Pub Date : 2019-10-01DOI: 10.1109/ICCCIS48478.2019.8974491
Shyam Mohan Parashar, L. G, M. Pande, Er. Jagvir Singh
This work is focused on placement and sizing of capacitors with effectiveness to maintain voltage profile and power savings.. A fuzzy expert system is considered for finding optimal location & size of capacitors. This scheme is tested on IEEE 14 bus system. The Data is analyzed for voltage profile, active and reactive power savings. The evaluation of performance for load flow on base load, active power losses and minimum voltage are considered. The bus is compensated with reactive power injection equivalent to self reactive load, then checked Power loss Index, Voltage profile, Active power and Reactive power losses for each cases. The most suitable size and location of the capacitor is achieved under proposed scheme. It shows a significant improvement for maintaining system voltage and frequency. Net savings of energy in percentage due to compensation is calculated.
{"title":"Flexible Capacitor Placement To Manage Disaster In Distributed Generation: A Fuzzy Technique","authors":"Shyam Mohan Parashar, L. G, M. Pande, Er. Jagvir Singh","doi":"10.1109/ICCCIS48478.2019.8974491","DOIUrl":"https://doi.org/10.1109/ICCCIS48478.2019.8974491","url":null,"abstract":"This work is focused on placement and sizing of capacitors with effectiveness to maintain voltage profile and power savings.. A fuzzy expert system is considered for finding optimal location & size of capacitors. This scheme is tested on IEEE 14 bus system. The Data is analyzed for voltage profile, active and reactive power savings. The evaluation of performance for load flow on base load, active power losses and minimum voltage are considered. The bus is compensated with reactive power injection equivalent to self reactive load, then checked Power loss Index, Voltage profile, Active power and Reactive power losses for each cases. The most suitable size and location of the capacitor is achieved under proposed scheme. It shows a significant improvement for maintaining system voltage and frequency. Net savings of energy in percentage due to compensation is calculated.","PeriodicalId":436154,"journal":{"name":"2019 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)","volume":"256 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132806417","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 : 2019-10-01DOI: 10.1109/ICCCIS48478.2019.8974558
Rishab Lamba, Sahil Lamba
Describing an image’s content automatically is a basic issue in artificial intelligence that links computer vision and processing of natural language. Recently, however, less attention has been given to extracting summaries from a set of associated pictures that can provide much better data. This paper presents an abstractive summary model with an Encoder-Decoder hierarchy that simultaneously sums up a gallery of pictures and matches phrases and pictures in summaries. The model is designed in order to enhance the probability of the destination identification sentence given the teaching picture. The precision of the model and the fluency of the language learned so only from image descriptions are demonstrated in experiments on various datasets. Our model is often quite precise and we check it in qualitative and quantitative terms. A recent study on neural summarization shows the power of the encoder-decoder model for picture and document overview. Experiments demonstrate that our model is better than neural abstraction and extraction techniques by producing better informative summaries of the collection of images.
{"title":"Image Montage Summarization","authors":"Rishab Lamba, Sahil Lamba","doi":"10.1109/ICCCIS48478.2019.8974558","DOIUrl":"https://doi.org/10.1109/ICCCIS48478.2019.8974558","url":null,"abstract":"Describing an image’s content automatically is a basic issue in artificial intelligence that links computer vision and processing of natural language. Recently, however, less attention has been given to extracting summaries from a set of associated pictures that can provide much better data. This paper presents an abstractive summary model with an Encoder-Decoder hierarchy that simultaneously sums up a gallery of pictures and matches phrases and pictures in summaries. The model is designed in order to enhance the probability of the destination identification sentence given the teaching picture. The precision of the model and the fluency of the language learned so only from image descriptions are demonstrated in experiments on various datasets. Our model is often quite precise and we check it in qualitative and quantitative terms. A recent study on neural summarization shows the power of the encoder-decoder model for picture and document overview. Experiments demonstrate that our model is better than neural abstraction and extraction techniques by producing better informative summaries of the collection of images.","PeriodicalId":436154,"journal":{"name":"2019 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132126707","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 : 2019-10-01DOI: 10.1109/ICCCIS48478.2019.8974523
Vardaan Pruthi, Kanika Mittal, Nikhil Sharma, I. Kaushik
WSN can be termed as a collection of dimensionally diffused nodes which are capable of surveilling and analyzing their surroundings. The sensors are delicate, transportable and small in size while being economical at the same time. However, the diffused nature of these networks also exposes them to a variety of security hazards. Hence, ensuring a reliable file exchange in these networks is not an easy job due to various security requirements that must be fulfilled. In this paper we concentrate mainly on network layer threats and their security countermeasures to overcome the scope of intruders to access the information without having any authentication on the network layer. Various network layer intrusions that are discussed here include Sinkhole Attack, Sybil Attack, Wormhole Attack, Selective Forwarding Attack, Blackhole Attack And Hello Flood Attack.
{"title":"Network Layers Threats & its Countermeasures in WSNs","authors":"Vardaan Pruthi, Kanika Mittal, Nikhil Sharma, I. Kaushik","doi":"10.1109/ICCCIS48478.2019.8974523","DOIUrl":"https://doi.org/10.1109/ICCCIS48478.2019.8974523","url":null,"abstract":"WSN can be termed as a collection of dimensionally diffused nodes which are capable of surveilling and analyzing their surroundings. The sensors are delicate, transportable and small in size while being economical at the same time. However, the diffused nature of these networks also exposes them to a variety of security hazards. Hence, ensuring a reliable file exchange in these networks is not an easy job due to various security requirements that must be fulfilled. In this paper we concentrate mainly on network layer threats and their security countermeasures to overcome the scope of intruders to access the information without having any authentication on the network layer. Various network layer intrusions that are discussed here include Sinkhole Attack, Sybil Attack, Wormhole Attack, Selective Forwarding Attack, Blackhole Attack And Hello Flood Attack.","PeriodicalId":436154,"journal":{"name":"2019 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)","volume":"199 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115718637","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 : 2019-10-01DOI: 10.1109/ICCCIS48478.2019.8974479
Riya Sil, Abhishek Roy, B. Bhushan, A. Mazumdar
The advancement of science and technology has facilitated adaptation of human intelligence into its computerized platform for logical analysis of any event. This porting of human intelligence to machine is known as Artificial Intelligence (AI). AI enhances human life since inception with the help of these intelligent machines, human potentials will be augmented in multiple spheres. An enormous improvement in this area of AI has been noticed in the past two decades that has given rise to expert systems. AI has huge impact on different fields of business, engineering, law, medicine, science, weather forecasting, etc. to enhance the quality and efficiency in our day to day life to solve complex problems. For the past few decades, AI has been playing an emerging role in the legal field and will definitely have an effect on the legal practices over the next few years. AI has the potential to analyses legal information based on semantics and make legal predictions from the legal data set, and hence it helps the judiciary system in automation thereby increasing the efficiency within affordable budget. For better understanding of the concept, in this paper authors have performed relevant survey on this field.
{"title":"Artificial Intelligence and Machine Learning based Legal Application: The State-of-the-Art and Future Research Trends","authors":"Riya Sil, Abhishek Roy, B. Bhushan, A. Mazumdar","doi":"10.1109/ICCCIS48478.2019.8974479","DOIUrl":"https://doi.org/10.1109/ICCCIS48478.2019.8974479","url":null,"abstract":"The advancement of science and technology has facilitated adaptation of human intelligence into its computerized platform for logical analysis of any event. This porting of human intelligence to machine is known as Artificial Intelligence (AI). AI enhances human life since inception with the help of these intelligent machines, human potentials will be augmented in multiple spheres. An enormous improvement in this area of AI has been noticed in the past two decades that has given rise to expert systems. AI has huge impact on different fields of business, engineering, law, medicine, science, weather forecasting, etc. to enhance the quality and efficiency in our day to day life to solve complex problems. For the past few decades, AI has been playing an emerging role in the legal field and will definitely have an effect on the legal practices over the next few years. AI has the potential to analyses legal information based on semantics and make legal predictions from the legal data set, and hence it helps the judiciary system in automation thereby increasing the efficiency within affordable budget. For better understanding of the concept, in this paper authors have performed relevant survey on this field.","PeriodicalId":436154,"journal":{"name":"2019 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121868020","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 : 2019-10-01DOI: 10.1109/ICCCIS48478.2019.8974485
A. Tiwari, G. Ramakrishna, L. Sharma, S. Kashyap
A hybrid data mining algorithm is presented in this paper. This hybridization is considered the neural network and genetic algorithm. Academic information contains the finite hidden information. This hidden information can be useful for the further planning in academics. There is definitely a link with the real information and predicted information. The functional dependence and independence are reviewed in this paper. Basically, this paper presents a study of student’s academic performance based on Neural Network and its optimization by Genetic Algorithm. Neural network is formulated by probabilistic approach and genetic algorithm is generalised by discrete distribution of variables. Hence a system is developed to predict academic information, which can be applied in various applications of academic development.
{"title":"Neural Network and Genetic Algorithm based Hybrid Data Mining Algorithm (Hybrid Data Mining Algorithm)","authors":"A. Tiwari, G. Ramakrishna, L. Sharma, S. Kashyap","doi":"10.1109/ICCCIS48478.2019.8974485","DOIUrl":"https://doi.org/10.1109/ICCCIS48478.2019.8974485","url":null,"abstract":"A hybrid data mining algorithm is presented in this paper. This hybridization is considered the neural network and genetic algorithm. Academic information contains the finite hidden information. This hidden information can be useful for the further planning in academics. There is definitely a link with the real information and predicted information. The functional dependence and independence are reviewed in this paper. Basically, this paper presents a study of student’s academic performance based on Neural Network and its optimization by Genetic Algorithm. Neural network is formulated by probabilistic approach and genetic algorithm is generalised by discrete distribution of variables. Hence a system is developed to predict academic information, which can be applied in various applications of academic development.","PeriodicalId":436154,"journal":{"name":"2019 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127404179","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 : 2019-10-01DOI: 10.1109/ICCCIS48478.2019.8974487
A. K. Sah, Srijana Bhusal, Sunidhi Amatya, Madhusudan Mainali, S. Shakya
Dermatological diseases rate has been increasing for past few decades. Most of these diseases tend to pass on from one person to another and are also based on visual perspectives, the dermatological diseases of one kind found on one part of the body might look different on another part of the body and diseases of different kinds on one part might look similar on other body parts.Therefore, it should be taken into account at initial stages to prevent it from spreading. So, in this paper, we proposed a system to classify such diseases of 10 different classes containing 5500 images obtained from the Dermnet dataset. The proposed system consists of 2 parts- image processing and transfer learning for training of dermatological images. The image processing part deals with image augmentation and removal of unwanted elements, which is found to be necessary before further processing, else it will affect the output efficiency. And transfer learning part deals with features extractions and fine tuning of pre-trained VGG16 model. The validation accuracy is found of be 74.1% and by further fine tuning is found to be 76.3%, when tested on those dataset. The accuracy can be improved further if more training images data are used.
{"title":"Dermatological Diseases Classification using Image Processing and Deep Neural Network","authors":"A. K. Sah, Srijana Bhusal, Sunidhi Amatya, Madhusudan Mainali, S. Shakya","doi":"10.1109/ICCCIS48478.2019.8974487","DOIUrl":"https://doi.org/10.1109/ICCCIS48478.2019.8974487","url":null,"abstract":"Dermatological diseases rate has been increasing for past few decades. Most of these diseases tend to pass on from one person to another and are also based on visual perspectives, the dermatological diseases of one kind found on one part of the body might look different on another part of the body and diseases of different kinds on one part might look similar on other body parts.Therefore, it should be taken into account at initial stages to prevent it from spreading. So, in this paper, we proposed a system to classify such diseases of 10 different classes containing 5500 images obtained from the Dermnet dataset. The proposed system consists of 2 parts- image processing and transfer learning for training of dermatological images. The image processing part deals with image augmentation and removal of unwanted elements, which is found to be necessary before further processing, else it will affect the output efficiency. And transfer learning part deals with features extractions and fine tuning of pre-trained VGG16 model. The validation accuracy is found of be 74.1% and by further fine tuning is found to be 76.3%, when tested on those dataset. The accuracy can be improved further if more training images data are used.","PeriodicalId":436154,"journal":{"name":"2019 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128827353","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 : 2019-10-01DOI: 10.1109/ICCCIS48478.2019.8974501
A. Tiwari, G. Ramakrishna, L. Sharma, S. Kashyap
The possibilistic mean is reviewed in this paper for prediction of academic data. The mean values of the probabilistic study of the possibilistic mean is classified by fuzzy numbers is the main result of this paper. This result is applied on the prediction of the academic performance over the academic data. Basically, this paper presents an analysis of academic data by fuzzy numbers. The variance of fuzzy numbers classes the big data into dynamic and compact data. This system performs efficiently over the various characteristic of fuzzy numbers. The illustration is also presented in this paper.
{"title":"New Data Mining Method based on Probabilistic-Possibilistic-Mean (Discrete Data Mining Algorithm)","authors":"A. Tiwari, G. Ramakrishna, L. Sharma, S. Kashyap","doi":"10.1109/ICCCIS48478.2019.8974501","DOIUrl":"https://doi.org/10.1109/ICCCIS48478.2019.8974501","url":null,"abstract":"The possibilistic mean is reviewed in this paper for prediction of academic data. The mean values of the probabilistic study of the possibilistic mean is classified by fuzzy numbers is the main result of this paper. This result is applied on the prediction of the academic performance over the academic data. Basically, this paper presents an analysis of academic data by fuzzy numbers. The variance of fuzzy numbers classes the big data into dynamic and compact data. This system performs efficiently over the various characteristic of fuzzy numbers. The illustration is also presented in this paper.","PeriodicalId":436154,"journal":{"name":"2019 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126926655","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 : 2019-10-01DOI: 10.1109/ICCCIS48478.2019.8974561
L. Reddy, Swaarrnnaa K Kucuhcihbihbohtloat
Stress emotion recognition from speech utterances seeks a humongous attention among the researchers. Stress is one of the serious problems in the current society. Due to this people in different sectors are suffering from many severe health issues which may lead to huge economic damage. In this paper a detailed survey has been made on the recognition of stress emotion of speech samples which addresses two important areas. The primary one deals with the suitable databases which are used for stress emotion recognition. The later one deals with the choice of best features and also the classifiers along with their performances used for stress emotion recognition. Conclusion deals with the performance evaluations and limitations of the stress emotion recognition system.
{"title":"Survey on Stress Emotion Recognition in Speech","authors":"L. Reddy, Swaarrnnaa K Kucuhcihbihbohtloat","doi":"10.1109/ICCCIS48478.2019.8974561","DOIUrl":"https://doi.org/10.1109/ICCCIS48478.2019.8974561","url":null,"abstract":"Stress emotion recognition from speech utterances seeks a humongous attention among the researchers. Stress is one of the serious problems in the current society. Due to this people in different sectors are suffering from many severe health issues which may lead to huge economic damage. In this paper a detailed survey has been made on the recognition of stress emotion of speech samples which addresses two important areas. The primary one deals with the suitable databases which are used for stress emotion recognition. The later one deals with the choice of best features and also the classifiers along with their performances used for stress emotion recognition. Conclusion deals with the performance evaluations and limitations of the stress emotion recognition system.","PeriodicalId":436154,"journal":{"name":"2019 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)","volume":"1329 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113994878","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 : 2019-10-01DOI: 10.1109/icccis48478.2019.8974520
{"title":"ICCCIS 2019 Organizing Committee","authors":"","doi":"10.1109/icccis48478.2019.8974520","DOIUrl":"https://doi.org/10.1109/icccis48478.2019.8974520","url":null,"abstract":"","PeriodicalId":436154,"journal":{"name":"2019 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)","volume":"9 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130159738","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 : 2019-10-01DOI: 10.1109/ICCCIS48478.2019.8974534
Vijayant Verma, Abhishek Badholia, S. Kashyap
In this paper, the wireless network is studied through probability-matrix theory. The channel capacity is formulated by this theory. The trace representation of the transmission of the information through the wireless network is studied over the logarithmic-binary formulation. The wireless network characterized by the binary sets then the information transmits over the noisy networks. The input and output of the information processed by coding thus the possibility of the error is studied by the probability.
{"title":"Wireless Network System Based on Discrete Probability : (Wireless Network System)","authors":"Vijayant Verma, Abhishek Badholia, S. Kashyap","doi":"10.1109/ICCCIS48478.2019.8974534","DOIUrl":"https://doi.org/10.1109/ICCCIS48478.2019.8974534","url":null,"abstract":"In this paper, the wireless network is studied through probability-matrix theory. The channel capacity is formulated by this theory. The trace representation of the transmission of the information through the wireless network is studied over the logarithmic-binary formulation. The wireless network characterized by the binary sets then the information transmits over the noisy networks. The input and output of the information processed by coding thus the possibility of the error is studied by the probability.","PeriodicalId":436154,"journal":{"name":"2019 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134555662","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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