S. Tripathy, Sisira Kumar Kapat, R. Patro, Susant Kumar Das
This paper focuses to present malware propagation mathematically and it also shows how the population of malware grows in a well-defined ultra-large sized network. The propagation refers to entry of a malware to a system as well as copying malware from one device to another in networked environment. We assumed the network to be SNS. We have also discussed the closure properties used by malware while propagation. The properties are quite useful to detect and avoid malware. We have calculated the number of infected system in a geometrically progressed system without defense and modified the equation to calculate the number of infected system for practical network.
{"title":"A Comprehensive Study of Malware Propagation Using Geometric Progression","authors":"S. Tripathy, Sisira Kumar Kapat, R. Patro, Susant Kumar Das","doi":"10.1109/CINE.2017.31","DOIUrl":"https://doi.org/10.1109/CINE.2017.31","url":null,"abstract":"This paper focuses to present malware propagation mathematically and it also shows how the population of malware grows in a well-defined ultra-large sized network. The propagation refers to entry of a malware to a system as well as copying malware from one device to another in networked environment. We assumed the network to be SNS. We have also discussed the closure properties used by malware while propagation. The properties are quite useful to detect and avoid malware. We have calculated the number of infected system in a geometrically progressed system without defense and modified the equation to calculate the number of infected system for practical network.","PeriodicalId":236972,"journal":{"name":"2017 3rd International Conference on Computational Intelligence and Networks (CINE)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132851158","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}
Anshul Chauhan, Sagar Verma, Shilpi Sharma, T. Choudhury
Healthcare Companion is an android application which provide symptoms of many diseases along with their treatment and location of the doctor which is specialized for that particular diseases. Healthcare companion will also provide the diet which is best suited to recover from that particular illness. Along with all of these basic facilities the application will also provide an emergency button that if pressed will alert the nearest Hospital facility along with the hospital's location. Therefore this application can be used by anyone who has a smartphone with internet facility. This application will prove to be very useful in today's world where diseases are spreading like a wildfire. The application also consist of a Panic Alert feature which will send the location and emergency message to any five selected person if user is in any trouble. Along with that user can also determine disease from the symptoms and then can see if he/she can treat the disease on its own or need help of a doctor. To accomplish the location of the hospital as well as the user's location information both geocoding and reverse geocoding were used along with location API's
{"title":"Healthcare Information Management System Using Android OS","authors":"Anshul Chauhan, Sagar Verma, Shilpi Sharma, T. Choudhury","doi":"10.1109/CINE.2017.29","DOIUrl":"https://doi.org/10.1109/CINE.2017.29","url":null,"abstract":"Healthcare Companion is an android application which provide symptoms of many diseases along with their treatment and location of the doctor which is specialized for that particular diseases. Healthcare companion will also provide the diet which is best suited to recover from that particular illness. Along with all of these basic facilities the application will also provide an emergency button that if pressed will alert the nearest Hospital facility along with the hospital's location. Therefore this application can be used by anyone who has a smartphone with internet facility. This application will prove to be very useful in today's world where diseases are spreading like a wildfire. The application also consist of a Panic Alert feature which will send the location and emergency message to any five selected person if user is in any trouble. Along with that user can also determine disease from the symptoms and then can see if he/she can treat the disease on its own or need help of a doctor. To accomplish the location of the hospital as well as the user's location information both geocoding and reverse geocoding were used along with location API's","PeriodicalId":236972,"journal":{"name":"2017 3rd International Conference on Computational Intelligence and Networks (CINE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126139164","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}
An artificial neural network based algorithm is proposed for building occupancy detection using the signals from various sensors such as temperature, light, CO2, humidity etc is proposed in this work. The input to the feed forward neural network is the data collected from several sensors. The output of the network is set to '0' for building not occupied and '1' for building occupied. The training algorithm used in this work is Lavenberg Marquardt algorithm. The accuracy of the proposed method is found to be 95.6% for occupancy detection. Occupancy detection is a necessary factor for building energy management.
{"title":"Building Occupancy Detection Using Feed Forward Back-Propagation Neural Networks","authors":"Sushmita Das, A. Swetapadma, C. Panigrahi","doi":"10.1109/CINE.2017.12","DOIUrl":"https://doi.org/10.1109/CINE.2017.12","url":null,"abstract":"An artificial neural network based algorithm is proposed for building occupancy detection using the signals from various sensors such as temperature, light, CO2, humidity etc is proposed in this work. The input to the feed forward neural network is the data collected from several sensors. The output of the network is set to '0' for building not occupied and '1' for building occupied. The training algorithm used in this work is Lavenberg Marquardt algorithm. The accuracy of the proposed method is found to be 95.6% for occupancy detection. Occupancy detection is a necessary factor for building energy management.","PeriodicalId":236972,"journal":{"name":"2017 3rd International Conference on Computational Intelligence and Networks (CINE)","volume":"145 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113959363","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}
Database security has become a prime concern in today's internet world due to the escalation of various web applications and information systems. Ensuring the security of the back-end databases is highly essential for maintaining the confidentiality and integrity of the stored sensitive information. In this paper, a Density-based clustering technique, namely, OPTICS, has been applied for constructing the normal profile of users. Each incoming transaction either lies within a cluster or is found to deviate from the clusters based on its Local Outlier Factor value. The transactions observed as outliers are further verified by employing various supervised machine learning techniques individually – Naïve Bayes, Decision Tree, Rule Induction, k-Nearest Neighbor and Radial Basis Function Network. The effectiveness of our system is demonstrated by carrying out extensive experimentations and comparative analysis using stochastic models.
{"title":"Use of OPTICS and Supervised Learning Methods for Database Intrusion Detection","authors":"Sharmila Subudhi, T. Behera, S. Panigrahi","doi":"10.1109/CINE.2017.10","DOIUrl":"https://doi.org/10.1109/CINE.2017.10","url":null,"abstract":"Database security has become a prime concern in today's internet world due to the escalation of various web applications and information systems. Ensuring the security of the back-end databases is highly essential for maintaining the confidentiality and integrity of the stored sensitive information. In this paper, a Density-based clustering technique, namely, OPTICS, has been applied for constructing the normal profile of users. Each incoming transaction either lies within a cluster or is found to deviate from the clusters based on its Local Outlier Factor value. The transactions observed as outliers are further verified by employing various supervised machine learning techniques individually – Naïve Bayes, Decision Tree, Rule Induction, k-Nearest Neighbor and Radial Basis Function Network. The effectiveness of our system is demonstrated by carrying out extensive experimentations and comparative analysis using stochastic models.","PeriodicalId":236972,"journal":{"name":"2017 3rd International Conference on Computational Intelligence and Networks (CINE)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132694882","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}
Cancer subtyping is a key step towards the design of improved personalized therapies. Subtype discovery from large-scale multimodal data sets poses several challenges like data heterogeneity and high dimensionality. Moreover, existing integrative clustering algorithms tend to consider that each modality provides homogeneous and consistent subtype information, which may not be true for real life omics data sets. In this regard, this paper presents a fast algorithm to extract a low-rank joint subspace from the principal subspace of each individual modality such that the joint subspace best preserves the underlying subtype structure. The algorithm evaluates the quality of cluster information provided by each modality and the concordance of information shared among different modalities. This allows the algorithm to judiciously select the most relevant modalities and discard modalities providing noisy and inconsistent information while construction of the joint subspace. The performance of clustering in the joint subspace extracted by the proposed algorithm and its computational efficiency is compared with several existing integrative clustering approaches, on real life multimodal omics data sets. Moreover, survival analysis shows that the subtypes identified by the proposed approach have significantly different survival profiles.
{"title":"Principal Subspace Updation for Integrative Clustering of Multimodal Omics Data","authors":"Aparajita Khan, P. Maji","doi":"10.1109/CINE.2017.14","DOIUrl":"https://doi.org/10.1109/CINE.2017.14","url":null,"abstract":"Cancer subtyping is a key step towards the design of improved personalized therapies. Subtype discovery from large-scale multimodal data sets poses several challenges like data heterogeneity and high dimensionality. Moreover, existing integrative clustering algorithms tend to consider that each modality provides homogeneous and consistent subtype information, which may not be true for real life omics data sets. In this regard, this paper presents a fast algorithm to extract a low-rank joint subspace from the principal subspace of each individual modality such that the joint subspace best preserves the underlying subtype structure. The algorithm evaluates the quality of cluster information provided by each modality and the concordance of information shared among different modalities. This allows the algorithm to judiciously select the most relevant modalities and discard modalities providing noisy and inconsistent information while construction of the joint subspace. The performance of clustering in the joint subspace extracted by the proposed algorithm and its computational efficiency is compared with several existing integrative clustering approaches, on real life multimodal omics data sets. Moreover, survival analysis shows that the subtypes identified by the proposed approach have significantly different survival profiles.","PeriodicalId":236972,"journal":{"name":"2017 3rd International Conference on Computational Intelligence and Networks (CINE)","volume":"48 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122522137","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}
Tushar Sharma, Tarun Nalwa, T. Choudhury, S. C. Satapathy, Praveen Kumar
Today innovation is enhancing every day in various viewpoints so as to give adaptable and safe development to the general population. Outwardly disabled individuals discover troubles recognizing obstacles before them, amid strolling in the road, which makes it unsafe. The smart cane has a system that empower them to see and differentiate the obstacles. Moving with the assistance of a white cane is a slippery errand for the outwardly tested. The smart cane is equipped with infrared and ultrasonic sensor that helps in detecting the obstacles. At the base of the smart cane there is a water sensor which detects and dodges puddles. When it recognizes any obstacle, it activates the sound system and the vibration motor. On detecting obstructions the sensor passes this information to the micro-controller. The micro-controller then procedures this information and computes if the object is sufficiently close. In the event that the object is not that close the circuit does nothing. In the event that the object is close the micro-controller sends a signal to sound a buzzer. GPS system provides the information about the current location in case of an emergency.
{"title":"Smart Cane: Better Walking Experience for Blind People","authors":"Tushar Sharma, Tarun Nalwa, T. Choudhury, S. C. Satapathy, Praveen Kumar","doi":"10.1109/CINE.2017.22","DOIUrl":"https://doi.org/10.1109/CINE.2017.22","url":null,"abstract":"Today innovation is enhancing every day in various viewpoints so as to give adaptable and safe development to the general population. Outwardly disabled individuals discover troubles recognizing obstacles before them, amid strolling in the road, which makes it unsafe. The smart cane has a system that empower them to see and differentiate the obstacles. Moving with the assistance of a white cane is a slippery errand for the outwardly tested. The smart cane is equipped with infrared and ultrasonic sensor that helps in detecting the obstacles. At the base of the smart cane there is a water sensor which detects and dodges puddles. When it recognizes any obstacle, it activates the sound system and the vibration motor. On detecting obstructions the sensor passes this information to the micro-controller. The micro-controller then procedures this information and computes if the object is sufficiently close. In the event that the object is not that close the circuit does nothing. In the event that the object is close the micro-controller sends a signal to sound a buzzer. GPS system provides the information about the current location in case of an emergency.","PeriodicalId":236972,"journal":{"name":"2017 3rd International Conference on Computational Intelligence and Networks (CINE)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130634199","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}
R. Das, Manisha Panda, Nirupama Mahapatra, S. Dash
Data mining is one of the most significant ways of extracting the important information from a required set of data. Now-a-days the healthcare systems generate a very large amount of data, which are difficult to analyze through traditional methods. Data mining techniques provide the technology to extract meaningful information from these huge healthcare data for decision making. This paper mainly focuses on the analysis and evaluation of different parameters from large healthcare datasets, using Artificial Immune System (AIS) based classification algorithms, and normal classification algorithms. Five life science based datasets focusing on healthcare are considered for our experiment, to evaluate different parameters, using AIS based and normal classification algorithms. The result of the experiment is analyzed to propose the best classifier among the considered algorithms, based on the factors like accuracy, sensitivity, F-measure and specificity. The proposed classifier can further be used for different decision making purposes in healthcare systems.
{"title":"Application of Artificial Immune System Algorithms on Healthcare Data","authors":"R. Das, Manisha Panda, Nirupama Mahapatra, S. Dash","doi":"10.1109/CINE.2017.32","DOIUrl":"https://doi.org/10.1109/CINE.2017.32","url":null,"abstract":"Data mining is one of the most significant ways of extracting the important information from a required set of data. Now-a-days the healthcare systems generate a very large amount of data, which are difficult to analyze through traditional methods. Data mining techniques provide the technology to extract meaningful information from these huge healthcare data for decision making. This paper mainly focuses on the analysis and evaluation of different parameters from large healthcare datasets, using Artificial Immune System (AIS) based classification algorithms, and normal classification algorithms. Five life science based datasets focusing on healthcare are considered for our experiment, to evaluate different parameters, using AIS based and normal classification algorithms. The result of the experiment is analyzed to propose the best classifier among the considered algorithms, based on the factors like accuracy, sensitivity, F-measure and specificity. The proposed classifier can further be used for different decision making purposes in healthcare systems.","PeriodicalId":236972,"journal":{"name":"2017 3rd International Conference on Computational Intelligence and Networks (CINE)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133802771","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}
In the modern world of digitalization, the need to develop expert system in growing tremendously. Most of the expert system perceive environment as image, and for reading the components of image there are various techniques. This paper focuses on artificial intelligence algorithm that can be used to extract features from the image.
{"title":"Artificial Intelligence Techniques Used to Detect Object and Face in an Image: A Review","authors":"Deepika P.U, Shivangi Chauhan, Neetu Narayan","doi":"10.1109/CINE.2017.20","DOIUrl":"https://doi.org/10.1109/CINE.2017.20","url":null,"abstract":"In the modern world of digitalization, the need to develop expert system in growing tremendously. Most of the expert system perceive environment as image, and for reading the components of image there are various techniques. This paper focuses on artificial intelligence algorithm that can be used to extract features from the image.","PeriodicalId":236972,"journal":{"name":"2017 3rd International Conference on Computational Intelligence and Networks (CINE)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115616715","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}
Abhinandan R. Gupta, D. K. Chaudhary, T. Choudhury
Stock exchange that is, buying and selling of stock is considered to be an important factor in the economy sector. The Stockbrokers typically use time series or technical analysis in predicting the stock price. These techniques are based on trends and not the actual stock value. Therefore a method of prediction which takes into account the historical values of stock is desired. Neural Networks once again have become famous for prediction of stock. This is due to their ability to deal with non-linear data. The use of Artificial Neural Networks to for predicting the stock prices is proposed in this paper. The input features to the model sometimes can be non-related to the output. Hence, Functional Link Artificial Neural Networks is used here to increase the number of related features in the form of inputs. The data is taken from NSE and is converted into a suitable form for FLANN and then prediction is carried out using Multi-layer feed forward Perceptron model.
{"title":"Stock Prediction Using Functional Link Artificial Neural Network (FLANN)","authors":"Abhinandan R. Gupta, D. K. Chaudhary, T. Choudhury","doi":"10.1109/CINE.2017.25","DOIUrl":"https://doi.org/10.1109/CINE.2017.25","url":null,"abstract":"Stock exchange that is, buying and selling of stock is considered to be an important factor in the economy sector. The Stockbrokers typically use time series or technical analysis in predicting the stock price. These techniques are based on trends and not the actual stock value. Therefore a method of prediction which takes into account the historical values of stock is desired. Neural Networks once again have become famous for prediction of stock. This is due to their ability to deal with non-linear data. The use of Artificial Neural Networks to for predicting the stock prices is proposed in this paper. The input features to the model sometimes can be non-related to the output. Hence, Functional Link Artificial Neural Networks is used here to increase the number of related features in the form of inputs. The data is taken from NSE and is converted into a suitable form for FLANN and then prediction is carried out using Multi-layer feed forward Perceptron model.","PeriodicalId":236972,"journal":{"name":"2017 3rd International Conference on Computational Intelligence and Networks (CINE)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114184288","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}
Automation of handwritten character recognition is one of the challenging tasks in the problem domain of document analysis. However various writing style in orientation, shape and size are the key factor which affects the offline recognition system of Indian scripts. Here we have used a set of symmetry axes which are perceptually uniquely representing the handwritten Odia characters and numerals as patterns. This empirical model generates two symmetry axes such as row symmetry and column symmetry chords. In the subsequent phase we added up the mid points of both symmetric axis and along with we have reported the angular projection and distance between centre of the image and respective midpoints. Subsequently we have taken the mean values of horizontal and vertical symmetry angular projection values along with the mean of horizontal, vertical distance as the key feature values for the recognition system. We have analyzed overall recognition system with J48 Decision Tree which is considered as a classifier. All the simulation setup was build over upon standard database of NIT RKL Odia handwritten character, ISI Kolkata Odia numeral database. A 6 fold cross validation was performed in order to validate the recognition system. After all the successful simulation work we have noted down very good promising recognition accuracy from the J48 classifier such as 96.2% accuracy upon Odia numeral database and 95.6% upon Odia character database.
{"title":"Symmetric Axis Based Off-Line Odia Handwritten Character and Numeral Recognition","authors":"A. Sethy, P. Patra, S. Nayak, Pyari mohan Jena","doi":"10.1109/CINE.2017.27","DOIUrl":"https://doi.org/10.1109/CINE.2017.27","url":null,"abstract":"Automation of handwritten character recognition is one of the challenging tasks in the problem domain of document analysis. However various writing style in orientation, shape and size are the key factor which affects the offline recognition system of Indian scripts. Here we have used a set of symmetry axes which are perceptually uniquely representing the handwritten Odia characters and numerals as patterns. This empirical model generates two symmetry axes such as row symmetry and column symmetry chords. In the subsequent phase we added up the mid points of both symmetric axis and along with we have reported the angular projection and distance between centre of the image and respective midpoints. Subsequently we have taken the mean values of horizontal and vertical symmetry angular projection values along with the mean of horizontal, vertical distance as the key feature values for the recognition system. We have analyzed overall recognition system with J48 Decision Tree which is considered as a classifier. All the simulation setup was build over upon standard database of NIT RKL Odia handwritten character, ISI Kolkata Odia numeral database. A 6 fold cross validation was performed in order to validate the recognition system. After all the successful simulation work we have noted down very good promising recognition accuracy from the J48 classifier such as 96.2% accuracy upon Odia numeral database and 95.6% upon Odia character database.","PeriodicalId":236972,"journal":{"name":"2017 3rd International Conference on Computational Intelligence and Networks (CINE)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114195161","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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