms other models.
The increasing need for traffic detection system has become a vital area in both developing and developed
countries. However, it is more important to get the accurate and valuable data to give the better result
about traffic condition. For this reason, this paper proposes an approach of tracking traffic data as cheap
as possible in terms of communication, computation and energy efficient ways by using mobile phone
network. This system gives the information of which vehicles are running on which location and how much
speed for the Traffic Detection System. The GPS sensor of mobile device will be mainly utilized to guess a
user’s transportation mode, then it integrates cloud environment to enhance the limitation of mobile device,
such as storage, energy and computing power. This system includes three main components: Client
Interface, Server process and Cloud Storage. Some tasks are carried out on the Client. Therefore, it greatly
reduces the bottleneck situation on Server side in efficient way. Most of tasks are executed on the Server
and history data are stored on the Cloud Storage. Moreover, the paper mainly uses the distance based
clustering algorithm in grouping mobile devices on the same bus to get the accurate data.
International Journal of Engineering and Science Invention (IJESI)inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online
Trend-Based Networking Driven by Big Data Telemetry for Sdn and Traditional N...josephjonse
Organizations face a challenge of accurately analyzing network data and providing automated action based on the observed trend. This trend-based analytics is beneficial to minimize the downtime and improve the performance of the network services, but organizations use different network management tools to understand and visualize the network traffic with limited abilities to dynamically optimize the network. This research focuses on the development of an intelligent system that leverages big data telemetry analysis in Platform for Network Data Analytics (PNDA) to enable comprehensive trendbased networking decisions. The results include a graphical user interface (GUI) done via a web application for effortless management of all subsystems, and the system and application developed in this research demonstrate the true potential for a scalable system capable of effectively benchmarking the network to set the expected behavior for comparison and trend analysis. Moreover, this research provides a proof of concept of how trend analysis results are actioned in both a traditional network and a software-defined network (SDN) to achieve dynamic, automated load balancing.
TREND-BASED NETWORKING DRIVEN BY BIG DATA TELEMETRY FOR SDN AND TRADITIONAL N...ijngnjournal
Organizations face a challenge of accurately analyzing network data and providing automated action
based on the observed trend. This trend-based analytics is beneficial to minimize the downtime and
improve the performance of the network services, but organizations use different network management
tools to understand and visualize the network traffic with limited abilities to dynamically optimize the
network. This research focuses on the development of an intelligent system that leverages big data
telemetry analysis in Platform for Network Data Analytics (PNDA) to enable comprehensive trendbased networking decisions. The results include a graphical user interface (GUI) done via a web
application for effortless management of all subsystems, and the system and application developed in
this research demonstrate the true potential for a scalable system capable of effectively benchmarking
the network to set the expected behavior for comparison and trend analysis. Moreover, this research
provides a proof of concept of how trend analysis results are actioned in both a traditional network and
a software-defined network (SDN) to achieve dynamic, automated load balancing.
Trend-Based Networking Driven by Big Data Telemetry for Sdn and Traditional N...josephjonse
Organizations face a challenge of accurately analyzing network data and providing automated action based on the observed trend. This trend-based analytics is beneficial to minimize the downtime and improve the performance of the network services, but organizations use different network management tools to understand and visualize the network traffic with limited abilities to dynamically optimize the network. This research focuses on the development of an intelligent system that leverages big data telemetry analysis in Platform for Network Data Analytics (PNDA) to enable comprehensive trendbased networking decisions. The results include a graphical user interface (GUI) done via a web application for effortless management of all subsystems, and the system and application developed in this research demonstrate the true potential for a scalable system capable of effectively benchmarking the network to set the expected behavior for comparison and trend analysis. Moreover, this research provides a proof of concept of how trend analysis results are actioned in both a traditional network and a software-defined network (SDN) to achieve dynamic, automated load balancing
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
Contact with Dawood Bhai Just call on +92322-6382012 and we'll help you. We'll solve all your problems within 12 to 24 hours and with 101% guarantee and with astrology systematic. If you want to take any personal or professional advice then also you can call us on +92322-6382012 , ONLINE LOVE PROBLEM & Other all types of Daily Life Problem's.Then CALL or WHATSAPP us on +92322-6382012 and Get all these problems solutions here by Amil Baba DAWOOD BANGALI
#vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore#blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #blackmagicforlove #blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #Amilbabainuk #amilbabainspain #amilbabaindubai #Amilbabainnorway #amilbabainkrachi #amilbabainlahore #amilbabaingujranwalan #amilbabainislamabad
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.