Pub Date : 2019-09-01DOI: 10.1109/ICICT46931.2019.8977715
S. Singh, H. Darbari, Ajai Kumar, Shikha Jain, Anu Lohan
Artificial intelligence is getting shrewd in real time with massive amounts of computational power and high in demand in last quarter more than billion dollars. The Intensification of Neural Networks in Machine learning playing major revolution in Machine Translation (MT). Looking same aspect, we have prepared a paper on Machine Translation for Hindi using Neural Machine Translation techniques. The Basis of this paper is to translate the source language into target language with the help of sentence structure for source language (English sentence) and corresponding reordering rules for target language (Hindi sentence) using a deep neural network (DNN).When the source input that is English sentences are exactly matched with the target language database then the suitable translation for related input is directly fetched from the database. The fuzzy matching between the input sentence and the sample sentence in the database is done with cosine similarity between the sentence structure of input sentence and the sample sentence and then the reordering rule of the most alike sentence to the input sentence is used for translation.Extracting the structure of the source language sentence and extracting the reordering rule for target sentence is the core part of the translator. Reordering of words in spite of that remains one of the complex problems.It is exciting to implement Deep Neural Network and fuzzy logic to build intelligent reordering rules machine learning system.
{"title":"Overview of Neural Machine Translation for English-Hindi","authors":"S. Singh, H. Darbari, Ajai Kumar, Shikha Jain, Anu Lohan","doi":"10.1109/ICICT46931.2019.8977715","DOIUrl":"https://doi.org/10.1109/ICICT46931.2019.8977715","url":null,"abstract":"Artificial intelligence is getting shrewd in real time with massive amounts of computational power and high in demand in last quarter more than billion dollars. The Intensification of Neural Networks in Machine learning playing major revolution in Machine Translation (MT). Looking same aspect, we have prepared a paper on Machine Translation for Hindi using Neural Machine Translation techniques. The Basis of this paper is to translate the source language into target language with the help of sentence structure for source language (English sentence) and corresponding reordering rules for target language (Hindi sentence) using a deep neural network (DNN).When the source input that is English sentences are exactly matched with the target language database then the suitable translation for related input is directly fetched from the database. The fuzzy matching between the input sentence and the sample sentence in the database is done with cosine similarity between the sentence structure of input sentence and the sample sentence and then the reordering rule of the most alike sentence to the input sentence is used for translation.Extracting the structure of the source language sentence and extracting the reordering rule for target sentence is the core part of the translator. Reordering of words in spite of that remains one of the complex problems.It is exciting to implement Deep Neural Network and fuzzy logic to build intelligent reordering rules machine learning system.","PeriodicalId":412668,"journal":{"name":"2019 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125248699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-01DOI: 10.1109/ICICT46931.2019.8977719
Somsankar Mookherji, Siddhant Patil
With a paradigm shift of focus towards user behavior in today’s E-commerce driven contemporary world there is a need for an efficient and portable learning methodology for machines so as to serve a particular user or a fraternity of users collectively based on knowledge acquired on interests of a pool of users. However, any data driven or data sourced Engine can be contaminated by redundant entries or bogus feedback to jack up or scale down a particular commodity on offer so as to facilitate the promotion of another one in it’s stead. This calls for the implementation of a Neural Network which familiarizes itself with the most popular user choices by virtue of user provided ratings and Machine Learning uses a Support Vector Machine model for filtering time-stamps and IP addresses associated with each user interaction with the system to nullify attempts at manipulating said user feedback or rating by miscreants that could affect recommendation of popular choices explicitly.A Neural Network using 12 Neurons representing various dimensions of a user commodity in form a theme or a colour coded scheme is used on which a rating system is laden for user to provide feedback. This feedback is used to provide unsupervised learning. The monitoring of time-stamps and IP addresses of each user feedback is done by using a supervised learning technique. This makes the model a semi-supervised one in it’s entirety which is the best approach. The Neural Network furthermore adopts a layered learning approach where it uses the ratings provided by the users to learn which fore-ground colour contrasts best with which background colour.In this research a comprehensive study of 10 relevant papers has been made to highlight and discover the scope of research and key challenges to the already existing systems in employment by various entities to serve a similar purpose.
{"title":"User Feedback based Recommendation Engine using Neural Network","authors":"Somsankar Mookherji, Siddhant Patil","doi":"10.1109/ICICT46931.2019.8977719","DOIUrl":"https://doi.org/10.1109/ICICT46931.2019.8977719","url":null,"abstract":"With a paradigm shift of focus towards user behavior in today’s E-commerce driven contemporary world there is a need for an efficient and portable learning methodology for machines so as to serve a particular user or a fraternity of users collectively based on knowledge acquired on interests of a pool of users. However, any data driven or data sourced Engine can be contaminated by redundant entries or bogus feedback to jack up or scale down a particular commodity on offer so as to facilitate the promotion of another one in it’s stead. This calls for the implementation of a Neural Network which familiarizes itself with the most popular user choices by virtue of user provided ratings and Machine Learning uses a Support Vector Machine model for filtering time-stamps and IP addresses associated with each user interaction with the system to nullify attempts at manipulating said user feedback or rating by miscreants that could affect recommendation of popular choices explicitly.A Neural Network using 12 Neurons representing various dimensions of a user commodity in form a theme or a colour coded scheme is used on which a rating system is laden for user to provide feedback. This feedback is used to provide unsupervised learning. The monitoring of time-stamps and IP addresses of each user feedback is done by using a supervised learning technique. This makes the model a semi-supervised one in it’s entirety which is the best approach. The Neural Network furthermore adopts a layered learning approach where it uses the ratings provided by the users to learn which fore-ground colour contrasts best with which background colour.In this research a comprehensive study of 10 relevant papers has been made to highlight and discover the scope of research and key challenges to the already existing systems in employment by various entities to serve a similar purpose.","PeriodicalId":412668,"journal":{"name":"2019 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116129534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-01DOI: 10.1109/ICICT46931.2019.8977629
Tanmay Sankhe, Pranav Puranik, Masira Mulla
Gesture regulated systems controlled by smart wearables have spearheaded the next epoch in human machine interaction. Gestures being intuitive and expressive are a more convenient way of communication. However, for developing a gesture controlled system, we need to accurately detect fingertips. In this paper, we present a fingertip detection system that can be efficiently used by smart wearables. This approach is free of markers and centroid-based techniques which are traditionally used to detect fingertips.The system’s functionality is controlled by the number of fingertips in the frame. We collated a customized dataset, ‘1-2-3-4 Hands’, which contained the images of different hands gesturing using one to four fingers. Using Faster RCNN with Inception v2 module, we trained this dataset to build a model capable of recognizing any of the first four fingertips, excluding the thumb. The count of fingertips is used to perform an action in real-time gesture controlled systems. Finally, we have implemented finger-control solutions such as AirWriting, AirDrawing, and Gaming Controls and enlisted their benefits.
{"title":"Futuristic Finger and its Modern Day Applications","authors":"Tanmay Sankhe, Pranav Puranik, Masira Mulla","doi":"10.1109/ICICT46931.2019.8977629","DOIUrl":"https://doi.org/10.1109/ICICT46931.2019.8977629","url":null,"abstract":"Gesture regulated systems controlled by smart wearables have spearheaded the next epoch in human machine interaction. Gestures being intuitive and expressive are a more convenient way of communication. However, for developing a gesture controlled system, we need to accurately detect fingertips. In this paper, we present a fingertip detection system that can be efficiently used by smart wearables. This approach is free of markers and centroid-based techniques which are traditionally used to detect fingertips.The system’s functionality is controlled by the number of fingertips in the frame. We collated a customized dataset, ‘1-2-3-4 Hands’, which contained the images of different hands gesturing using one to four fingers. Using Faster RCNN with Inception v2 module, we trained this dataset to build a model capable of recognizing any of the first four fingertips, excluding the thumb. The count of fingertips is used to perform an action in real-time gesture controlled systems. Finally, we have implemented finger-control solutions such as AirWriting, AirDrawing, and Gaming Controls and enlisted their benefits.","PeriodicalId":412668,"journal":{"name":"2019 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121898397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-01DOI: 10.1109/ICICT46931.2019.8977652
A. Khan, Ajat Shatru Arora
the most common cancer among the women is breast cancer with very high mortality rate accounting for about 7% of the all cancer deaths (1). Though very nominal, the men too can have the chances of developing the breast cancer. The early detection can be boon for survival chances of the patients. Though Mammography is commonly accepted screening tool technique for breast cancer detection. But the thermography has the advantage of the early detection of the cancer when no masses are formed to be detected by the mammography. Moreover, mammography is a painful procedure and patient is exposedto harmful X-rays. The thermography is based on the asymmetry between affected and the normal breasts due to increased blood flow in the cancerous cells. This results in the difference in the temperature profile of the two breasts which is detected with the help of thermal imagers. The texture of bothbreasts are obtained withGabor texturefeatures. The features that can contribute to the classification are selected from the feature space of the all Gabor features extracted. Finally, the classification of the thermograms into healthy and sickcases are done using ensemble classifier. The accuracy obtained in his paper using selected Gabor features and ensemble classifier is 92.55%.
{"title":"Classification in Thermograms for Breast Cancer Detection using Texture Features with Feature Selection Method and Ensemble Classifier","authors":"A. Khan, Ajat Shatru Arora","doi":"10.1109/ICICT46931.2019.8977652","DOIUrl":"https://doi.org/10.1109/ICICT46931.2019.8977652","url":null,"abstract":"the most common cancer among the women is breast cancer with very high mortality rate accounting for about 7% of the all cancer deaths (1). Though very nominal, the men too can have the chances of developing the breast cancer. The early detection can be boon for survival chances of the patients. Though Mammography is commonly accepted screening tool technique for breast cancer detection. But the thermography has the advantage of the early detection of the cancer when no masses are formed to be detected by the mammography. Moreover, mammography is a painful procedure and patient is exposedto harmful X-rays. The thermography is based on the asymmetry between affected and the normal breasts due to increased blood flow in the cancerous cells. This results in the difference in the temperature profile of the two breasts which is detected with the help of thermal imagers. The texture of bothbreasts are obtained withGabor texturefeatures. The features that can contribute to the classification are selected from the feature space of the all Gabor features extracted. Finally, the classification of the thermograms into healthy and sickcases are done using ensemble classifier. The accuracy obtained in his paper using selected Gabor features and ensemble classifier is 92.55%.","PeriodicalId":412668,"journal":{"name":"2019 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128601321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-01DOI: 10.1109/ICICT46931.2019.8977685
N. Kumari, V. Bhargava
This is studied and implemented in MATLAB Simulink on the ability of think about problems and decisions in a reasonable way without exaggerating of electric vehicles specifically for hybrid energy storage system. It formally suggest something as a possible to plan for hybrid electric storage system consists of lithium ion battery and super capacitor to charge the electric vehicle. The control system is designed using Artificial Neural Network to improve something the results obtained using the PI controlled techniques. It reduces the calculation complexity of the system by reducing the values of Kp and Ki calculations. The neural network promotes self-learning capability of the system and also improves the system by reducing any fluctuations if any. In this paper, we study and implement techniques for design of Hybrid Energy Storage System for Electric Vehicles.
{"title":"Artificial Neural Network","authors":"N. Kumari, V. Bhargava","doi":"10.1109/ICICT46931.2019.8977685","DOIUrl":"https://doi.org/10.1109/ICICT46931.2019.8977685","url":null,"abstract":"This is studied and implemented in MATLAB Simulink on the ability of think about problems and decisions in a reasonable way without exaggerating of electric vehicles specifically for hybrid energy storage system. It formally suggest something as a possible to plan for hybrid electric storage system consists of lithium ion battery and super capacitor to charge the electric vehicle. The control system is designed using Artificial Neural Network to improve something the results obtained using the PI controlled techniques. It reduces the calculation complexity of the system by reducing the values of Kp and Ki calculations. The neural network promotes self-learning capability of the system and also improves the system by reducing any fluctuations if any. In this paper, we study and implement techniques for design of Hybrid Energy Storage System for Electric Vehicles.","PeriodicalId":412668,"journal":{"name":"2019 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128582037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-01DOI: 10.1109/ICICT46931.2019.8977678
Deboshree Seal, A. P. Shukla, Ankur Bhardwaj
The ultrasound medical field are more favored in clinical diagnosis than further imaging mode, because it is conveyable, suitable, harmless and economic nature of it. The images are acquired and per-processed as this step plays a critical role in clinical diagnosis. The acquisition of the image may sometimes include various types of noise. In this paper we will work upon the speckle noise that may leads to suppression of the pixel quality and dissimilarity of the image as it is intrinsic in nature. The lower quality of the image, the more is the hurdle for the better extrication of features, identification, study and resolution of edges[1]. Because if the image is not properly captured due to the noise, that may result in inappropriate diagnosis by doctor. Under this paper, the work has been done by imposing various filters have been over the pictures, with different speckle values. And thereafter the image quality parameters are taken into considerations to compare the values of imposing filters over the image. The main aim of this paper is to have a comparison of some basic filters on various images.
{"title":"Effect of Iterative Variations on Despeckling Filters in Ultrasound Imaging","authors":"Deboshree Seal, A. P. Shukla, Ankur Bhardwaj","doi":"10.1109/ICICT46931.2019.8977678","DOIUrl":"https://doi.org/10.1109/ICICT46931.2019.8977678","url":null,"abstract":"The ultrasound medical field are more favored in clinical diagnosis than further imaging mode, because it is conveyable, suitable, harmless and economic nature of it. The images are acquired and per-processed as this step plays a critical role in clinical diagnosis. The acquisition of the image may sometimes include various types of noise. In this paper we will work upon the speckle noise that may leads to suppression of the pixel quality and dissimilarity of the image as it is intrinsic in nature. The lower quality of the image, the more is the hurdle for the better extrication of features, identification, study and resolution of edges[1]. Because if the image is not properly captured due to the noise, that may result in inappropriate diagnosis by doctor. Under this paper, the work has been done by imposing various filters have been over the pictures, with different speckle values. And thereafter the image quality parameters are taken into considerations to compare the values of imposing filters over the image. The main aim of this paper is to have a comparison of some basic filters on various images.","PeriodicalId":412668,"journal":{"name":"2019 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129301879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-01DOI: 10.1109/ICICT46931.2019.8977673
J. Singh, Sakshi Arora, Sanjeev Jain, Uday Pratap Singh SoM
Biometric gait has found importance in recognition, security, behavior learning and also in clinical analysis. From a few decades research on gait recognition and dataset available publically focused on a single moving person. But in real time applications (such as shopping malls, railway stations, airport parking, etc.) where people walk in a group and occlusion issue affects the gait recognition performance. Considering this issue, we constructed a new database which focused on Multi-Gait (dynamic) occlusion situation. The dataset is classified into two categories i.e. first, Multi-Gait (MG), subjects walk in a group, and second, Single-Gait (SG), same subjects walk alone. Therefore, the dataset included both occluded and non-occluded gait patterns. The objective of this dataset is to analyze gait pattern variations when a person walks in a group or the same person walk separately. This dataset is also useful for researchers for identification of SG to MG.
{"title":"A Multi-Gait Dataset for Human Recognition under Occlusion Scenario","authors":"J. Singh, Sakshi Arora, Sanjeev Jain, Uday Pratap Singh SoM","doi":"10.1109/ICICT46931.2019.8977673","DOIUrl":"https://doi.org/10.1109/ICICT46931.2019.8977673","url":null,"abstract":"Biometric gait has found importance in recognition, security, behavior learning and also in clinical analysis. From a few decades research on gait recognition and dataset available publically focused on a single moving person. But in real time applications (such as shopping malls, railway stations, airport parking, etc.) where people walk in a group and occlusion issue affects the gait recognition performance. Considering this issue, we constructed a new database which focused on Multi-Gait (dynamic) occlusion situation. The dataset is classified into two categories i.e. first, Multi-Gait (MG), subjects walk in a group, and second, Single-Gait (SG), same subjects walk alone. Therefore, the dataset included both occluded and non-occluded gait patterns. The objective of this dataset is to analyze gait pattern variations when a person walks in a group or the same person walk separately. This dataset is also useful for researchers for identification of SG to MG.","PeriodicalId":412668,"journal":{"name":"2019 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT)","volume":"273 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131921512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-01DOI: 10.1109/ICICT46931.2019.8977716
Rajbala Singh, Deepak Kumar, B. B. Sagar
The immense use of software is changing our society profoundly. Customers rely on the accuracy and functionality that is provided by the software. Agile development is a framework that achieves project goals sooner by involving every stakeholder in the decision process from the very beginning. This research paper exhibits Analytical Hierarchical Process (AHP), for improving the software quality in the organization. Thus this research provides a framework to calculate the critical attributes based on survey done by the experts’ working on agile project development to improve the quality of the software in the organization. Overall utility is calculated to assess in the perspective of the developers as well as stakeholders described as Two Way Assessment, which further decides the best attribute from both perspective to improve the efficiency of the software in the organization.
{"title":"Utility of Process Prioritization for Agile Software Testing using Analytical Hierarchical Process","authors":"Rajbala Singh, Deepak Kumar, B. B. Sagar","doi":"10.1109/ICICT46931.2019.8977716","DOIUrl":"https://doi.org/10.1109/ICICT46931.2019.8977716","url":null,"abstract":"The immense use of software is changing our society profoundly. Customers rely on the accuracy and functionality that is provided by the software. Agile development is a framework that achieves project goals sooner by involving every stakeholder in the decision process from the very beginning. This research paper exhibits Analytical Hierarchical Process (AHP), for improving the software quality in the organization. Thus this research provides a framework to calculate the critical attributes based on survey done by the experts’ working on agile project development to improve the quality of the software in the organization. Overall utility is calculated to assess in the perspective of the developers as well as stakeholders described as Two Way Assessment, which further decides the best attribute from both perspective to improve the efficiency of the software in the organization.","PeriodicalId":412668,"journal":{"name":"2019 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT)","volume":"186 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131954871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-01DOI: 10.1109/ICICT46931.2019.8977665
A. Pandey, Minakshi Chauhan
A large part of the world’s population depends upon agriculture industry for its source of livelihood. The growth of agriculture industry is bound to the two major parameters; quality and quantity yield of the crops. Polyhouse farming is an excellent method which enables the production of crops with minimum resources and efforts and maximum yield by providing a controlled condition environment. Polyhouse is a methodology to grow crops under controlled environment with continuous monitoring and analysis. In this paper, we proposed an automated IOT based smart polyhouse system with Hadoop technologies to bring precision in farming techniques. In order to increase the yield and quality of crops, the proposed non-manual system helps to automate the functionality of polyhouse. This system contains sensors, IOT devices and Hadoop technologies like Apache Hadoop, Flume and Hive. Using these technologies, data from various sensors will be analyzed to help in decision making. It also focuses on incorporating smart irrigation methods and technologies like drip irrigation etc within the conservatory which can assist farmers in preventing damage to their crops due to unnecessary water logging.
{"title":"IOT Based Smart Polyhouse System using Data Analysis","authors":"A. Pandey, Minakshi Chauhan","doi":"10.1109/ICICT46931.2019.8977665","DOIUrl":"https://doi.org/10.1109/ICICT46931.2019.8977665","url":null,"abstract":"A large part of the world’s population depends upon agriculture industry for its source of livelihood. The growth of agriculture industry is bound to the two major parameters; quality and quantity yield of the crops. Polyhouse farming is an excellent method which enables the production of crops with minimum resources and efforts and maximum yield by providing a controlled condition environment. Polyhouse is a methodology to grow crops under controlled environment with continuous monitoring and analysis. In this paper, we proposed an automated IOT based smart polyhouse system with Hadoop technologies to bring precision in farming techniques. In order to increase the yield and quality of crops, the proposed non-manual system helps to automate the functionality of polyhouse. This system contains sensors, IOT devices and Hadoop technologies like Apache Hadoop, Flume and Hive. Using these technologies, data from various sensors will be analyzed to help in decision making. It also focuses on incorporating smart irrigation methods and technologies like drip irrigation etc within the conservatory which can assist farmers in preventing damage to their crops due to unnecessary water logging.","PeriodicalId":412668,"journal":{"name":"2019 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128903795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-01DOI: 10.1109/ICICT46931.2019.8977699
Pravin Kumar, Rajesh Dwivedi, V. Tyagi
Wireless sensor networks contains small sensor node and processor which have several issues and challenges with regards to Limited Computational Capacity, Energy Efficiency, Path Planning Overhead and many more. Entire lifetime of network depends on efficient energy consumption in sensor network so it is a challenging task to design an Energy Efficient Routing Protocol for wireless sensor networks. Wireless sensor nodes dynamically self-organize themselves without utilizing preexisting infrastructure. Problem becomes more complex if some sensor node having mobility for some particular instance. In this paper we proposed an energy efficient routing protocol for WSN motivated by swarm intelligence of rummage behavior of ant and also covered mobility behavior of node. Furthermore we have combined the fuzzy logic with behavior of ant to take a better decision to find optimal path with less energy consumption. Our proposed algorithm utilize the principle of Fuzzy logic in addition of Ant Colony Optimization to develop energy efficient routing. The performance of our proposed work is evaluated on QualNet 7.4 Network Simulator. The simulation result shows that the proposed technique optimize the energy consumption, decrease RREQ packet and increase the lifetime of network as compare to existing routing protocol.
{"title":"Fuzzy Ant Colony Optimization Based Energy Efficient Routing For Mixed Wireless Sensor Network","authors":"Pravin Kumar, Rajesh Dwivedi, V. Tyagi","doi":"10.1109/ICICT46931.2019.8977699","DOIUrl":"https://doi.org/10.1109/ICICT46931.2019.8977699","url":null,"abstract":"Wireless sensor networks contains small sensor node and processor which have several issues and challenges with regards to Limited Computational Capacity, Energy Efficiency, Path Planning Overhead and many more. Entire lifetime of network depends on efficient energy consumption in sensor network so it is a challenging task to design an Energy Efficient Routing Protocol for wireless sensor networks. Wireless sensor nodes dynamically self-organize themselves without utilizing preexisting infrastructure. Problem becomes more complex if some sensor node having mobility for some particular instance. In this paper we proposed an energy efficient routing protocol for WSN motivated by swarm intelligence of rummage behavior of ant and also covered mobility behavior of node. Furthermore we have combined the fuzzy logic with behavior of ant to take a better decision to find optimal path with less energy consumption. Our proposed algorithm utilize the principle of Fuzzy logic in addition of Ant Colony Optimization to develop energy efficient routing. The performance of our proposed work is evaluated on QualNet 7.4 Network Simulator. The simulation result shows that the proposed technique optimize the energy consumption, decrease RREQ packet and increase the lifetime of network as compare to existing routing protocol.","PeriodicalId":412668,"journal":{"name":"2019 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116953875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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