Pub Date : 2022-03-21DOI: 10.15837/ijccc.2022.3.4481
A. Ramamoorthy, K. Karuppasamy
IDM design and implementation remain a difficult undertaking and an unsolved research topic. Multi-dimensional irrelevant characteristics and duplicate information are included in the network dataset. To boost the effectiveness of IDM, a novel hybrid model is developed that combines Fuzzy Genetic Algorithms with Increment Import Vector Machines (FGA-I2VM), which works with huge amounts of both normal and aberrant network data with high detecting accuracy and low false alarm rates. The algorithms chosen for IDM in this stage are machine learning algorithms, which learn, find, and adapt patterns to changing situations over time. Pre-processing is the most essential stage in any IDM, and feature selection is utilized for pre-processing, which is the act of picking a collection or subset of relevant features for the purpose of creating a solution model. Information Gain (IG) is utilized in this FGA-I2VM model to pick features from the dataset for I2VM classification. To train the I2VM classifier, FGA uses three sets of operations to produce a new set of inhabitants with distinct patterns: cross over operation, selection, and finally mutation. The new population is then put into the Import Vector Machine, a strong classifier that has been used to solve a wide range of pattern recognition issues. FGA are quick, especially considering their capacity to discover global optima. Another advantage of FGA is their naturally parallel nature of assessing the individuals within a population. As a classifier, I2VM has self-tuning properties that allow patterns to attain global optimums. The FGA-efficacy I2VM model’s is complemented by information gain, which improves speed and detection accuracy while having a low computing cost
IDM的设计和实现仍然是一项艰巨的任务，也是一个尚未解决的研究课题。网络数据集中包含多维不相关特征和重复信息。为了提高IDM的有效性，提出了一种将模糊遗传算法与增量导入向量机(FGA-I2VM)相结合的混合模型，该模型可以同时处理大量的正常和异常网络数据，具有较高的检测精度和较低的虚警率。在这个阶段为IDM选择的算法是机器学习算法，它可以学习、发现模式，并根据不断变化的情况调整模式。预处理是任何IDM中最重要的阶段，特征选择用于预处理，这是为了创建解决方案模型而选择相关特征的集合或子集的行为。FGA-I2VM模型利用信息增益(Information Gain, IG)从数据集中挑选特征进行I2VM分类。为了训练I2VM分类器，FGA使用三组操作来产生一组具有不同模式的新居民:交叉操作、选择和最后的突变。然后将新的种群放入导入向量机，这是一种强大的分类器，已被用于解决广泛的模式识别问题。FGA是快速的，特别是考虑到它们发现全局最优的能力。FGA的另一个优点是它们在评估种群中的个体时具有天然的并行性。作为一个分类器，I2VM具有允许模式达到全局最优的自调优属性。fga -功效I2VM模型辅以信息增益，提高了速度和检测精度，同时具有较低的计算成本
{"title":"Integration of Fuzzy with Incremental Import Vector Machine for Intrusion Detection","authors":"A. Ramamoorthy, K. Karuppasamy","doi":"10.15837/ijccc.2022.3.4481","DOIUrl":"https://doi.org/10.15837/ijccc.2022.3.4481","url":null,"abstract":"IDM design and implementation remain a difficult undertaking and an unsolved research topic. Multi-dimensional irrelevant characteristics and duplicate information are included in the network dataset. To boost the effectiveness of IDM, a novel hybrid model is developed that combines Fuzzy Genetic Algorithms with Increment Import Vector Machines (FGA-I2VM), which works with huge amounts of both normal and aberrant network data with high detecting accuracy and low false alarm rates. The algorithms chosen for IDM in this stage are machine learning algorithms, which learn, find, and adapt patterns to changing situations over time. Pre-processing is the most essential stage in any IDM, and feature selection is utilized for pre-processing, which is the act of picking a collection or subset of relevant features for the purpose of creating a solution model. Information Gain (IG) is utilized in this FGA-I2VM model to pick features from the dataset for I2VM classification. To train the I2VM classifier, FGA uses three sets of operations to produce a new set of inhabitants with distinct patterns: cross over operation, selection, and finally mutation. The new population is then put into the Import Vector Machine, a strong classifier that has been used to solve a wide range of pattern recognition issues. FGA are quick, especially considering their capacity to discover global optima. Another advantage of FGA is their naturally parallel nature of assessing the individuals within a population. As a classifier, I2VM has self-tuning properties that allow patterns to attain global optimums. The FGA-efficacy I2VM model’s is complemented by information gain, which improves speed and detection accuracy while having a low computing cost","PeriodicalId":179619,"journal":{"name":"Int. J. Comput. Commun. Control","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132931232","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 : 2022-03-21DOI: 10.15837/ijccc.2022.3.3744
Cong Luo, Xiangbo Fan, Ying Yan, Han Jin, Xuan Wang
Identity authentication is one of the most basic components in the computer network world. It is the key technology of information security. It plays an important role in the protection of system and data security. Biometric recognition technology provides a reliable and convenient way for identity authentication. Compared with other biometric recognition technologies, face recognition has become a hot research topic because of its convenience, friendliness and easy acceptance. With the maturity and progress of face recognition technology, its commercial application has become more and more widespread. Internet finance, e-commerce and other asset-related areas have begun to try to use face recognition technology as a means of authentication, so people’s security needs for face recognition systems are also increasing. However, as a biometric recognition system, face recognition system still has inherent security vulnerabilities and faces security threats such as template attack and counterfeit attack. In view of this, this paper studies the application of threedimensional face recognition algorithm in the field of financial identity authentication. On the basis of feature extraction of face information using neural network algorithm, K-L transform is applied to image high-dimensional vector mapping to make face recognition clearer. Thus, the image loss can be reduced.
{"title":"Optimization of Three-dimensional Face Recognition Algorithms in Financial Identity Authentication","authors":"Cong Luo, Xiangbo Fan, Ying Yan, Han Jin, Xuan Wang","doi":"10.15837/ijccc.2022.3.3744","DOIUrl":"https://doi.org/10.15837/ijccc.2022.3.3744","url":null,"abstract":"Identity authentication is one of the most basic components in the computer network world. It is the key technology of information security. It plays an important role in the protection of system and data security. Biometric recognition technology provides a reliable and convenient way for identity authentication. Compared with other biometric recognition technologies, face recognition has become a hot research topic because of its convenience, friendliness and easy acceptance. With the maturity and progress of face recognition technology, its commercial application has become more and more widespread. Internet finance, e-commerce and other asset-related areas have begun to try to use face recognition technology as a means of authentication, so people’s security needs for face recognition systems are also increasing. However, as a biometric recognition system, face recognition system still has inherent security vulnerabilities and faces security threats such as template attack and counterfeit attack. In view of this, this paper studies the application of threedimensional face recognition algorithm in the field of financial identity authentication. On the basis of feature extraction of face information using neural network algorithm, K-L transform is applied to image high-dimensional vector mapping to make face recognition clearer. Thus, the image loss can be reduced.","PeriodicalId":179619,"journal":{"name":"Int. J. Comput. Commun. Control","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124940839","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 : 2022-03-21DOI: 10.15837/ijccc.2022.3.4482
Yahya M. Tashtoush, M. Al-Refai, Ghaith Al-refai, Dirar A. Darweesh, Noor Zaghal, Omar M. Darwish
Traditional traffic light system, which works based on fixed cycle can be a main reason for traffic jam, due to lack of adaptation to road conditions. Traffic jam has a bad impact on drivers and road users due to the time delay it causes for road users to reach their destinations. This delay can cause a life threat in case of emergency vehicles, such as ambulance vehicles and police cars. One key solution to solve traffic jam on intersections is the dynamic traffic lights, where traffic light operation adapts based on the intersection traffic conditions. Since few of researches projects in the literature interested in solving traffic jam problem for emergency vehicles, the contribution of this paper is to introduces a novel approach to operate traffic light system. The new approach consists of two algorithms which are pure operation mode and hybrid operation mode. These operation modes aim to reduce the waiting time of emergency vehicles on traffic intersections. They assume that there is a smart infrastructure system uses Internet of Things (IoT) that can detect emergency vehicles arrival to an intersection. The smart infrastructure system switches traffic light operation from fixed cycle mode to dynamic mode. The dynamic mode manages traffic lights at intersections to reduce the waiting time of emergency vehicles. The paper presents a simulation of the proposed algorithms, highlights their advantages. In order to evaluate the efficiency of the new technique, we compared our approach with Wen algorithm in the literature and the Traditional traffic light system. Our evaluation study indicated that the proposed algorithms outperformed Wen technique and the Traditional system under different traffic scenarios
{"title":"Dynamic Traffic Light System to Reduce The Waiting Time of Emergency Vehicles at Intersections within IoT Environment","authors":"Yahya M. Tashtoush, M. Al-Refai, Ghaith Al-refai, Dirar A. Darweesh, Noor Zaghal, Omar M. Darwish","doi":"10.15837/ijccc.2022.3.4482","DOIUrl":"https://doi.org/10.15837/ijccc.2022.3.4482","url":null,"abstract":"Traditional traffic light system, which works based on fixed cycle can be a main reason for traffic jam, due to lack of adaptation to road conditions. Traffic jam has a bad impact on drivers and road users due to the time delay it causes for road users to reach their destinations. This delay can cause a life threat in case of emergency vehicles, such as ambulance vehicles and police cars. One key solution to solve traffic jam on intersections is the dynamic traffic lights, where traffic light operation adapts based on the intersection traffic conditions. Since few of researches projects in the literature interested in solving traffic jam problem for emergency vehicles, the contribution of this paper is to introduces a novel approach to operate traffic light system. The new approach consists of two algorithms which are pure operation mode and hybrid operation mode. These operation modes aim to reduce the waiting time of emergency vehicles on traffic intersections. They assume that there is a smart infrastructure system uses Internet of Things (IoT) that can detect emergency vehicles arrival to an intersection. The smart infrastructure system switches traffic light operation from fixed cycle mode to dynamic mode. The dynamic mode manages traffic lights at intersections to reduce the waiting time of emergency vehicles. The paper presents a simulation of the proposed algorithms, highlights their advantages. In order to evaluate the efficiency of the new technique, we compared our approach with Wen algorithm in the literature and the Traditional traffic light system. Our evaluation study indicated that the proposed algorithms outperformed Wen technique and the Traditional system under different traffic scenarios","PeriodicalId":179619,"journal":{"name":"Int. J. Comput. Commun. Control","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133499727","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 : 2022-03-18DOI: 10.15837/ijccc.2022.2.4351
M. Kalaiarasu, C. Kumar
Sentiment Analysis is an important method in which many researchers are working on the automated approach for extraction and analysis of huge volumes of user achieved data, which are accessible on social networking websites. This approach helps in analyzing the direct falls under the domain of SA. SA comprises the vast field of effective classification of user-initiated text under defined polarities. The proposed work includes four major steps for solving these issues: the first step is preprocessing which holds tokenization, stop word removal, stemming, cleaning up of unwanted text information like removing of Ads from Web pages, Text normalization for converting binary format. Secondly, the Feature extraction is based on the Bag words, Word2Vec and TF-ID which is a Term Frequency-Inverse Document Frequency. Thirdly, this feature selection includes the procedure for examining semantic gaps along with source features using teaching models and this involves target task characteristic application for Improved Novel Convolutional Neural Network (INCNN). The Feature Selection accompanies the procedure of Information Gain (IG) and PCC which is a Pearson Correlation Coefficient. Finally, the classification step INCNN gives out sentiment posts and responses for the user-based post aspects which helps in enhancing the system performance. The experimental outcome proposes the INCNN algorithm and provides higher performance rather than the existing approach. The proposed INCNN classifier results in highest accuracy.
{"title":"Sentiment Analysis using Improved Novel Convolutional Neural Network (SNCNN)","authors":"M. Kalaiarasu, C. Kumar","doi":"10.15837/ijccc.2022.2.4351","DOIUrl":"https://doi.org/10.15837/ijccc.2022.2.4351","url":null,"abstract":"Sentiment Analysis is an important method in which many researchers are working on the automated approach for extraction and analysis of huge volumes of user achieved data, which are accessible on social networking websites. This approach helps in analyzing the direct falls under the domain of SA. SA comprises the vast field of effective classification of user-initiated text under defined polarities. The proposed work includes four major steps for solving these issues: the first step is preprocessing which holds tokenization, stop word removal, stemming, cleaning up of unwanted text information like removing of Ads from Web pages, Text normalization for converting binary format. Secondly, the Feature extraction is based on the Bag words, Word2Vec and TF-ID which is a Term Frequency-Inverse Document Frequency. Thirdly, this feature selection includes the procedure for examining semantic gaps along with source features using teaching models and this involves target task characteristic application for Improved Novel Convolutional Neural Network (INCNN). The Feature Selection accompanies the procedure of Information Gain (IG) and PCC which is a Pearson Correlation Coefficient. Finally, the classification step INCNN gives out sentiment posts and responses for the user-based post aspects which helps in enhancing the system performance. The experimental outcome proposes the INCNN algorithm and provides higher performance rather than the existing approach. The proposed INCNN classifier results in highest accuracy.","PeriodicalId":179619,"journal":{"name":"Int. J. Comput. Commun. Control","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115443622","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 : 2022-03-14DOI: 10.15837/ijccc.2022.3.4371
A. Aljumah
Wildfires are one of the most devastating catastrophes and can inflict tremendous losses to life and nature. Moreover, the loss of civilization is incomprehensible, potentially extending suddenly over vast land sectors. Global warming has contributed to increased forest fires, but it needs immediate attention from the organizations involved. This analysis aims to forecast forest fires to reduce losses and take decisive measures in the direction of protection. Specifically, this study suggests an energy-efficient IoT architecture for the early detection of wildfires backed by fog-cloud computing technologies. To evaluate the repeatable information obtained from IoT sensors in a time-sensitive manner, Jaccard similarity analysis is used. This data is assessed in the fog processing layer and reduces the single value of multidimensional data called the Forest Fire Index. Finally, based on Wildfire Triggering Criteria, the Artificial Neural Network (ANN) is used to simulate the susceptibility of the forest area. ANN are intelligent techniques for inferring future outputs as these can be made hybrid with fuzzy methods for decision-modeling. For productive visualization of the geographical location of wildfire vulnerability, the Self-Organized Mapping Technique is used. Simulation of the implementation is done over multiple datasets. For total efficiency assessment, outcomes are contrasted in comparison to other techniques.
{"title":"IoT-inspired Framework for Real-time Prediction of Forest Fire","authors":"A. Aljumah","doi":"10.15837/ijccc.2022.3.4371","DOIUrl":"https://doi.org/10.15837/ijccc.2022.3.4371","url":null,"abstract":"Wildfires are one of the most devastating catastrophes and can inflict tremendous losses to life and nature. Moreover, the loss of civilization is incomprehensible, potentially extending suddenly over vast land sectors. Global warming has contributed to increased forest fires, but it needs immediate attention from the organizations involved. This analysis aims to forecast forest fires to reduce losses and take decisive measures in the direction of protection. Specifically, this study suggests an energy-efficient IoT architecture for the early detection of wildfires backed by fog-cloud computing technologies. To evaluate the repeatable information obtained from IoT sensors in a time-sensitive manner, Jaccard similarity analysis is used. This data is assessed in the fog processing layer and reduces the single value of multidimensional data called the Forest Fire Index. Finally, based on Wildfire Triggering Criteria, the Artificial Neural Network (ANN) is used to simulate the susceptibility of the forest area. ANN are intelligent techniques for inferring future outputs as these can be made hybrid with fuzzy methods for decision-modeling. For productive visualization of the geographical location of wildfire vulnerability, the Self-Organized Mapping Technique is used. Simulation of the implementation is done over multiple datasets. For total efficiency assessment, outcomes are contrasted in comparison to other techniques.","PeriodicalId":179619,"journal":{"name":"Int. J. Comput. Commun. Control","volume":"620 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134483086","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 : 2022-03-14DOI: 10.15837/ijccc.2022.3.4697
H. Bouhamed, Monia Hamdi, R. Gargouri
This paper described a suggested model to predict bed occupancy for Covid-19 patients by country during the rapid spread of the Omicron variant. This model can be used to make decisions on the introduction or alleviation of restrictive measures and on the prediction of oxygen and health human resource requirements. To predict Covid-19 hospital occupancy, we tested some recurrent deep learning architectures. To train the model, we referred to Covid-19 hospital occupancy data from 15 countries whose curves started their regressions during January 2022. The studied period covers the month of December 2021 and the beginning of January 2022, which represents the period of strong contagion of the omicron variant around the world. The evolution sequences of hospital occupancy, vaccination percentages and median ages of populations were used to train our model. The results are very promising which could help to better manage the current pandemic peak.
{"title":"Covid-19 Patients' Hospital Occupancy Prediction During the Recent Omicron Wave via some Recurrent Deep Learning Architectures","authors":"H. Bouhamed, Monia Hamdi, R. Gargouri","doi":"10.15837/ijccc.2022.3.4697","DOIUrl":"https://doi.org/10.15837/ijccc.2022.3.4697","url":null,"abstract":"This paper described a suggested model to predict bed occupancy for Covid-19 patients by country during the rapid spread of the Omicron variant. This model can be used to make decisions on the introduction or alleviation of restrictive measures and on the prediction of oxygen and health human resource requirements. To predict Covid-19 hospital occupancy, we tested some recurrent deep learning architectures. To train the model, we referred to Covid-19 hospital occupancy data from 15 countries whose curves started their regressions during January 2022. The studied period covers the month of December 2021 and the beginning of January 2022, which represents the period of strong contagion of the omicron variant around the world. The evolution sequences of hospital occupancy, vaccination percentages and median ages of populations were used to train our model. The results are very promising which could help to better manage the current pandemic peak.","PeriodicalId":179619,"journal":{"name":"Int. J. Comput. Commun. Control","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123075600","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 : 2022-03-07DOI: 10.15837/ijccc.2022.2.4296
Umamageswaran Jambulingam, K. Balasubadra
The use of the multi-cloud data storage in one heterogeneous service is a polynimbus cloud strategy. Cloud computing uses a pay-as-you-go model to deliver services to a variety of end users. Customers can outsource daunting tasks to cloud data centres for processing and producing results, thanks to cloud computing. Cloud computing becomes the popular IT brand that provides various on-demand services over the internet. This technology is devoted to distributing computer and software resources. The proven usefulness of workflows to enforce relevant scientific achievements is the availability of data from advanced scientific tools. Scheduling algorithms are essential in order to automate these strenuous workflows efficiently. A number of new heuristics based on a Cloud resource model have been developed. The majority of these heuristic - based address QoS issues in one or two dimensions. The cloud computing technology offers a decentralised pool of services and resources with various models that are provided to the customers across the Internet in an on-demand, continuously distributed, and pay-per-use model. The key challenge we address in this paper is to maximise revenue while maintaining a minimum consumption of energy with an enhanced QoS for resource allocation. The obtained results from proposed method when compared with the existing state of art methods observed to be novel and better.
{"title":"A Unique Multi-Agent-Based Approach for Enhanced QoS Resource Allocation in Multi Cloud Environment while Maintaining Minimized Energy and Maximize Revenue","authors":"Umamageswaran Jambulingam, K. Balasubadra","doi":"10.15837/ijccc.2022.2.4296","DOIUrl":"https://doi.org/10.15837/ijccc.2022.2.4296","url":null,"abstract":"The use of the multi-cloud data storage in one heterogeneous service is a polynimbus cloud strategy. Cloud computing uses a pay-as-you-go model to deliver services to a variety of end users. Customers can outsource daunting tasks to cloud data centres for processing and producing results, thanks to cloud computing. Cloud computing becomes the popular IT brand that provides various on-demand services over the internet. This technology is devoted to distributing computer and software resources. The proven usefulness of workflows to enforce relevant scientific achievements is the availability of data from advanced scientific tools. Scheduling algorithms are essential in order to automate these strenuous workflows efficiently. A number of new heuristics based on a Cloud resource model have been developed. The majority of these heuristic - based address QoS issues in one or two dimensions. The cloud computing technology offers a decentralised pool of services and resources with various models that are provided to the customers across the Internet in an on-demand, continuously distributed, and pay-per-use model. The key challenge we address in this paper is to maximise revenue while maintaining a minimum consumption of energy with an enhanced QoS for resource allocation. The obtained results from proposed method when compared with the existing state of art methods observed to be novel and better.","PeriodicalId":179619,"journal":{"name":"Int. J. Comput. Commun. Control","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125599803","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 : 2022-03-05DOI: 10.15837/ijccc.2022.2.4741
Olimpia I. Ban, L. Droj, Delia A. Tușe, G. Droj, N. Bugnar
Likert-type scales are a common technique used in social science. Plus, the Likert scale is among the most frequently used psychometric tools in social sciences and educational research. Despite its frequently used, the Likert scale raises up many questions mark. We can say that the use of the Likert scale in its classical form is too rigid and loses valuable information. Li (2013, p. 1613) calls on previous studies that "have claimed that fuzzy scales are more accurate than traditional scales due to the continuous nature of fuzzy sets". The aim of this research is to reduce the inaccuracy caused by the use of the Likert scale, by proposing a method of more appropriate processing of data collected in this way. As shown in this paper, fuzzy methods can be a good alternative. The research methodology consists of using the usual technique on the set of fuzzy numbers by considering the input data as linguistic variables, subsequently identified by triangular fuzzy numbers. The obtained scale is more elastic with respect to the input data, therefore it better captures the reality. The newly proposed method is applied in the concrete example of the competitors in the hotel field. The Importance-Performance Competitor Analysis is utilized. A weakness of the method is due to the use in its application of data collection with the Likert scale. The results conclude on the situation of the competitors regarding each attribute considered as in the crisp version of the method, but the identification and processing of data correspond better to the aspects of subjectivity and uncertainty specific to human thinking. A novelty is also the obtaining of a hierarchy within each category of attributes from the quadrants proposed by the Important-Performance Analysis in relation to the competition.
李克特量表是社会科学中常用的一种方法。此外，李克特量表是社会科学和教育研究中最常用的心理测量工具之一。尽管李克特量表被频繁使用，但它也引发了许多问题。我们可以说，使用经典形式的李克特量表过于死板，失去了有价值的信息。Li (2013, p. 1613)引用了先前的研究，“由于模糊集的连续性，模糊尺度比传统尺度更准确”。本研究的目的是通过提出一种更合适的方法来处理以这种方式收集的数据，以减少使用李克特量表造成的不准确性。如本文所示，模糊方法是一种很好的替代方法。本文的研究方法是将输入数据作为语言变量，然后用三角模糊数识别模糊数。得到的尺度相对于输入数据更有弹性，因此能更好地捕捉现实。并将该方法应用于酒店行业竞争对手的具体案例中。运用了竞争对手重要性-绩效分析。该方法的一个弱点是由于在应用中使用李克特量表收集数据。结果得出的结论是，在该方法的清晰版本中，考虑到每个属性的竞争对手的情况，但数据的识别和处理更符合人类思维的主观性和不确定性方面。新颖之处还在于从重要性绩效分析提出的与竞争相关的象限中获得每个类别属性的层次结构。
{"title":"Data Processing by Fuzzy Methods in Social Sciences Researches. Example in Hospitality Industry","authors":"Olimpia I. Ban, L. Droj, Delia A. Tușe, G. Droj, N. Bugnar","doi":"10.15837/ijccc.2022.2.4741","DOIUrl":"https://doi.org/10.15837/ijccc.2022.2.4741","url":null,"abstract":"Likert-type scales are a common technique used in social science. Plus, the Likert scale is among the most frequently used psychometric tools in social sciences and educational research. Despite its frequently used, the Likert scale raises up many questions mark. We can say that the use of the Likert scale in its classical form is too rigid and loses valuable information. Li (2013, p. 1613) calls on previous studies that \"have claimed that fuzzy scales are more accurate than traditional scales due to the continuous nature of fuzzy sets\". The aim of this research is to reduce the inaccuracy caused by the use of the Likert scale, by proposing a method of more appropriate processing of data collected in this way. As shown in this paper, fuzzy methods can be a good alternative. The research methodology consists of using the usual technique on the set of fuzzy numbers by considering the input data as linguistic variables, subsequently identified by triangular fuzzy numbers. The obtained scale is more elastic with respect to the input data, therefore it better captures the reality. The newly proposed method is applied in the concrete example of the competitors in the hotel field. The Importance-Performance Competitor Analysis is utilized. A weakness of the method is due to the use in its application of data collection with the Likert scale. The results conclude on the situation of the competitors regarding each attribute considered as in the crisp version of the method, but the identification and processing of data correspond better to the aspects of subjectivity and uncertainty specific to human thinking. A novelty is also the obtaining of a hierarchy within each category of attributes from the quadrants proposed by the Important-Performance Analysis in relation to the competition.","PeriodicalId":179619,"journal":{"name":"Int. J. Comput. Commun. Control","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126805189","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 : 2022-02-20DOI: 10.15837/ijccc.2022.2.4732
F. Filip
Collaboration means in substance that several entities such as humans, computers, robots, enterprises and so on jointly perform a certain task instead of working individually so that a better result could be obtained. Decision-making is a specific form of activity, commonly carried out by human agents, which is meant to eventually select a certain course of action which is expected to result in attaining a desired result. The chapter is meant to present a concise and balanced view of the basic concepts and main classes of supporting information and communication tools and systems regarding decision-making processes carried out by several collaborating human agents called participants. The reasons for collaboration are briefly explained followed by an exposure of collaboration application in the multi-participant decision-making settings. Having presented the classification of decision problems and decision-making units, the main phases of a specific multi-participant form of Herbert Simon’s decision process model are described followed by the presentation of two main forms of close and soft collaboration, namely consensus building and crowdsourcing, respectively. The need for technology support offered to collaborating participants is justified and two main classes of decision supporting systems, namely Decision support systems and the ever more largely used platforms, are addressed. A practical example of an open ended and evolving platform is presented. Open questions about the further role the information and communication tools in multi-participant decision-making processes are eventually formulated from two perspectives, digital humanism and dataism, respectively.
{"title":"Collaborative Decision-Making: Concepts and Supporting Information and Communication Technology Tools and Systems","authors":"F. Filip","doi":"10.15837/ijccc.2022.2.4732","DOIUrl":"https://doi.org/10.15837/ijccc.2022.2.4732","url":null,"abstract":"Collaboration means in substance that several entities such as humans, computers, robots, enterprises and so on jointly perform a certain task instead of working individually so that a better result could be obtained. Decision-making is a specific form of activity, commonly carried out by human agents, which is meant to eventually select a certain course of action which is expected to result in attaining a desired result. The chapter is meant to present a concise and balanced view of the basic concepts and main classes of supporting information and communication tools and systems regarding decision-making processes carried out by several collaborating human agents called participants. The reasons for collaboration are briefly explained followed by an exposure of collaboration application in the multi-participant decision-making settings. Having presented the classification of decision problems and decision-making units, the main phases of a specific multi-participant form of Herbert Simon’s decision process model are described followed by the presentation of two main forms of close and soft collaboration, namely consensus building and crowdsourcing, respectively. The need for technology support offered to collaborating participants is justified and two main classes of decision supporting systems, namely Decision support systems and the ever more largely used platforms, are addressed. A practical example of an open ended and evolving platform is presented. Open questions about the further role the information and communication tools in multi-participant decision-making processes are eventually formulated from two perspectives, digital humanism and dataism, respectively.","PeriodicalId":179619,"journal":{"name":"Int. J. Comput. Commun. Control","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129163243","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 : 2022-02-18DOI: 10.15837/ijccc.2022.2.4356
J. Anitha, N. Saranya
Agriculture is the primary source of livelihood for about 60% of the world's total population according to the Food and Agricultural Organization (FAO). The economy of the developing countries is solely dependent on agriculture commodities. As the world population is increasing at faster pace, the demand for food is also escalating tremendously. In recent days, agriculture is experiencing an automation revolution. Hence the introduction of disruptive technologies like Artificial Intelligence plays a major role in increasing agricultural productivity. AI enabled approaches would help in overcoming the traditional challenges faced in agriculture practices, by automating various agriculture related tasks. Nowadays, farmers adopt precision farming which uses AI techniques namely in crop health monitoring, weed detection, plant disease identification and detection, and forecast weather, commodity prices to increase the yield. As there is scarcity of manpower in agriculture sector, AI based equipment like bots and drones are used widely. Crop diseases are a major threat to food security and the manual identification of the diseases with the help of experts will incur more cost and time, especially for larger farms. The machine-vision based techniques provide image based automatic process control, inspection, and robot guidance for pest and disease control. It provides automated process in agriculture, paving way for improved efficiency and profitability. Various factors contribute for plant diseases, which includes soil health, climatic conditions, species and pests. The proposed chapter elaborates on the use of deep learning techniques in the leaf disease detection of Cassava plants. The chapter initially describes the evolution of various neural network techniques used in classification and prediction. It describes the significance of using Convolutional Neural Network (CNN) over deep neural networks. The chapter focuses on classification of leaf disease in Cassava plants using images acquired real time and from Kaggle dataset. In the final part of the chapter, the results of the models with original and augmented data were illustrated considering accuracy as performance metric.
{"title":"Cassava Leaf Disease Identification and Detection Using Deep Learning Approach","authors":"J. Anitha, N. Saranya","doi":"10.15837/ijccc.2022.2.4356","DOIUrl":"https://doi.org/10.15837/ijccc.2022.2.4356","url":null,"abstract":"Agriculture is the primary source of livelihood for about 60% of the world's total population according to the Food and Agricultural Organization (FAO). The economy of the developing countries is solely dependent on agriculture commodities. As the world population is increasing at faster pace, the demand for food is also escalating tremendously. In recent days, agriculture is experiencing an automation revolution. Hence the introduction of disruptive technologies like Artificial Intelligence plays a major role in increasing agricultural productivity. AI enabled approaches would help in overcoming the traditional challenges faced in agriculture practices, by automating various agriculture related tasks. Nowadays, farmers adopt precision farming which uses AI techniques namely in crop health monitoring, weed detection, plant disease identification and detection, and forecast weather, commodity prices to increase the yield. As there is scarcity of manpower in agriculture sector, AI based equipment like bots and drones are used widely. Crop diseases are a major threat to food security and the manual identification of the diseases with the help of experts will incur more cost and time, especially for larger farms. The machine-vision based techniques provide image based automatic process control, inspection, and robot guidance for pest and disease control. It provides automated process in agriculture, paving way for improved efficiency and profitability. Various factors contribute for plant diseases, which includes soil health, climatic conditions, species and pests. The proposed chapter elaborates on the use of deep learning techniques in the leaf disease detection of Cassava plants. The chapter initially describes the evolution of various neural network techniques used in classification and prediction. It describes the significance of using Convolutional Neural Network (CNN) over deep neural networks. The chapter focuses on classification of leaf disease in Cassava plants using images acquired real time and from Kaggle dataset. In the final part of the chapter, the results of the models with original and augmented data were illustrated considering accuracy as performance metric.","PeriodicalId":179619,"journal":{"name":"Int. J. Comput. Commun. Control","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126469577","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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