Pub Date : 2020-10-07DOI: 10.1109/I-SMAC49090.2020.9243407
M. Mohan, M. Kavithadevi, J. V
ElGamal cryptosystem possess many technical challenges such as encryption of large messages, integrity and authentication of data, non-malleability, semantic security etc. This paper proposes an efficient ElGamal based public-key cryptosystem (PKEIE) to achieve the encryption of large messages along with confidentiality, integrity and authentication. The algorithm is built on DDH assumption (Decisional Diffie– Hellman assumption) which is quite hard to break. Apart from the normal key generation, it uses a secret key for the secured hash function to provide integrity and authentication. The algorithm is capable enough to encrypt data of any size which is not possible with the ElGamal cryptosystem. The message to be encrypted is split into blocks of equal size and each block is assigned with a block index. The block indexing makes the scheme a probabilistic one. The algorithm holds semantic security because it is built on the DDH assumption. It assures non-malleability against attacks. The algorithm is compared with the ElGamal variant schemes based on the attained level of security and throughput and suitable for IoT networks by in cooperating integrity, authentication and encryption using a single algorithm.
{"title":"Improved ElGamal Cryptosystem for Secure Data Transfer in IoT Networks","authors":"M. Mohan, M. Kavithadevi, J. V","doi":"10.1109/I-SMAC49090.2020.9243407","DOIUrl":"https://doi.org/10.1109/I-SMAC49090.2020.9243407","url":null,"abstract":"ElGamal cryptosystem possess many technical challenges such as encryption of large messages, integrity and authentication of data, non-malleability, semantic security etc. This paper proposes an efficient ElGamal based public-key cryptosystem (PKEIE) to achieve the encryption of large messages along with confidentiality, integrity and authentication. The algorithm is built on DDH assumption (Decisional Diffie– Hellman assumption) which is quite hard to break. Apart from the normal key generation, it uses a secret key for the secured hash function to provide integrity and authentication. The algorithm is capable enough to encrypt data of any size which is not possible with the ElGamal cryptosystem. The message to be encrypted is split into blocks of equal size and each block is assigned with a block index. The block indexing makes the scheme a probabilistic one. The algorithm holds semantic security because it is built on the DDH assumption. It assures non-malleability against attacks. The algorithm is compared with the ElGamal variant schemes based on the attained level of security and throughput and suitable for IoT networks by in cooperating integrity, authentication and encryption using a single algorithm.","PeriodicalId":432766,"journal":{"name":"2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"129 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128863002","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 : 2020-10-07DOI: 10.1109/I-SMAC49090.2020.9243396
K. Tharageswari, Laxmi Raja, D. Selvapandian, R. Dhanapal
The work that has been taken enforces the artificial intelligent technique in providing framework to design and make calculations based upon the discovers made by following the examples in learning technology to make a relationship successful for the appraisal of the work done with the enormous amount of information shared using collective learning ambience. The Collective learning ambience will be help for the larger team members for a problem in real time world and bring out a solution for the same. Visualizing these organized work flow in a multilayered frame work gives more difficulties in finding out a perfect solution. So to make the process much easier we are going to use a technique that deals around the machine learning framework in obtaining the required data and fulfill the information gathering as an easy process which is to be taken place in the collective learning ambience(CLA). After which the data and information that has been obtained enhanced with the integration technique in finding out the psychometric analysis and deep learning techniques to figure out feature extraction, skill recognition, pattern finding and also finding out the behaviors in human begin based upon the input that has been obtained from various resources. Thereafter the process will also be helpful in finding out the lower level process involved in the learning process.
{"title":"Collective Learning Ambiance of Human Pursuance with Intelligent Revival and Prediction Analysis","authors":"K. Tharageswari, Laxmi Raja, D. Selvapandian, R. Dhanapal","doi":"10.1109/I-SMAC49090.2020.9243396","DOIUrl":"https://doi.org/10.1109/I-SMAC49090.2020.9243396","url":null,"abstract":"The work that has been taken enforces the artificial intelligent technique in providing framework to design and make calculations based upon the discovers made by following the examples in learning technology to make a relationship successful for the appraisal of the work done with the enormous amount of information shared using collective learning ambience. The Collective learning ambience will be help for the larger team members for a problem in real time world and bring out a solution for the same. Visualizing these organized work flow in a multilayered frame work gives more difficulties in finding out a perfect solution. So to make the process much easier we are going to use a technique that deals around the machine learning framework in obtaining the required data and fulfill the information gathering as an easy process which is to be taken place in the collective learning ambience(CLA). After which the data and information that has been obtained enhanced with the integration technique in finding out the psychometric analysis and deep learning techniques to figure out feature extraction, skill recognition, pattern finding and also finding out the behaviors in human begin based upon the input that has been obtained from various resources. Thereafter the process will also be helpful in finding out the lower level process involved in the learning process.","PeriodicalId":432766,"journal":{"name":"2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122739514","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 : 2020-10-07DOI: 10.1109/I-SMAC49090.2020.9243518
Saharsh S B, Sanoj Viswasom, S. S
This paper deals with the designing of antenna arrays of fractal geometry to obtain the desired performance like the compact size and multiband behavior. Single element antenna cannot cover different wireless standards. To achieve multiband and compactness, different array configurations of fractal geometries are needed. This paper describes the design approach and simulation of different array configurations of fractal geometries. This mainly focused on the Koch snowflake fractal geometry. The various array configurations are 1 x 2, 1 x 4 and 2 x 2. The elements of this antenna arrays are divided by a distance of 0.5λ. The proposed antenna arrays resonate at different frequencies and cover the different wireless communication systems such as Wi-Max, C-band applications, WLAN, X band for satellite communication. The effects of various parameters on the performance of the antenna array are analyzed using OpenEMS.
{"title":"Design and Analysis of Koch Snowflake Fractal Antenna Array","authors":"Saharsh S B, Sanoj Viswasom, S. S","doi":"10.1109/I-SMAC49090.2020.9243518","DOIUrl":"https://doi.org/10.1109/I-SMAC49090.2020.9243518","url":null,"abstract":"This paper deals with the designing of antenna arrays of fractal geometry to obtain the desired performance like the compact size and multiband behavior. Single element antenna cannot cover different wireless standards. To achieve multiband and compactness, different array configurations of fractal geometries are needed. This paper describes the design approach and simulation of different array configurations of fractal geometries. This mainly focused on the Koch snowflake fractal geometry. The various array configurations are 1 x 2, 1 x 4 and 2 x 2. The elements of this antenna arrays are divided by a distance of 0.5λ. The proposed antenna arrays resonate at different frequencies and cover the different wireless communication systems such as Wi-Max, C-band applications, WLAN, X band for satellite communication. The effects of various parameters on the performance of the antenna array are analyzed using OpenEMS.","PeriodicalId":432766,"journal":{"name":"2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"76 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122452965","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 : 2020-10-07DOI: 10.1109/I-SMAC49090.2020.9243483
R. S. Kumar, N. Prakash, S. Anbuchelian
Addressing the unemployment problem is a bit challenging task. The non-engineering graduates can work in all the sectors, whereas engineering graduates can work in their designated job domain. So, the engineering graduate needs to be guided in getting the employment opportunity in their job domain. The unemployment rate in India is increasing drastically every year. The unemployment of engineering graduates is mainly due to the lack of knowledge on various job categories and all the graduates are enriching their skills in the attractive domain or the upcoming technologies. So, the graduates are falling only in a specified job category where the competition is more. This problem must be resolved by guiding the graduates. There is a need for a balanced approach in guiding the graduates to avoid the problem of searching for the job in the attractive domain. This paper presents a new method to predict the number of job openings based on location and job category using the Long Short-Term Memory model (LS TM). After the series of experiments conducted, the results show that the proposed method is 96% effective. The performance of the proposed system is found to be superior to the Simple Recurrent Neural Network (SRNN). By using the proposed model, the graduates are benefited in getting knowledge about the current job opportunities.
{"title":"Prediction of Job Openings in IT Sector using Long Short -Term Memory Model","authors":"R. S. Kumar, N. Prakash, S. Anbuchelian","doi":"10.1109/I-SMAC49090.2020.9243483","DOIUrl":"https://doi.org/10.1109/I-SMAC49090.2020.9243483","url":null,"abstract":"Addressing the unemployment problem is a bit challenging task. The non-engineering graduates can work in all the sectors, whereas engineering graduates can work in their designated job domain. So, the engineering graduate needs to be guided in getting the employment opportunity in their job domain. The unemployment rate in India is increasing drastically every year. The unemployment of engineering graduates is mainly due to the lack of knowledge on various job categories and all the graduates are enriching their skills in the attractive domain or the upcoming technologies. So, the graduates are falling only in a specified job category where the competition is more. This problem must be resolved by guiding the graduates. There is a need for a balanced approach in guiding the graduates to avoid the problem of searching for the job in the attractive domain. This paper presents a new method to predict the number of job openings based on location and job category using the Long Short-Term Memory model (LS TM). After the series of experiments conducted, the results show that the proposed method is 96% effective. The performance of the proposed system is found to be superior to the Simple Recurrent Neural Network (SRNN). By using the proposed model, the graduates are benefited in getting knowledge about the current job opportunities.","PeriodicalId":432766,"journal":{"name":"2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"167 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124000227","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 : 2020-10-07DOI: 10.1109/I-SMAC49090.2020.9243385
Shivani S Nikam, R. Dalvi
Along with the advancement of the world wide web, the rise and far reaching appropriation of the social site initiative have distorted the manner in which news is shaped and distributed. News has gotten quicker, less expensive and effectively available among web based life. This modify has joined a few hindrances also. Specifically, flabbergasting content, for example, fake news made by online networking clients, is getting progressively perilous. The fake news issue, in spite of being presented just because as of late, has become a significant examination theme because of the high substance of online networking. Writing fake remarks and news via web-based networking media is simple for clients. The primary test is to decide the distinction among genuine and fake news. We developed a method for the fake news classification on twitter. Web- based GUI is developed for the fake news classification system to categorize the tweets as fake or genuine. We develop a machine learning program to identify fake news by comparing tweets with genuine sources. Naive bayes and passive aggressive machine learning algorithms are estimated with TF-IDF feature extraction method.
{"title":"Machine Learning Algorithm based model for classification of fake news on Twitter","authors":"Shivani S Nikam, R. Dalvi","doi":"10.1109/I-SMAC49090.2020.9243385","DOIUrl":"https://doi.org/10.1109/I-SMAC49090.2020.9243385","url":null,"abstract":"Along with the advancement of the world wide web, the rise and far reaching appropriation of the social site initiative have distorted the manner in which news is shaped and distributed. News has gotten quicker, less expensive and effectively available among web based life. This modify has joined a few hindrances also. Specifically, flabbergasting content, for example, fake news made by online networking clients, is getting progressively perilous. The fake news issue, in spite of being presented just because as of late, has become a significant examination theme because of the high substance of online networking. Writing fake remarks and news via web-based networking media is simple for clients. The primary test is to decide the distinction among genuine and fake news. We developed a method for the fake news classification on twitter. Web- based GUI is developed for the fake news classification system to categorize the tweets as fake or genuine. We develop a machine learning program to identify fake news by comparing tweets with genuine sources. Naive bayes and passive aggressive machine learning algorithms are estimated with TF-IDF feature extraction method.","PeriodicalId":432766,"journal":{"name":"2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130835129","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 : 2020-10-07DOI: 10.1109/I-SMAC49090.2020.9243535
Madhuri R A, Mahima M Hampali, Nisarga Umesh, P. S, Y. J. Shirur, V. S. Chakravarthi
Processing of the multiple data streams demand highperformance multiple transfers overburden the Processor System on Chips (SoC) in real time multimedia processing applications. High performance direct memory access (DMA) controller eases the processor as it performs bulk data transfer without the intervention of processor. This is true even in most artificial intelligence (AI) based systems and interleaving functions in communication systems where high-speed bulk data transfers are required. This is achieved by the design of Enhanced Direct Memory Access (EDMA) Controller, for high speed bulk data transfers. Paper presents the design of enhanced DMA core which is synthesizable ready to integrate for high performance AI based Digital Signal Processing SoC. The EDMA core is used for flexible Memory Access and bulk data transfers. EDMA core support several methods for data transfer between an input or output (I/O) device and the core processing unit. The processor in the SoC is used to program the Direct Memory Access (DMA) transfer instructions and actual transfers are performed by the EDMA core without the interference of processor. The EDMA design supports flexible addressing modes like linear, circular, step for bulk data transfers. The EDMA core is planned to be verified with test cases as in realistic application scenarios of interleaving, real time video processing.
在实时多媒体处理应用中，多数据流的处理需要高性能的多传输，使SoC (Processor System on Chips)系统不堪重负。高性能直接存储器访问(DMA)控制器在不需要处理器干预的情况下进行批量数据传输，减轻了处理器的负担。即使在大多数基于人工智能(AI)的系统和需要高速批量数据传输的通信系统中的交错功能中也是如此。这是通过设计增强型直接存储器访问(EDMA)控制器来实现的，用于高速批量数据传输。本文提出了一种增强型DMA核心的设计，该核心可用于基于人工智能的高性能数字信号处理SoC。EDMA核心用于灵活的内存访问和批量数据传输。EDMA核心支持在输入或输出(I/O)设备和核心处理单元之间进行数据传输的几种方法。SoC中的处理器用于编程DMA (Direct Memory Access)传输指令，实际传输由EDMA核心执行，不受处理器的干扰。EDMA设计支持灵活的寻址模式，如线性，循环，步进批量数据传输。EDMA核心计划在交错实时视频处理的实际应用场景中进行测试用例验证。
{"title":"Design and Implementation of EDMA Controller for AI based DSP SoCs for Real- Time Multimedia Processing","authors":"Madhuri R A, Mahima M Hampali, Nisarga Umesh, P. S, Y. J. Shirur, V. S. Chakravarthi","doi":"10.1109/I-SMAC49090.2020.9243535","DOIUrl":"https://doi.org/10.1109/I-SMAC49090.2020.9243535","url":null,"abstract":"Processing of the multiple data streams demand highperformance multiple transfers overburden the Processor System on Chips (SoC) in real time multimedia processing applications. High performance direct memory access (DMA) controller eases the processor as it performs bulk data transfer without the intervention of processor. This is true even in most artificial intelligence (AI) based systems and interleaving functions in communication systems where high-speed bulk data transfers are required. This is achieved by the design of Enhanced Direct Memory Access (EDMA) Controller, for high speed bulk data transfers. Paper presents the design of enhanced DMA core which is synthesizable ready to integrate for high performance AI based Digital Signal Processing SoC. The EDMA core is used for flexible Memory Access and bulk data transfers. EDMA core support several methods for data transfer between an input or output (I/O) device and the core processing unit. The processor in the SoC is used to program the Direct Memory Access (DMA) transfer instructions and actual transfers are performed by the EDMA core without the interference of processor. The EDMA design supports flexible addressing modes like linear, circular, step for bulk data transfers. The EDMA core is planned to be verified with test cases as in realistic application scenarios of interleaving, real time video processing.","PeriodicalId":432766,"journal":{"name":"2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128706524","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 : 2020-10-07DOI: 10.1109/I-SMAC49090.2020.9243446
J. Sowjanya, Naveen Kumar Chikkakrishna, Teja Tallam
Due to the mixed traffic conditions in developing countries like India, there is an exponential growth of road accidents over the decade which leads to the deterioration of road safety. Therefore, road safety has become a major concern for researchers and engineers. It is very important to know the effect of influencing variables on the crash count. Although the data to collect about the influencing variables for the crashes is a challenging task it is very important to know the effect. In this study, a four-lane divided national highway is considered and the analysis is done for the crash records for five years which is from 2013–2018. From the plan and profile drawings of the highway, different geometrical characteristics are extracted. Traffic characteristics are collected from the field studies. Stochastic models were developed to know the effect of selected variables on collision type. Using soft computing tool Artificial Neural Network is developed and the significance of the influencing variables on collision type is known.
{"title":"ANN based Study to Investigate the Parameters Influencing Collision Type on a Four Lane Divided National Highway","authors":"J. Sowjanya, Naveen Kumar Chikkakrishna, Teja Tallam","doi":"10.1109/I-SMAC49090.2020.9243446","DOIUrl":"https://doi.org/10.1109/I-SMAC49090.2020.9243446","url":null,"abstract":"Due to the mixed traffic conditions in developing countries like India, there is an exponential growth of road accidents over the decade which leads to the deterioration of road safety. Therefore, road safety has become a major concern for researchers and engineers. It is very important to know the effect of influencing variables on the crash count. Although the data to collect about the influencing variables for the crashes is a challenging task it is very important to know the effect. In this study, a four-lane divided national highway is considered and the analysis is done for the crash records for five years which is from 2013–2018. From the plan and profile drawings of the highway, different geometrical characteristics are extracted. Traffic characteristics are collected from the field studies. Stochastic models were developed to know the effect of selected variables on collision type. Using soft computing tool Artificial Neural Network is developed and the significance of the influencing variables on collision type is known.","PeriodicalId":432766,"journal":{"name":"2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131188416","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 : 2020-10-07DOI: 10.1109/I-SMAC49090.2020.9243303
Nusrat Shams, Tasfia Tasnim Labiba
Industrial revolution changes the scenario of developing countries. Proper industry monitoring system helps to increase the production of industry. But it is cost-effective to use more manpower or use more transducer to monitor the same thing in the industry. Keeping these things in mind, an automated robotic system is developed which helps to inspect the temperature and humidity sensitive industry as well as protects the industry from fire or smoke attack or gas bursting. This robot will able to collect important data from different sides of the industry and transmit them to the IoT (Internet of Things) network, stores the data for data analysis and helps industry development by improving the quality of materials. The robot can make an alarm for any imbalanced situation of the industry. It also can switch off the whole system remotely if IoT network is connected to the main system of the industry. This robot will monitor the place from time to time and reduce the cost of using too many fire and smoke detectors in the industry. Basically, in a word, an automatic multitasking IoT based robot is developed to save time and prevent unwanted accidents in the industrial workplace.
{"title":"Autonomous Industrial Ambient Robotic System","authors":"Nusrat Shams, Tasfia Tasnim Labiba","doi":"10.1109/I-SMAC49090.2020.9243303","DOIUrl":"https://doi.org/10.1109/I-SMAC49090.2020.9243303","url":null,"abstract":"Industrial revolution changes the scenario of developing countries. Proper industry monitoring system helps to increase the production of industry. But it is cost-effective to use more manpower or use more transducer to monitor the same thing in the industry. Keeping these things in mind, an automated robotic system is developed which helps to inspect the temperature and humidity sensitive industry as well as protects the industry from fire or smoke attack or gas bursting. This robot will able to collect important data from different sides of the industry and transmit them to the IoT (Internet of Things) network, stores the data for data analysis and helps industry development by improving the quality of materials. The robot can make an alarm for any imbalanced situation of the industry. It also can switch off the whole system remotely if IoT network is connected to the main system of the industry. This robot will monitor the place from time to time and reduce the cost of using too many fire and smoke detectors in the industry. Basically, in a word, an automatic multitasking IoT based robot is developed to save time and prevent unwanted accidents in the industrial workplace.","PeriodicalId":432766,"journal":{"name":"2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125279124","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 : 2020-10-07DOI: 10.1109/I-SMAC49090.2020.9243515
Choe Hyeon, Sagaya Aurelia
Cloud computing is a technology that uses centrally processed computing resources over the Internet by a large number of users. Because many requests are concentrated on cloud servers, they must be properly distributed to avoid degradation of quality. Load balancing categorizes requests from users according to established algorithms and assigns appropriate virtual machines. Because load balancing algorithms are developed according to the cloud's usage environment, various algorithms are being utilized. Recently, government agencies are also interested in introducing cloud technologies beyond private sectors. Many militaries have selected Cloud as its basic task to apply new technologies such as AI to military operations. However, there is no precedent for military cloud development, and the lack of doud technology research considering the operational environment has delayed the progress of cloud adoption. The algorithm presented by this paper makes the combat power, which varies according to the importance of the operation, an important variable. This variable makes each user's access to computing resources different. Although similar to other dynamic algorithms, the impact of priorities is so big that the degree of imbalance between tasks was higher.
{"title":"Enhancement of Efficiency of Military Cloud Computing using Lanchester Model","authors":"Choe Hyeon, Sagaya Aurelia","doi":"10.1109/I-SMAC49090.2020.9243515","DOIUrl":"https://doi.org/10.1109/I-SMAC49090.2020.9243515","url":null,"abstract":"Cloud computing is a technology that uses centrally processed computing resources over the Internet by a large number of users. Because many requests are concentrated on cloud servers, they must be properly distributed to avoid degradation of quality. Load balancing categorizes requests from users according to established algorithms and assigns appropriate virtual machines. Because load balancing algorithms are developed according to the cloud's usage environment, various algorithms are being utilized. Recently, government agencies are also interested in introducing cloud technologies beyond private sectors. Many militaries have selected Cloud as its basic task to apply new technologies such as AI to military operations. However, there is no precedent for military cloud development, and the lack of doud technology research considering the operational environment has delayed the progress of cloud adoption. The algorithm presented by this paper makes the combat power, which varies according to the importance of the operation, an important variable. This variable makes each user's access to computing resources different. Although similar to other dynamic algorithms, the impact of priorities is so big that the degree of imbalance between tasks was higher.","PeriodicalId":432766,"journal":{"name":"2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114336854","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 : 2020-10-07DOI: 10.1109/I-SMAC49090.2020.9243521
K. Deepika, T. Teja, Naveen Kumar Chikkakrishna
Generally drivers face a dilemma as they approach the intersection during the amber phase. Due to the existence of this Dilemma zone, safety and efficiency of the intersection affect. Whereas, decision-making behaviour depends upon different parameters such as approaching speed, vehicular volume per cycle, type of vehicle, distance from stop line, number of lanes at the intersection, yellow phase and driver's attributes such as age and gender. The two main contributions offered by this paper are first, developing the prediction and classification models of driver's decision using Artificial Neural Network (ANN) and Support Vector Machine (SVM). Second, defining the importance of parameters using Random Forest which influences the driver's decision-making behaviour. For this study, 328 driver's decision or responses were collected through video graphic survey conducted at three different locations of Hyderabad, India. The research concludes that SVM with the sigmoidal kernel is showing more classification accuracy when compared with other kernels. Whereas; when SVM (71.95%) and ANN (76.82%) models are compared than ANN was found to be having more accuracy. It was found that distance from stop-line, approaching speedand driver's age is found to the most affecting parameters among all considered parameters.
{"title":"Predicting driver's decision- making behaviour in Amber phase using ML techniques","authors":"K. Deepika, T. Teja, Naveen Kumar Chikkakrishna","doi":"10.1109/I-SMAC49090.2020.9243521","DOIUrl":"https://doi.org/10.1109/I-SMAC49090.2020.9243521","url":null,"abstract":"Generally drivers face a dilemma as they approach the intersection during the amber phase. Due to the existence of this Dilemma zone, safety and efficiency of the intersection affect. Whereas, decision-making behaviour depends upon different parameters such as approaching speed, vehicular volume per cycle, type of vehicle, distance from stop line, number of lanes at the intersection, yellow phase and driver's attributes such as age and gender. The two main contributions offered by this paper are first, developing the prediction and classification models of driver's decision using Artificial Neural Network (ANN) and Support Vector Machine (SVM). Second, defining the importance of parameters using Random Forest which influences the driver's decision-making behaviour. For this study, 328 driver's decision or responses were collected through video graphic survey conducted at three different locations of Hyderabad, India. The research concludes that SVM with the sigmoidal kernel is showing more classification accuracy when compared with other kernels. Whereas; when SVM (71.95%) and ANN (76.82%) models are compared than ANN was found to be having more accuracy. It was found that distance from stop-line, approaching speedand driver's age is found to the most affecting parameters among all considered parameters.","PeriodicalId":432766,"journal":{"name":"2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114820223","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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