Pub Date : 2018-12-01DOI: 10.1109/ICONIC.2018.8601093
S. Ntsaluba, M. Michael, Solathiso Shasha, Isaac Dlamini
This study presents an analysis of the energy usage of the twenty-eight residences at the University of Johannesburg during the 2017 calendar year. The paper presents the energy consumption if the residences based on the total energy usage per residence as well as in terms of the number of students at each of the residences for the different seasons of the year. The results of this study show a strong correlation between energy usage and factors such as temperature, time of day and building occupancy. Furthermore, an analysis of the load factors and power factors for each of these residences during different months of the 2017 calendar year revealed possible opportunities for energy saving interventions.
{"title":"Electrical Energy Usage Analysis for all Residences of a South African Academic Institution","authors":"S. Ntsaluba, M. Michael, Solathiso Shasha, Isaac Dlamini","doi":"10.1109/ICONIC.2018.8601093","DOIUrl":"https://doi.org/10.1109/ICONIC.2018.8601093","url":null,"abstract":"This study presents an analysis of the energy usage of the twenty-eight residences at the University of Johannesburg during the 2017 calendar year. The paper presents the energy consumption if the residences based on the total energy usage per residence as well as in terms of the number of students at each of the residences for the different seasons of the year. The results of this study show a strong correlation between energy usage and factors such as temperature, time of day and building occupancy. Furthermore, an analysis of the load factors and power factors for each of these residences during different months of the 2017 calendar year revealed possible opportunities for energy saving interventions.","PeriodicalId":277315,"journal":{"name":"2018 International Conference on Intelligent and Innovative Computing Applications (ICONIC)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124921410","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 : 2018-12-01DOI: 10.1109/ICONIC.2018.8601278
Thabo Mahlangu, Chunling Tu, P. Owolawi
As we see the cyberspace evolve we also see a directly proportional growth of the people using the cyberspace for communication. As a result, the misuse of the cyberspace has given rise to negative issues such as cyberbullying, which is a form of harassing other people using information technology in a deliberate and continual manner. The detection and prevention of cyberbullying becomes critical for safe and health social media platforms. In this paper, a review of the cyberbullying content in Internet, the categories of cyberbullying, data sources containing cyberbullying data for research, and machine learning techniques for cyberbullying detection are overviewed. The main challenges of the cyberbullying detection are demonstrated, including the lack of multimedia content-based detection and availability of public accessible dataset. Suggestions are provided as the conclusion of the overview.
{"title":"A Review of Automated Detection Methods for Cyberbullying","authors":"Thabo Mahlangu, Chunling Tu, P. Owolawi","doi":"10.1109/ICONIC.2018.8601278","DOIUrl":"https://doi.org/10.1109/ICONIC.2018.8601278","url":null,"abstract":"As we see the cyberspace evolve we also see a directly proportional growth of the people using the cyberspace for communication. As a result, the misuse of the cyberspace has given rise to negative issues such as cyberbullying, which is a form of harassing other people using information technology in a deliberate and continual manner. The detection and prevention of cyberbullying becomes critical for safe and health social media platforms. In this paper, a review of the cyberbullying content in Internet, the categories of cyberbullying, data sources containing cyberbullying data for research, and machine learning techniques for cyberbullying detection are overviewed. The main challenges of the cyberbullying detection are demonstrated, including the lack of multimedia content-based detection and availability of public accessible dataset. Suggestions are provided as the conclusion of the overview.","PeriodicalId":277315,"journal":{"name":"2018 International Conference on Intelligent and Innovative Computing Applications (ICONIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131184427","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 : 2018-12-01DOI: 10.1109/ICONIC.2018.8601087
S. Mapunya, M. Velempini
The ever-increasing number of wireless network systems brought a problem of spectrum congestion leading to slow data communications. All of the radio spectrums are allocated to different users, services and applications. Hence studies have shown that some of those spectrum bands are underutilized while others are congested. Cognitive radio concept has evolved to solve the problem of spectrum congestion by allowing cognitive users to opportunistically utilize the underutilized spectrum while minimizing interference with other users. Byzantine attack is one of the security issues which threaten the successful deployment of this technology. Byzantine attack is compromised cognitive radios which relay falsified data about the availability of the spectrum to other legitimate cognitive radios in the network leading interference. In this paper we are proposing a security measure to thwart the effect caused by these attacks and compared it to Attack-Proof Cooperative Spectrum Sensing.
{"title":"The Design of Byzantine Attack Mitigation Scheme in Cognitive Radio Ad-hoc Networks","authors":"S. Mapunya, M. Velempini","doi":"10.1109/ICONIC.2018.8601087","DOIUrl":"https://doi.org/10.1109/ICONIC.2018.8601087","url":null,"abstract":"The ever-increasing number of wireless network systems brought a problem of spectrum congestion leading to slow data communications. All of the radio spectrums are allocated to different users, services and applications. Hence studies have shown that some of those spectrum bands are underutilized while others are congested. Cognitive radio concept has evolved to solve the problem of spectrum congestion by allowing cognitive users to opportunistically utilize the underutilized spectrum while minimizing interference with other users. Byzantine attack is one of the security issues which threaten the successful deployment of this technology. Byzantine attack is compromised cognitive radios which relay falsified data about the availability of the spectrum to other legitimate cognitive radios in the network leading interference. In this paper we are proposing a security measure to thwart the effect caused by these attacks and compared it to Attack-Proof Cooperative Spectrum Sensing.","PeriodicalId":277315,"journal":{"name":"2018 International Conference on Intelligent and Innovative Computing Applications (ICONIC)","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132019329","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 : 2018-12-01DOI: 10.1109/ICONIC.2018.8601218
Olamide M. Shekoni, Ali N. Hasan, T. Shongwe
Orthogonal frequency division multiplexer (OFDM) is a recent modulation scheme used to transmit signals across power line communication (PLC) channel due to its robustness against some known PLC problems. However, this scheme is greatly affected by the impulsive noise (IN) and often causes corruption with the transmitted bits. Different impulsive noise error correcting methods have been introduced and used to remove impulsive noise in OFDM systems. However, these techniques suffer some limitations and require much signal to noise ratio (SNR) power to operate. In this paper, an approach of designing an effective impulsive-noise error-correcting technique was introduced using three-known artificial neural network techniques (Levenberg-Marquardt, Scaled conjugate gradient, and Bayesian regularization). Findings suggest that both Bayesian regularization and Levenberg-Marquardt ANN techniques can be used to effectively remove the impulsive noise present in an OFDM channel and using the least SNR power.
{"title":"Detecting and Removing the Impulsive Noise in OFDM Channels Using Different ANN Techniques","authors":"Olamide M. Shekoni, Ali N. Hasan, T. Shongwe","doi":"10.1109/ICONIC.2018.8601218","DOIUrl":"https://doi.org/10.1109/ICONIC.2018.8601218","url":null,"abstract":"Orthogonal frequency division multiplexer (OFDM) is a recent modulation scheme used to transmit signals across power line communication (PLC) channel due to its robustness against some known PLC problems. However, this scheme is greatly affected by the impulsive noise (IN) and often causes corruption with the transmitted bits. Different impulsive noise error correcting methods have been introduced and used to remove impulsive noise in OFDM systems. However, these techniques suffer some limitations and require much signal to noise ratio (SNR) power to operate. In this paper, an approach of designing an effective impulsive-noise error-correcting technique was introduced using three-known artificial neural network techniques (Levenberg-Marquardt, Scaled conjugate gradient, and Bayesian regularization). Findings suggest that both Bayesian regularization and Levenberg-Marquardt ANN techniques can be used to effectively remove the impulsive noise present in an OFDM channel and using the least SNR power.","PeriodicalId":277315,"journal":{"name":"2018 International Conference on Intelligent and Innovative Computing Applications (ICONIC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132373875","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 : 2018-12-01DOI: 10.1109/ICONIC.2018.8601221
B. E. Buthelezi, M. Mphahlele, D. D. du Plessis, Solly Maswikaneng, Topside E. Mathonsi
ZigBee is an industrial standard for personal area network (PAN) developed for low power, cost and rate for wireless radio communications. Lately, ZigBee implementation in Healthcare Monitoring Systems (HMS) is exponentially increasing. ZigBee specification proposed that a number of wireless sensor nodes can be connected through a tree hierarchal topology in a ZigBee network. In Health sector, HMSs implement body sensor nodes that are capable of collecting human physiological data such as Heart Rate signals. However, routing in tree topology needs every sensor node to transmit data to a central node called coordinator. Parent-child is the mechanism that is used to forward packets to the coordinator in the original ZigBee Tree Routing (ZTR) scheme. However, parent-child mechanism is not suitable to transmit sensitive data such as heart rate pulses. Parent-child mechanism suffers from end to end delay problem which is a disadvantage for physiological signals as they require emergency attention. This paper is proposing a solution that attempts to solve this problem by implementing New Tree Routing Protocol (NTRP) algorithm. NTRP scheme is evaluated on NS-2 simulator tool and its simulation results show improvement of average end to end delay of 0.3-1% while the packet delivery ratio is 5-11.6%.
{"title":"A New Tree Routing Protocol for ZigBee Healthcare Monitoring Systems","authors":"B. E. Buthelezi, M. Mphahlele, D. D. du Plessis, Solly Maswikaneng, Topside E. Mathonsi","doi":"10.1109/ICONIC.2018.8601221","DOIUrl":"https://doi.org/10.1109/ICONIC.2018.8601221","url":null,"abstract":"ZigBee is an industrial standard for personal area network (PAN) developed for low power, cost and rate for wireless radio communications. Lately, ZigBee implementation in Healthcare Monitoring Systems (HMS) is exponentially increasing. ZigBee specification proposed that a number of wireless sensor nodes can be connected through a tree hierarchal topology in a ZigBee network. In Health sector, HMSs implement body sensor nodes that are capable of collecting human physiological data such as Heart Rate signals. However, routing in tree topology needs every sensor node to transmit data to a central node called coordinator. Parent-child is the mechanism that is used to forward packets to the coordinator in the original ZigBee Tree Routing (ZTR) scheme. However, parent-child mechanism is not suitable to transmit sensitive data such as heart rate pulses. Parent-child mechanism suffers from end to end delay problem which is a disadvantage for physiological signals as they require emergency attention. This paper is proposing a solution that attempts to solve this problem by implementing New Tree Routing Protocol (NTRP) algorithm. NTRP scheme is evaluated on NS-2 simulator tool and its simulation results show improvement of average end to end delay of 0.3-1% while the packet delivery ratio is 5-11.6%.","PeriodicalId":277315,"journal":{"name":"2018 International Conference on Intelligent and Innovative Computing Applications (ICONIC)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130588746","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 : 2018-12-01DOI: 10.1109/ICONIC.2018.8601262
L. Grobbelaar
A below average throughput of Information Technology students specializing in software development is a challenge that many Universities and Universities of Technology in South Africa face. Contributing factors to this phenomenon are varied at best, but one of the identified factors are that students in this field, especially first year students, find it difficult to conceptualize the associated information and manner of thinking required to become successful in their studies. This is especially true when considering object orientated programming concepts and paradigms that students are required to master as part of their studies. Literary evidence suggests that a high level of working memory, which is associated with abstract thinking ability, is required when learning and applying object orientated programming concepts. The problem becomes more evident and serious if we consider that the Information and Communication Technology sector of a country is largely dependent on the graduating student populous in terms of growing the sector sustainably. A specialized software instrument was developed and tested in an attempt to affect a change in the abstract thinking ability of students from a student sample at a University of Technology. The results of this study focusses on the effect that the instrument realized on the academic performance of first year students related to particularly to object orientated programming and their abstract thinking ability in general as gauged by, amongst other instruments, the General Scholastic Ability Test, or GSAT, rather than focusing on the instrument itself.
{"title":"The Effects of a Software Artefact Designed to Stimulate Abstract Thinking Ability on the Academic Performance in Object Oriented Programming of First Year Information Technology Students","authors":"L. Grobbelaar","doi":"10.1109/ICONIC.2018.8601262","DOIUrl":"https://doi.org/10.1109/ICONIC.2018.8601262","url":null,"abstract":"A below average throughput of Information Technology students specializing in software development is a challenge that many Universities and Universities of Technology in South Africa face. Contributing factors to this phenomenon are varied at best, but one of the identified factors are that students in this field, especially first year students, find it difficult to conceptualize the associated information and manner of thinking required to become successful in their studies. This is especially true when considering object orientated programming concepts and paradigms that students are required to master as part of their studies. Literary evidence suggests that a high level of working memory, which is associated with abstract thinking ability, is required when learning and applying object orientated programming concepts. The problem becomes more evident and serious if we consider that the Information and Communication Technology sector of a country is largely dependent on the graduating student populous in terms of growing the sector sustainably. A specialized software instrument was developed and tested in an attempt to affect a change in the abstract thinking ability of students from a student sample at a University of Technology. The results of this study focusses on the effect that the instrument realized on the academic performance of first year students related to particularly to object orientated programming and their abstract thinking ability in general as gauged by, amongst other instruments, the General Scholastic Ability Test, or GSAT, rather than focusing on the instrument itself.","PeriodicalId":277315,"journal":{"name":"2018 International Conference on Intelligent and Innovative Computing Applications (ICONIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130434325","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 : 2018-12-01DOI: 10.1109/ICONIC.2018.8601203
Skhumbuzo Zwane, Paul Tarwireyi, M. Adigun
Modern tactical wireless network (TWN) communication technologies are not only capable of transmitting voice but also capable of transmitting data. Due to such capabilities, TWN have high security requirements as any security breach can lead to detrimental effects. Hence, securing such an environment is not only a requirement but also a virtual prerequisite to the network centric warfare operational (NCW) theory. One key to securing this environment is to promptly and accurately recognize information warfare attacks directed to the network and respond to them. This is achieved using intrusion detection systems (IDS). However, false detection of nodes in hostile environment remains a major problem that need to be addressed. Recently, machine learning methods and algorithms have shown applicability and are growing research area for cyber security and intrusion detection. Conversely, several decades of research in the field of machine learning have resulted in a multitude of different algorithms for solving a broad range of problems. The question then becomes, which one amongst these machine learning algorithms have the potential to enhance or address IDS issues in TWN. In this paper, seven machine learning classifiers are analyzed; Multi-Layer Perceptron, Bayesian Network, Support Vector Machine (SMO), Adaboost, Random Forest, Bootstrap Aggregation, and Decision Tree (J48). WEKA tool was used to implement and evaluate the classifiers. The results obtained indicate that ensemble-based learning methods outperformed single learning methods when we consider the detection accuracy metrics; AUC, TPR, and FPR. However, ensemble classifiers tend to be slower in in terms of build time and model test time.
{"title":"Performance Analysis of Machine Learning Classifiers for Intrusion Detection","authors":"Skhumbuzo Zwane, Paul Tarwireyi, M. Adigun","doi":"10.1109/ICONIC.2018.8601203","DOIUrl":"https://doi.org/10.1109/ICONIC.2018.8601203","url":null,"abstract":"Modern tactical wireless network (TWN) communication technologies are not only capable of transmitting voice but also capable of transmitting data. Due to such capabilities, TWN have high security requirements as any security breach can lead to detrimental effects. Hence, securing such an environment is not only a requirement but also a virtual prerequisite to the network centric warfare operational (NCW) theory. One key to securing this environment is to promptly and accurately recognize information warfare attacks directed to the network and respond to them. This is achieved using intrusion detection systems (IDS). However, false detection of nodes in hostile environment remains a major problem that need to be addressed. Recently, machine learning methods and algorithms have shown applicability and are growing research area for cyber security and intrusion detection. Conversely, several decades of research in the field of machine learning have resulted in a multitude of different algorithms for solving a broad range of problems. The question then becomes, which one amongst these machine learning algorithms have the potential to enhance or address IDS issues in TWN. In this paper, seven machine learning classifiers are analyzed; Multi-Layer Perceptron, Bayesian Network, Support Vector Machine (SMO), Adaboost, Random Forest, Bootstrap Aggregation, and Decision Tree (J48). WEKA tool was used to implement and evaluate the classifiers. The results obtained indicate that ensemble-based learning methods outperformed single learning methods when we consider the detection accuracy metrics; AUC, TPR, and FPR. However, ensemble classifiers tend to be slower in in terms of build time and model test time.","PeriodicalId":277315,"journal":{"name":"2018 International Conference on Intelligent and Innovative Computing Applications (ICONIC)","volume":"51 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126001809","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 : 2018-12-01DOI: 10.1109/ICONIC.2018.8601228
Sindisiwe Mahlambi, S. Civilcharran, Nurudeen Ajayi
The advent of Web 2.0 brought about a new medium of communication, such as social media. Today’s generation of students have been greatly influenced by the Web and hence, have become active users of the different available social media platforms. The influence of social media platforms on students, is causing a shift in learning, and as a result, educational institutions are increasingly creating alternative learning platforms, using social media to facilitate student-centred learning. This paper presents the findings from a study that was conducted to understand the perception of students regarding the use of social media as an alternative learning platform. The quantitative research method was adopted, and 331 questionnaires were administered at the Pietermaritzburg campus of University of KwaZulu-Natal (UKZN). The findings reveal that students are comfortable with the use of social media as a learning platform, because it enhances their ability to be interactive and it improves their chances of accessing educational information, even when not within the academic environment. The findings, however, also revealed that some features of social media could cause distraction to students, but when adequate measures are introduced, social media could become an additional means of interaction for students, especially for academic purposes.
Web 2.0的出现带来了一种新的交流媒介，比如社交媒体。当今一代的学生深受网络的影响，因此，他们已经成为各种可用社交媒体平台的活跃用户。社交媒体平台对学生的影响正在导致学习方式的转变，因此，教育机构越来越多地创建替代学习平台，利用社交媒体促进以学生为中心的学习。本文介绍了一项研究的结果，该研究旨在了解学生对使用社交媒体作为替代学习平台的看法。采用定量研究方法，在夸祖鲁-纳塔尔大学彼得马里茨堡校区发放问卷331份。调查结果显示，学生们乐于使用社交媒体作为学习平台，因为它增强了他们互动的能力，增加了他们获取教育信息的机会，即使不是在学术环境中。然而，研究结果也表明，社交媒体的一些功能可能会分散学生的注意力，但如果采取适当的措施，社交媒体可能会成为学生的一种额外的互动方式，尤其是在学术方面。
{"title":"The Perception of Students about the use of Social Media as an Alternate Learning Platform","authors":"Sindisiwe Mahlambi, S. Civilcharran, Nurudeen Ajayi","doi":"10.1109/ICONIC.2018.8601228","DOIUrl":"https://doi.org/10.1109/ICONIC.2018.8601228","url":null,"abstract":"The advent of Web 2.0 brought about a new medium of communication, such as social media. Today’s generation of students have been greatly influenced by the Web and hence, have become active users of the different available social media platforms. The influence of social media platforms on students, is causing a shift in learning, and as a result, educational institutions are increasingly creating alternative learning platforms, using social media to facilitate student-centred learning. This paper presents the findings from a study that was conducted to understand the perception of students regarding the use of social media as an alternative learning platform. The quantitative research method was adopted, and 331 questionnaires were administered at the Pietermaritzburg campus of University of KwaZulu-Natal (UKZN). The findings reveal that students are comfortable with the use of social media as a learning platform, because it enhances their ability to be interactive and it improves their chances of accessing educational information, even when not within the academic environment. The findings, however, also revealed that some features of social media could cause distraction to students, but when adequate measures are introduced, social media could become an additional means of interaction for students, especially for academic purposes.","PeriodicalId":277315,"journal":{"name":"2018 International Conference on Intelligent and Innovative Computing Applications (ICONIC)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115854639","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 : 2018-12-01DOI: 10.1109/ICONIC.2018.8601225
E. Ginters, M. Mezitis, D. Aizstrauta
Humans are an integral part of the nature and to avoid imbalance, the impact of technology on the surrounding environment should be limited. Therefore, green technologies are becoming an important part of economy, politics and science. One of the largest sources of pollution are motorized vehicles, that account for around 14 % of the global greenhouse gas emissions [1]. Use of green transportation within the integrated multimodal transport system is important to minimize carbon emissions [2]. One of these means of transportation is cycling. For cycling to become a fully integrated element within a multimodal transport system and not just a type of tourism, an appropriate infrastructure is necessary – cycling routes, lighting, bike rental, parking, repair services, as well as alignment with other types of transport. Cyclists need route planning tools, information about road surface quality, relief, and usage patterns during different weather conditions and over different days of the week. Infrastructure development projects demand significant financial resources and therefore municipalities need sustainable tools for services design and management. In this article the authors discuss acceptance and sustainability assessment methodology IASAM use to validate the VeloRouter - cycling network designing technology.
{"title":"Sustainability Simulation and Assessment of Bicycle Network Design and Maintenance Environment","authors":"E. Ginters, M. Mezitis, D. Aizstrauta","doi":"10.1109/ICONIC.2018.8601225","DOIUrl":"https://doi.org/10.1109/ICONIC.2018.8601225","url":null,"abstract":"Humans are an integral part of the nature and to avoid imbalance, the impact of technology on the surrounding environment should be limited. Therefore, green technologies are becoming an important part of economy, politics and science. One of the largest sources of pollution are motorized vehicles, that account for around 14 % of the global greenhouse gas emissions [1]. Use of green transportation within the integrated multimodal transport system is important to minimize carbon emissions [2]. One of these means of transportation is cycling. For cycling to become a fully integrated element within a multimodal transport system and not just a type of tourism, an appropriate infrastructure is necessary – cycling routes, lighting, bike rental, parking, repair services, as well as alignment with other types of transport. Cyclists need route planning tools, information about road surface quality, relief, and usage patterns during different weather conditions and over different days of the week. Infrastructure development projects demand significant financial resources and therefore municipalities need sustainable tools for services design and management. In this article the authors discuss acceptance and sustainability assessment methodology IASAM use to validate the VeloRouter - cycling network designing technology.","PeriodicalId":277315,"journal":{"name":"2018 International Conference on Intelligent and Innovative Computing Applications (ICONIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115127209","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 : 2018-12-01DOI: 10.1109/ICONIC.2018.8601235
R. Trifonov, O. Nakov, V. Mladenov
In the field of Cyber Security there has been a transition from the stage of Cyber Criminality to the stage of Cyber War over the last few years. According to the new challenges, the expert community has two main approaches: to adopt the philosophy and methods of Military Intelligence, and to use Artificial Intelligence methods for counteraction of Cyber Attacks. Тhis paper describes some of the results obtained at Technical University of Sofia in the implementation of project related to the application of intelligent methods for increasing the security in computer networks. The analysis of the feasibility of various Artificial Intelligence methods has shown that a method that is equally effective for all stages of the Cyber Intelligence cannot be identified. While for Tactical Cyber Threats Intelligence has been selected and experimented a Multi-Agent System, the Recurrent Neural Networks are offered for the needs of Operational Cyber Threats Intelligence.
{"title":"Artificial Intelligence in Cyber Threats Intelligence","authors":"R. Trifonov, O. Nakov, V. Mladenov","doi":"10.1109/ICONIC.2018.8601235","DOIUrl":"https://doi.org/10.1109/ICONIC.2018.8601235","url":null,"abstract":"In the field of Cyber Security there has been a transition from the stage of Cyber Criminality to the stage of Cyber War over the last few years. According to the new challenges, the expert community has two main approaches: to adopt the philosophy and methods of Military Intelligence, and to use Artificial Intelligence methods for counteraction of Cyber Attacks. Тhis paper describes some of the results obtained at Technical University of Sofia in the implementation of project related to the application of intelligent methods for increasing the security in computer networks. The analysis of the feasibility of various Artificial Intelligence methods has shown that a method that is equally effective for all stages of the Cyber Intelligence cannot be identified. While for Tactical Cyber Threats Intelligence has been selected and experimented a Multi-Agent System, the Recurrent Neural Networks are offered for the needs of Operational Cyber Threats Intelligence.","PeriodicalId":277315,"journal":{"name":"2018 International Conference on Intelligent and Innovative Computing Applications (ICONIC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114956286","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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