Pub Date : 2023-06-10DOI: 10.52547/jist.34031.11.42.102
Elham Askari, Sara Motamed
The Agnosia is a neurological condition that leads to an inability to name, recognize, and extract meaning from the visual, auditory, and sensory environment, despite the fact that the receptor organ is perfect. Visual agnosia is the most common type of this disorder. People with agnosia have trouble communicating between the mind and the brain. As a result, they cannot understand the images seen. In this paper, a model is proposed that is based on the visual pathway so that it first receives the visual stimulus and then, after understanding, the object is identified. In this paper, a model based on the visual pathway is proposed and using intelligent Fuzzy Cognitive Map will help improve image processing in the minds of these patients. First, the proposed model that is inspired by the visual perception pathway, is designed. Then, appropriate attributes that include the texture and color of the images are extracted and the concept of the seen image is perceived using Fuzzy Cognitive Mapping, the meaning recognition and the relationships between objects. This model reduces the difficulty of perceiving and recognizing objects in patients with visual agnosia. The results show that the proposed model, with 98.1% accuracy, shows better performance than other methods.
{"title":"Computational Model for Image Processing in the Minds of People with Visual Agnosia using Fuzzy Cognitive Map","authors":"Elham Askari, Sara Motamed","doi":"10.52547/jist.34031.11.42.102","DOIUrl":"https://doi.org/10.52547/jist.34031.11.42.102","url":null,"abstract":"The Agnosia is a neurological condition that leads to an inability to name, recognize, and extract meaning from the visual, auditory, and sensory environment, despite the fact that the receptor organ is perfect. Visual agnosia is the most common type of this disorder. People with agnosia have trouble communicating between the mind and the brain. As a result, they cannot understand the images seen. In this paper, a model is proposed that is based on the visual pathway so that it first receives the visual stimulus and then, after understanding, the object is identified. In this paper, a model based on the visual pathway is proposed and using intelligent Fuzzy Cognitive Map will help improve image processing in the minds of these patients. First, the proposed model that is inspired by the visual perception pathway, is designed. Then, appropriate attributes that include the texture and color of the images are extracted and the concept of the seen image is perceived using Fuzzy Cognitive Mapping, the meaning recognition and the relationships between objects. This model reduces the difficulty of perceiving and recognizing objects in patients with visual agnosia. The results show that the proposed model, with 98.1% accuracy, shows better performance than other methods.","PeriodicalId":37681,"journal":{"name":"Journal of Information Systems and Telecommunication","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135050996","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 : 2023-06-10DOI: 10.52547/jist.24214.11.42.110
Shanmuga Sundari M, Rudra Kalyan Nayak, Vijaya Chandra Jadala, Sai Kiran Pasupuleti
All service and manufacturing businesses are resilient and strive for a more efficient and better end in today's world. Data mining is data-driven and necessitates significant data to analyze the pattern and train the model. Assume the data is incorrect and was not collected from reliable sources, causing the analysis to be skewed. We introduce a procedure in which the dataset is split into test and training datasets with a specific ratio to overcome this challenge. Process mining will find the traces of actions to streamline the process and aid data mining in producing a more efficient result. The most responsible domain is the healthcare industry. In this study, we used the activity data from the hospital and applied process mining algorithms such as alpha miner and fuzzy miner. Process mining is used to check for conformity in the event log and do performance analysis, and a pattern of accuracy is exhibited. Finally, we used process mining techniques to show the deviation flow and fix the process flow. This study showed that there was a variation in the flow by employing alpha and fuzzy miners in the hospital.
{"title":"Performance Analysis and Activity Deviation Discovery in Event Log Using Process Mining Tool for Hospital System","authors":"Shanmuga Sundari M, Rudra Kalyan Nayak, Vijaya Chandra Jadala, Sai Kiran Pasupuleti","doi":"10.52547/jist.24214.11.42.110","DOIUrl":"https://doi.org/10.52547/jist.24214.11.42.110","url":null,"abstract":"All service and manufacturing businesses are resilient and strive for a more efficient and better end in today's world. Data mining is data-driven and necessitates significant data to analyze the pattern and train the model. Assume the data is incorrect and was not collected from reliable sources, causing the analysis to be skewed. We introduce a procedure in which the dataset is split into test and training datasets with a specific ratio to overcome this challenge. Process mining will find the traces of actions to streamline the process and aid data mining in producing a more efficient result. The most responsible domain is the healthcare industry. In this study, we used the activity data from the hospital and applied process mining algorithms such as alpha miner and fuzzy miner. Process mining is used to check for conformity in the event log and do performance analysis, and a pattern of accuracy is exhibited. Finally, we used process mining techniques to show the deviation flow and fix the process flow. This study showed that there was a variation in the flow by employing alpha and fuzzy miners in the hospital.","PeriodicalId":37681,"journal":{"name":"Journal of Information Systems and Telecommunication","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135050998","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 : 2023-06-10DOI: 10.52547/jist.27279.11.42.123
Malihe Bahekmat, Mohammad Hossein Yaghmaee Moghaddam
Reliability of data transmission in wireless sensor networks (WSN) is very important in the case of high lost packet rate due to link problems or buffer congestion. In this regard, mechanisms such as middle cache points and congestion control can improve the performance of the reliability of transmission protocols when the packet is lost. On the other hand, the issue of energy consumption in this type of networks has become an important parameter in their reliability. In this paper, considering the energy constraints in the sensor nodes and the direct relationship between energy consumption and the number of transmissions made by the nodes, the system tries to reduce the number of transmissions needed to send a packet from source to destination as much as possible by optimal selection of the cache points and packet caching. In order to select the best cache points, the information extracted from the network behavior analysis by deep learning algorithm has been used. In the training phase, long-short term memory (LSTM) capabilities as an example of recurrent neural network (RNN) deep learning networks to learn network conditions. The results show that the proposed method works better in examining the evaluation criteria of transmission costs, end-to-end delays, cache use and throughput.
{"title":"Cache Point Selection and Transmissions Reduction using LSTM Neural Network","authors":"Malihe Bahekmat, Mohammad Hossein Yaghmaee Moghaddam","doi":"10.52547/jist.27279.11.42.123","DOIUrl":"https://doi.org/10.52547/jist.27279.11.42.123","url":null,"abstract":"Reliability of data transmission in wireless sensor networks (WSN) is very important in the case of high lost packet rate due to link problems or buffer congestion. In this regard, mechanisms such as middle cache points and congestion control can improve the performance of the reliability of transmission protocols when the packet is lost. On the other hand, the issue of energy consumption in this type of networks has become an important parameter in their reliability. In this paper, considering the energy constraints in the sensor nodes and the direct relationship between energy consumption and the number of transmissions made by the nodes, the system tries to reduce the number of transmissions needed to send a packet from source to destination as much as possible by optimal selection of the cache points and packet caching. In order to select the best cache points, the information extracted from the network behavior analysis by deep learning algorithm has been used. In the training phase, long-short term memory (LSTM) capabilities as an example of recurrent neural network (RNN) deep learning networks to learn network conditions. The results show that the proposed method works better in examining the evaluation criteria of transmission costs, end-to-end delays, cache use and throughput.","PeriodicalId":37681,"journal":{"name":"Journal of Information Systems and Telecommunication","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135050997","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}
Elham Gholam, Seyed Reza Kamel Tabbakh, M. Khairabadi
Gastric cancer is the second most common cancer worldwide, responsible for the death of many people in society. One of the issues regarding this disease is the absence of early and accurate detection. In the medical industry, gastric cancer is diagnosed by conducting numerous tests and imagings, which are costly and time-consuming. Therefore, doctors are seeking a cost-effective and time-efficient alternative. One of the medical solutions is Chinese medicine and diagnosis by observing changes of the tongue. Detecting the disease using tongue appearance and color of various sections of the tongue is one of the key components of traditional Chinese medicine. In this study, a method is presented which can carry out the localization of tongue surface regardless of the different poses of people in images. In fact, if the localization of face components, especially the mouth, is done correctly, the components leading to the biggest distinction in the dataset can be used which is favorable in terms of time and space complexity. Also, since we have the best estimation, the best features can be extracted relative to those components and the best possible accuracy can be achieved in this situation. The extraction of appropriate features in this study is done using deep convolutional neural networks. Finally, we use the random forest algorithm to train the proposed model and evaluate the criteria. Experimental results show that the average classification accuracy has reached approximately 73.78 which demonstrates the superiority of the proposed method compared to other methods.
{"title":"Diagnosis of Gastric Cancer via Classification of the Tongue Images using Deep\u0000 Convolutional Networks","authors":"Elham Gholam, Seyed Reza Kamel Tabbakh, M. Khairabadi","doi":"10.52547/jist.9.35.191","DOIUrl":"https://doi.org/10.52547/jist.9.35.191","url":null,"abstract":"Gastric cancer is the second most common cancer worldwide, responsible for the death of many people in society. One of the issues regarding this disease is the absence of early and accurate detection. In the medical industry, gastric cancer is diagnosed by conducting numerous tests and imagings, which are costly and time-consuming. Therefore, doctors are seeking a cost-effective and time-efficient alternative. One of the medical solutions is Chinese medicine and diagnosis by observing changes of the tongue. Detecting the disease using tongue appearance and color of various sections of the tongue is one of the key components of traditional Chinese medicine. In this study, a method is presented which can carry out the localization of tongue surface regardless of the different poses of people in images. In fact, if the localization of face components, especially the mouth, is done correctly, the components leading to the biggest distinction in the dataset can be used which is favorable in terms of time and space complexity. Also, since we have the best estimation, the best features can be extracted relative to those components and the best possible accuracy can be achieved in this situation. The extraction of appropriate features in this study is done using deep convolutional neural networks. Finally, we use the random forest algorithm to train the proposed model and evaluate the criteria. Experimental results show that the average classification accuracy has reached approximately 73.78 which demonstrates the superiority of the proposed method compared to other methods.","PeriodicalId":37681,"journal":{"name":"Journal of Information Systems and Telecommunication","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70688710","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}
Sensor nodes are typically less mobile, much limited in capabilities, and more densely deployed than the traditional wired networks as well as mobile ad-hoc networks. General Wireless Sensor Networks (WSNs) are designed with electro-mechanical sensors through wireless data communication. Nowadays the WSN has become ubiquitous. WSN is used in combination with Internet of Things and in many Big Data applications, it is used in the lower layer for data collection. It is deployed in combination with several high end networks. All the higher layer networks and application layer services depend on the low level WSN in the deployment site. So to achieve energy efficiency in the overall network some simplification strategies have to be carried out not only in the Medium Access Control (MAC) layer but also in the network and transport layers. An energy efficient algorithm for scheduling and clustering is proposed and described in detail. The proposed methodology clusters the nodes using a traditional yet simplified approach of hierarchically sorting the sensor nodes. Few important works on cross layer protocols for WSNs are reviewed and an attempt to modify their pattern has also been presented in this paper with results. Comparison with few prominent protocols in this domain has also been made. As a result of the comparison one would get a basic idea of using which type of scheduling algorithm for which type of monitoring applications.
{"title":"Energy Efficient Cross Layer MAC Protocol for Wireless Sensor Networks in\u0000 Remote Area Monitoring Applications","authors":"R. Rathna, L. Gladence, J. Cynthia, V. Anu","doi":"10.52547/jist.9.35.207","DOIUrl":"https://doi.org/10.52547/jist.9.35.207","url":null,"abstract":"Sensor nodes are typically less mobile, much limited in capabilities, and more densely deployed than the traditional wired networks as well as mobile ad-hoc networks. General Wireless Sensor Networks (WSNs) are designed with electro-mechanical sensors through wireless data communication. Nowadays the WSN has become ubiquitous. WSN is used in combination with Internet of Things and in many Big Data applications, it is used in the lower layer for data collection. It is deployed in combination with several high end networks. All the higher layer networks and application layer services depend on the low level WSN in the deployment site. So to achieve energy efficiency in the overall network some simplification strategies have to be carried out not only in the Medium Access Control (MAC) layer but also in the network and transport layers. An energy efficient algorithm for scheduling and clustering is proposed and described in detail. The proposed methodology clusters the nodes using a traditional yet simplified approach of hierarchically sorting the sensor nodes. Few important works on cross layer protocols for WSNs are reviewed and an attempt to modify their pattern has also been presented in this paper with results. Comparison with few prominent protocols in this domain has also been made. As a result of the comparison one would get a basic idea of using which type of scheduling algorithm for which type of monitoring applications.","PeriodicalId":37681,"journal":{"name":"Journal of Information Systems and Telecommunication","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44530827","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}
The videos are more popular for sharing content on social media to capture the audience’s attention. The artificial manipulation of videos is growing rapidly to make the videos flashy and interesting but they can easily misuse to spread false information on social media platforms. Deep Fake is a problematic method for the manipulation of videos in which artificial components are added to the video using emerging deep learning techniques. Due to the increase in the accuracy of deep fake generation methods, artificially created videos are no longer detectable and pose a major threat to social media users. To address this growing problem, we have proposed a new method for detecting deep fake videos using 3D Inflated Xception Net with Discrete Fourier Transformation. Xception Net was originally designed for application on 2D images only. The proposed method is the first attempt to use a 3D Xception Net for categorizing video-based data. The advantage of the proposed method is, it works on the whole video rather than the subset of frames while categorizing. Our proposed model was tested on the popular dataset Celeb-DF and achieved better accuracy.
{"title":"DeepFake Detection using 3D-Xception Net with Discrete Fourier\u0000 Transformation","authors":"Adeep Biswas, Debayan Bhattacharya, Anil Kumar Kakelli","doi":"10.52547/jist.9.35.161","DOIUrl":"https://doi.org/10.52547/jist.9.35.161","url":null,"abstract":"The videos are more popular for sharing content on social media to capture the audience’s attention. The artificial manipulation of videos is growing rapidly to make the videos flashy and interesting but they can easily misuse to spread false information on social media platforms. Deep Fake is a problematic method for the manipulation of videos in which artificial components are added to the video using emerging deep learning techniques. Due to the increase in the accuracy of deep fake generation methods, artificially created videos are no longer detectable and pose a major threat to social media users. To address this growing problem, we have proposed a new method for detecting deep fake videos using 3D Inflated Xception Net with Discrete Fourier Transformation. Xception Net was originally designed for application on 2D images only. The proposed method is the first attempt to use a 3D Xception Net for categorizing video-based data. The advantage of the proposed method is, it works on the whole video rather than the subset of frames while categorizing. Our proposed model was tested on the popular dataset Celeb-DF and achieved better accuracy.","PeriodicalId":37681,"journal":{"name":"Journal of Information Systems and Telecommunication","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70688497","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}
Network coverage is one of the most important challenges in wireless sensor networks (WSNs). In a WSN, each sensor node has a sensing area coverage based on its sensing range. In most applications, sensor nodes are randomly deployed in the environment which causes the density of nodes become high in some areas and low in some other. In this case, some areas are not covered by none of sensor nodes which these areas are called coverage holes. Also, creating areas with high density leads to redundant overlapping and as a result the network lifetime decreases. In this paper, a cluster-based scheme for the coverage problem of WSNs using learning automata is proposed. In the proposed scheme, each node creates the action and probability vectors of learning automata for itself and its neighbors, then determines the status of itself and all its neighbors and finally sends them to the cluster head (CH). Afterward, each CH starts to reward or penalize the vectors and sends the results to the sender for updating purposes. Thereafter, among the sent vectors, the CH node selects the best action vector and broadcasts it in the form of a message inside the cluster. Finally, each member changes its status in accordance with the vector included in the received message from the corresponding CH and the active sensor nodes perform environment monitoring operations. The simulation results show that the proposed scheme improves the network coverage and the energy consumption.
{"title":"Cluster-based Coverage Scheme for Wireless Sensor Networks using Learning\u0000 Automata","authors":"A. Ghaffari, S. Mousavi","doi":"10.52547/jist.9.35.197","DOIUrl":"https://doi.org/10.52547/jist.9.35.197","url":null,"abstract":"Network coverage is one of the most important challenges in wireless sensor networks (WSNs). In a WSN, each sensor node has a sensing area coverage based on its sensing range. In most applications, sensor nodes are randomly deployed in the environment which causes the density of nodes become high in some areas and low in some other. In this case, some areas are not covered by none of sensor nodes which these areas are called coverage holes. Also, creating areas with high density leads to redundant overlapping and as a result the network lifetime decreases. In this paper, a cluster-based scheme for the coverage problem of WSNs using learning automata is proposed. In the proposed scheme, each node creates the action and probability vectors of learning automata for itself and its neighbors, then determines the status of itself and all its neighbors and finally sends them to the cluster head (CH). Afterward, each CH starts to reward or penalize the vectors and sends the results to the sender for updating purposes. Thereafter, among the sent vectors, the CH node selects the best action vector and broadcasts it in the form of a message inside the cluster. Finally, each member changes its status in accordance with the vector included in the received message from the corresponding CH and the active sensor nodes perform environment monitoring operations. The simulation results show that the proposed scheme improves the network coverage and the energy consumption.","PeriodicalId":37681,"journal":{"name":"Journal of Information Systems and Telecommunication","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70688720","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}
Mehdi Farrokhbakht Foumani, Sajad Moazami Goudarzi
Sales forecasting is one of the significant issues in the industrial and service sector which can lead to facilitated management decisions and reduce the lost values in case of being dealt with properly. Also sales forecasting is one of the complicated problems in analyzing time series and data mining due to the number of intervening parameters. Various models were presented on this issue and each one found acceptable results. However, developing the methods in this study is still considered by researchers. In this regard, the present study provided a hybrid model with error feedback for sales forecasting. In this study, forecasting was conducted using a supervised learning method. Then, the remaining values (model error) were specified and the error values were forecasted using another learning method. Finally, two trained models were combined together and consecutively used for sales forecasting. In other words, first the forecasting was conducted and then the error rate was determined by the second model. The total forecasting and model error indicated the final forecasting. The computational results obtained from numerical experiments indicated the superiority of the proposed hybrid method performance over the common models in the available literature and reduced the indicators related to forecasting error.
{"title":"The Development of a Hybrid Error Feedback Model for Sales\u0000 Forecasting","authors":"Mehdi Farrokhbakht Foumani, Sajad Moazami Goudarzi","doi":"10.52547/JIST.9.34.131","DOIUrl":"https://doi.org/10.52547/JIST.9.34.131","url":null,"abstract":"Sales forecasting is one of the significant issues in the industrial and service sector which can lead to facilitated management decisions and reduce the lost values in case of being dealt with properly. Also sales forecasting is one of the complicated problems in analyzing time series and data mining due to the number of intervening parameters. Various models were presented on this issue and each one found acceptable results. However, developing the methods in this study is still considered by researchers. In this regard, the present study provided a hybrid model with error feedback for sales forecasting. In this study, forecasting was conducted using a supervised learning method. Then, the remaining values (model error) were specified and the error values were forecasted using another learning method. Finally, two trained models were combined together and consecutively used for sales forecasting. In other words, first the forecasting was conducted and then the error rate was determined by the second model. The total forecasting and model error indicated the final forecasting. The computational results obtained from numerical experiments indicated the superiority of the proposed hybrid method performance over the common models in the available literature and reduced the indicators related to forecasting error.","PeriodicalId":37681,"journal":{"name":"Journal of Information Systems and Telecommunication","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47798080","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}
Mohammad Pouya Salvati, S. Sulaimany, Jamshid Bagherzadeh Mohasefi
Link prediction seeks to detect missing links and the ones that may be established in the future given the network structure or node features. Numerous methods have been presented for improving the basic unsupervised neighbourhood-based methods of link prediction. A major issue confronted by all these methods, is that many of the available networks are sparse. This results in high volume of computation, longer processing times, more memory requirements, and more poor results. This research has presented a new, distinct method for link prediction based on community detection in large-scale sparse networks. Here, the communities over the network are first identified, and the link prediction operations are then performed within each obtained community using neighbourhood-based methods. Next, a new method for link prediction has been carried out between the clusters with a specified manner for maximal utilization of the network capacity. Utilized community detection algorithms are Best partition, Link community, Info map and Girvan-Newman, and the datasets used in experiments are Email, HEP, REL, Wikivote, Word and PPI. For evaluation of the proposed method, three measures have been used: precision, computation time and AUC. The results obtained over different datasets demonstrate that extra calculations have been prevented, and precision has been increased. In this method, runtime has also been reduced considerably. Moreover, in many cases Best partition community detection method has good results compared to other community detection algorithms.
{"title":"Overcoming the Link Prediction Limitation in Sparse Networks using\u0000 Community Detection","authors":"Mohammad Pouya Salvati, S. Sulaimany, Jamshid Bagherzadeh Mohasefi","doi":"10.52547/jist.9.35.183","DOIUrl":"https://doi.org/10.52547/jist.9.35.183","url":null,"abstract":"Link prediction seeks to detect missing links and the ones that may be established in the future given the network structure or node features. Numerous methods have been presented for improving the basic unsupervised neighbourhood-based methods of link prediction. A major issue confronted by all these methods, is that many of the available networks are sparse. This results in high volume of computation, longer processing times, more memory requirements, and more poor results. This research has presented a new, distinct method for link prediction based on community detection in large-scale sparse networks. Here, the communities over the network are first identified, and the link prediction operations are then performed within each obtained community using neighbourhood-based methods. Next, a new method for link prediction has been carried out between the clusters with a specified manner for maximal utilization of the network capacity. Utilized community detection algorithms are Best partition, Link community, Info map and Girvan-Newman, and the datasets used in experiments are Email, HEP, REL, Wikivote, Word and PPI. For evaluation of the proposed method, three measures have been used: precision, computation time and AUC. The results obtained over different datasets demonstrate that extra calculations have been prevented, and precision has been increased. In this method, runtime has also been reduced considerably. Moreover, in many cases Best partition community detection method has good results compared to other community detection algorithms.","PeriodicalId":37681,"journal":{"name":"Journal of Information Systems and Telecommunication","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70688882","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}
Nafeesa Bashir, Raeesa Bashir, J. Joorel, Tariqullah Jan
The paper proposes a new real life model and the main aim is to examine the cost benefit analysis of Textile Industry model subject to different failure and repair strategies. The reliability model comprises of three units i,e Spinning machine (S), Weaving machine (W), Colouring and Finishing machine(Cf). The working principal of the model starts with spinning machine (S) where in unit S is in operative state while as weaving machine, Colouring and Finishing machine are in ideal state. Complete failure of system is observed when all three units of system i.e. S,W and Cf are in down state. Repairperson is always available to carry out the repair activities in the system in which first priority in repair is given to Colouring and Finishing machine followed by Spinning and weaving machine. The proposed model attempts to maximize the reliability of a real life system. Reliability measures such as Mean Sojourn time, Mean time to system failure, Profit analysis of system are examined to define the performance of the reliability characteristics. For concluding the study of such model, different stochastic measures are analyzed in steady state using regenerative point technique. The tables are prepared for arbitrary values of the parameters to show the performance of some important reliability measures and to check the efficiency of the model under such situations.
{"title":"Cost Benefit Analysis of Three Non-Identical Machine Model with Priority\u0000 in Operation and Repair","authors":"Nafeesa Bashir, Raeesa Bashir, J. Joorel, Tariqullah Jan","doi":"10.52547/jist.9.35.151","DOIUrl":"https://doi.org/10.52547/jist.9.35.151","url":null,"abstract":"The paper proposes a new real life model and the main aim is to examine the cost benefit analysis of Textile Industry model subject to different failure and repair strategies. The reliability model comprises of three units i,e Spinning machine (S), Weaving machine (W), Colouring and Finishing machine(Cf). The working principal of the model starts with spinning machine (S) where in unit S is in operative state while as weaving machine, Colouring and Finishing machine are in ideal state. Complete failure of system is observed when all three units of system i.e. S,W and Cf are in down state. Repairperson is always available to carry out the repair activities in the system in which first priority in repair is given to Colouring and Finishing machine followed by Spinning and weaving machine. The proposed model attempts to maximize the reliability of a real life system. Reliability measures such as Mean Sojourn time, Mean time to system failure, Profit analysis of system are examined to define the performance of the reliability characteristics. For concluding the study of such model, different stochastic measures are analyzed in steady state using regenerative point technique. The tables are prepared for arbitrary values of the parameters to show the performance of some important reliability measures and to check the efficiency of the model under such situations.","PeriodicalId":37681,"journal":{"name":"Journal of Information Systems and Telecommunication","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70688418","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：