Milad Abaspoor, S. Meshgini, T. Y. Rezaii, A. Farzamnia
{"title":"A Novel Method for Detecting Breast Cancer Location Based on Growing GA-FCM Approach","authors":"Milad Abaspoor, S. Meshgini, T. Y. Rezaii, A. Farzamnia","doi":"10.1109/ICCKE48569.2019.8964904","DOIUrl":"https://doi.org/10.1109/ICCKE48569.2019.8964904","url":null,"abstract":"The main idea of this article is to provide a numerical diagnostic method for breast cancer diagnosis of the MRI images. To achieve this goal, we used the region’s growth method to identify the target area. In the area’s growth method, based on the similarity or homogeneity of the adjacent pixels, the image is subdivided into distinct areas according to the criteria used for homogeneity analysis to determine their belonging to the corresponding region. In this paper, we used manual methods and use of FCM as the function of genetic algorithm fitness. The presented algorithm is performed for 212 healthy and 110 patients. Results show that GA-FCM method have better performance than hand method to select initial points. The sensitivity of presented method is 0.67. The results of the comparison of the fuzzy fitness function in the genetic algorithm with other technique show that the proposed model is better suited to the Jaccard index with the highest Jaccard values and the lowest Jaccard distance. Among the techniques, the presented works well because of the similarity of techniques and the lowest Jaccard distance. Values close to 0.9 are close to 0.8.","PeriodicalId":6685,"journal":{"name":"2019 9th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"30 1","pages":"238-242"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81313424","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}
{"title":"Scheduling Mixed-criticality Systems on Reconfigurable Platforms","authors":"Sadegh Sehhatbakhsh, Yasser Sedaghat","doi":"10.1109/ICCKE48569.2019.8964800","DOIUrl":"https://doi.org/10.1109/ICCKE48569.2019.8964800","url":null,"abstract":"The scheduling for mixed criticality systems, where multiple functionalities with different criticality levels are integrated into a shared hardware platform, is an important research area. Reconfigurable platforms, which combine the advantages of software flexibility and performance efficiencies, are recognized as a suitable processing platform for real-time embedded systems. In this paper, we consider the scheduling of mixed criticality systems with two criticality levels on reconfigurable platforms. Partitioned fixed-priority preemptive scheduling is used to schedule tasks. Since the context switch overhead in reconfigurable platforms is not as small as that of multiprocessors, it has been taken into account in our schedulability analysis. Furthermore, a context-switch-aware partitioning algorithm is presented to improve the schedulability of tasks in platforms that context switch cost cannot be neglected. The experiments results show that our proposed partitioning algorithm gives higher schedulability ratios when compared to the classical partitioning algorithms.","PeriodicalId":6685,"journal":{"name":"2019 9th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"31 1","pages":"431-436"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90176664","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}
{"title":"An LSTM Auto-Encoder for Single-Channel Speaker Attention System","authors":"Mahnaz Rahmani, F. Razzazi","doi":"10.1109/ICCKE48569.2019.8965084","DOIUrl":"https://doi.org/10.1109/ICCKE48569.2019.8965084","url":null,"abstract":"In this paper, we utilized a set of long short term memory (LSTM) deep neural networks to distinguish a particular speaker from the rest of the speakers in a single channel recorded speech. The structure of this network is modified to provide the suitable result. The proposed architecture models the sequence of spectral data in each frame as the key feature. Each network has two memory cells and accepts an 8 band spectral window as the input. The results of the reconstructions of different bands are merged to rebuild the speaker’s utterance. We evaluated the intended speaker's reconstruction performance of the proposed system with PESQ and MSE measures. Using all utterances of each speaker in TIMIT dataset as the training data to build an LSTM based attention auto-encoder model, we achieved 3.66 in PESQ measure to rebuild the intended speaker. In contrast, the PESQ was 1.92 in average for other speakers when we used the mentioned speaker’s network. This test was successfully repeated for different utterances of different speakers.","PeriodicalId":6685,"journal":{"name":"2019 9th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"1 1","pages":"110-115"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90197320","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}
{"title":"Efficiency Improvement of Differential Evolution Algorithm Using a Novel Mutation Method","authors":"Milad Ghahramani, Abolfazl Laakdashti","doi":"10.1109/ICCKE48569.2019.8964840","DOIUrl":"https://doi.org/10.1109/ICCKE48569.2019.8964840","url":null,"abstract":"The differential evolution algorithm is one of the fast, efficient, and strong population-based algorithms, which has extended applications in solving various problems. Although the velocity, power, and efficiency of this algorithm have been demonstrated in solving many optimization problems, this algorithm, like other metaheuristic algorithms, is not guaranteed to achieve the global optimal points of the optimization problems and may be ceased at optimal local points. One of the reasons for stopping the algorithm at the local optimum points is the imbalance between the exploration and exploitation abilities of the algorithm. One of the operators of the differential evolution algorithm, which plays an essential role in establishing the proper balance between the exploitation and exploitation of the algorithm, is the mutation operator. In this paper, a new mutation method is proposed to improve the efficiency of the differential evolution algorithm to make an appropriate balance between the exploitation and exploitation abilities of the algorithm. Comparing the results of the proposed mutation method with other mutation methods indicates that the proposed method has better speed and accuracy convergence rather than other methods, and it can be employed to solve large-scale optimization problems.","PeriodicalId":6685,"journal":{"name":"2019 9th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"97 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83596751","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 Ali Labbaf Khaniki, Amir Hossein Asnavandi, M. Manthouri
{"title":"Boost PFC Converter Control using Fractional Order Fuzzy PI Controller Optimized via ICA","authors":"Mohammad Ali Labbaf Khaniki, Amir Hossein Asnavandi, M. Manthouri","doi":"10.1109/ICCKE48569.2019.8964893","DOIUrl":"https://doi.org/10.1109/ICCKE48569.2019.8964893","url":null,"abstract":"One of the most important types of DC-DC converters is Boost converters. They increase the voltage level, stabilizing and reducing the voltage ripples at output. The nature of this system is nonlinear and uncertainty is unavoidable in modeling it. This study presented a fractional order fuzzy PI (FOFPI) controller to control the system. The Imperialist Competitive Algorithm (ICA) Optimization is used to optimize the parameters of proposed controllers. The fractional order of integral is achieved by ICA. The results are compared with fuzzy PI (FPI) controller. They show the FOFPI has less fluctuations, overshoot and settling time compared to FPI. Additionally, the value of Power Factor Correction (PFC) is closer to one. In fact, FOFPI has more flexibility and good performance in dealing with uncertainty in comparison with FPI. The results reveal the performance of the proposed method against other methods.","PeriodicalId":6685,"journal":{"name":"2019 9th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"18 1","pages":"131-136"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76379101","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}
{"title":"Persian Sentiment Lexicon Expansion Using Unsupervised Learning Methods","authors":"Reza Akhoundzade, Kourosh Hashemi Devin","doi":"10.1109/ICCKE48569.2019.8964692","DOIUrl":"https://doi.org/10.1109/ICCKE48569.2019.8964692","url":null,"abstract":"Sentiment analysis, is a subfield of natural language processing that aims at opinion mining to analyze thoughts, orientation and, evaluation of users within some texts. The solution to this problem includes two main steps: extracting aspects and determining users’ positive or negative sentiments with respect to the aspects. Two main challenges of sentiment analysis in the Persian language are lack of comprehensive tagged data sets and use of colloquial language in texts. In this paper we propose, a system to specify and extract sentiment words using unsupervised methods in the Persian language that also support colloquial words. Additionally, we also proposed and implemented a state-of-art technique to expand Persian sentiment lexicon. Our proposed method utilized neural network (Word2Vec model) with the help of rule-based methods. F1 measure for sentiment words extraction in our proposed method is 0.58.","PeriodicalId":6685,"journal":{"name":"2019 9th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"38 1","pages":"461-465"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91324918","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}
{"title":"An Eligibility Traces based Cooperative and Integrated Control Strategy for Traffic Flow Control in Freeways","authors":"Seyed Soroosh Tabadkani Aval, Negar Shojaee Ghandeshtani, Parisa Akbari, N. Eghbal, Amin Noori","doi":"10.1109/ICCKE48569.2019.8965184","DOIUrl":"https://doi.org/10.1109/ICCKE48569.2019.8965184","url":null,"abstract":"Traffic congestion and gridlocks are considered as main problems of designing an urban motorway network. For this purpose, traffic flow control strategies are presented through recent decades to address this problem. In this paper, an Eligibility Traces based Reinforcement Learning (ETRL) traffic flow control strategy was proposed. This strategy is based on cooperative and integrated control of Ramp Metering (RM) and Variable Speed Limits (VSL). To test the proposed method, first the traffic macroscopic model was calibrated via Genetic Algorithm (GA) optimization to simulate traffic behavior and further, the traffic control strategy is applied to M62 highway stretch in England which is one of the smartest highways, and the results are presented.","PeriodicalId":6685,"journal":{"name":"2019 9th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"28 1","pages":"40-45"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77572967","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}
{"title":"FTHR: Fault Tolerant Hypercube-based Routing for NoCs","authors":"R. Kourdy, Amir Rajabzadeh","doi":"10.1109/ICCKE48569.2019.8965117","DOIUrl":"https://doi.org/10.1109/ICCKE48569.2019.8965117","url":null,"abstract":"Network-on-chips are a novel communications infrastructure for decoupling the communication elements from processing cores, with the goal of eliminating the challenges of many cores systems. One of the most important NoCs challenges is fault tolerance. This article tries to resolve the challenge into separate ways i.e., topology and routing. The proposed topology is called fault tolerant Hypercube-base NoC (HNoC) and the proposed routing algorithm is called Fault Tolerant Hypercube-based Routing (FTHR). The FTHR was simulated in a HNoC topology in NS-2 standard simulator. The FTHR was evaluated using 3D to 10D NoCs with 8 to 2014 cores in normal and faulty conditions. The results of the experiments show that the HNoC packet loss by FTHR routing varies between 75.0% and 98.16% depending on the different NoC dimensions. This high degree of fault tolerance is because of router degree and the diversity of paths in HNoC and also applied innovations in the FTHR routing. The results also show that the FTHR routing has reasonable outcome and is capable of tolerance about 100% of router and link faults in permanent and transient timing.","PeriodicalId":6685,"journal":{"name":"2019 9th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"283 1","pages":"331-338"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76841033","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}
{"title":"Eliminating the Repetitive Motions as a Preprocessing step for Fast Human Action Retrieval","authors":"Mohsen Ramezani, F. Yaghmaee","doi":"10.1109/ICCKE48569.2019.8965087","DOIUrl":"https://doi.org/10.1109/ICCKE48569.2019.8965087","url":null,"abstract":"Today, video searching methods dropped behind the growth of using capturing devices. Action retrieval is a new research field which seeks to use the captured human action for searching the videos. As most human actions consist of similar motions which are repeated over time, we seek to propose a method for eliminating the repetitive motions before retrieving the videos. This method, as a preprocessing step, can decrease the volume of the retrieval computations for each video. Here, a function is used to calculate a value per each pixel as its movement energy. Then, CWT (Continuous Wavelet Transform) is used for mapping the response function of the points into the frequency space to find similar motion patterns more easier. The DTW (Dynamic Time Wrapping) is then applied on the new space to find similar frequency patterns (episodes) over time. Finally, one of the similar episodes, i.e. some sequential frames, remains for the retrieval computations and others are eliminated. The proposed method is evaluated on KTH, UCFYT, and HMDB datasets and results indicate the proper performance of the proposed method. Eliminating the repetitive motions results into significant reduction in retrieval computations and time.","PeriodicalId":6685,"journal":{"name":"2019 9th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"104 1","pages":"26-31"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80550242","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}
{"title":"[Copyright notice]","authors":"","doi":"10.1109/iccke48569.2019.8964673","DOIUrl":"https://doi.org/10.1109/iccke48569.2019.8964673","url":null,"abstract":"","PeriodicalId":6685,"journal":{"name":"2019 9th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"40 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78994498","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}