2022 12th International Conference on Computer and Knowledge Engineering (ICCKE)最新文献

Detection of Parkinso’s disease using Convolutional Neural Networks and Data Augmentation with SPECT images 利用卷积神经网络和SPECT图像数据增强技术检测帕金森病

2022 12th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2022-11-17 DOI: 10.1109/ICCKE57176.2022.9960085

Reyhaneh Dehghan, M. Naderan, Seyyed Enayatallah Alavi

{"title":"Detection of Parkinso’s disease using Convolutional Neural Networks and Data Augmentation with SPECT images","authors":"Reyhaneh Dehghan, M. Naderan, Seyyed Enayatallah Alavi","doi":"10.1109/ICCKE57176.2022.9960085","DOIUrl":"https://doi.org/10.1109/ICCKE57176.2022.9960085","url":null,"abstract":"Parkinson’s Disease or PD, is syndrome related to humans’ brains which mostly has impact on the neurons producing dopamine inside the substantia nigra area. Despite the fact that this disease has been known for many years, accurate detection of PD in its initial stages is still a challenge for physicians and researchers. In this study, a deep neural network based on CNN is used to diagnose the disease, which is able to differentiate between patients with PD from healthy individuals based on specific type of images, namely SPECT images. The proposed method consists of these phases: preprocessing, training and testing/evaluation. 650 SPECT images were investigated in this study, taken from the PPMI database. Since the number of images in the dataset may not be sufficient for the training phase, a data augmentation phase was also added to the whole process. The architecture of the CNN used and the augmentation step on SPECT images are the novelties of this study. Simulation results compared with other classification methods, show an accuracy of 97.01%, recall of 96.61%, specificity of 96.61%, and an f1-score of 96.61%. Results of adding data augmentation also show an accuracy of 95.50%, recall of 98.88%, specificity of 97.82%, and an f1-score of 98.32%, which are promising compared to previous work.","PeriodicalId":253277,"journal":{"name":"2022 12th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125832009","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}

引用次数: 0

ICCKE 2022 Cover Page ICCKE 2022封面页

2022 12th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2022-11-17 DOI: 10.1109/iccke57176.2022.9960132

引用次数: 0

Maximum diffusion of news in social media with the approach of reducing the search space 通过减少搜索空间的方法，在社交媒体上最大限度地传播新闻

2022 12th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2022-11-17 DOI: 10.1109/ICCKE57176.2022.9960033

M. Karian

引用次数: 0

LPCNet: Lane detection by lane points correction network in challenging environments based on deep learning LPCNet:基于深度学习的挑战性环境下车道点校正网络的车道检测

2022 12th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2022-11-17 DOI: 10.1109/ICCKE57176.2022.9960098

S. BaniasadAzad, M. Mosavi

{"title":"LPCNet: Lane detection by lane points correction network in challenging environments based on deep learning","authors":"S. BaniasadAzad, M. Mosavi","doi":"10.1109/ICCKE57176.2022.9960098","DOIUrl":"https://doi.org/10.1109/ICCKE57176.2022.9960098","url":null,"abstract":"Recently, lane detection methods with the help of deep learning have achieved significant accuracy in various conditions. But many do not perform well in computational complexity and are not applicable for real applications. In addition, their accuracy decreases in challenging situations. In this study, after identifying the critical points by the hourglass algorithm, we remove the redundant and invalid points using the Random Sample Consensus (RANSAC) algorithm. The Lane Point Correction Network (LPCNet) achieves acceptable accuracy in challenging conditions such as desert roads with poor texture and foggy conditions with poor light and clarity. Also, the computational complexity of the system is suitable, making it practical for real-time execution. The three factors of low computational complexity, high speed, and excellent results in unstructured and challenging environments make our algorithm applicable for real-time and practical applications. The number of network parameters is 4.5 million and significantly reduced compared to the valid methods. The execution speed in the tensor version reaches 32 frames per second, and the accuracy of the network in unlined environments is estimated at 49.80%.","PeriodicalId":253277,"journal":{"name":"2022 12th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124616772","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}

引用次数: 0

Deep Deterministic Policy Gradient in Acoustic to Articulatory Inversion 声学到发音反转的深度确定性策略梯度

2022 12th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2022-11-17 DOI: 10.1109/ICCKE57176.2022.9959976

Farzane Abdoli, H. Sheikhzadeh, V. Pourahmadi

引用次数: 0

Damage Detection After the Earthquake Using Sentinel-1 and 2 Images and Machine Learning Algorithms (Case Study: Sarpol-e Zahab Earthquake) 基于Sentinel-1和sentinel - 2图像和机器学习算法的震后损伤检测(以Sarpol-e Zahab地震为例)

2022 12th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2022-11-17 DOI: 10.1109/ICCKE57176.2022.9960127

Niloofar S. Alizadeh, B. Beirami, M. Mokhtarzade

引用次数: 0

Deep Learning Based Multi-Classifier System for Crack Segmentation in Civil Structure Images 基于深度学习的土木结构图像裂纹分割多分类器系统

2022 12th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2022-11-17 DOI: 10.1109/ICCKE57176.2022.9960114

M. Asadi, Seyedeh Sogand Hashemi, M. Sadeghi

引用次数: 0

An interactive user groups recommender system based on reinforcement learning 基于强化学习的交互式用户组推荐系统

2022 12th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2022-11-17 DOI: 10.1109/ICCKE57176.2022.9960042

Hediyeh Naderi Allaf, M. Kahani

{"title":"An interactive user groups recommender system based on reinforcement learning","authors":"Hediyeh Naderi Allaf, M. Kahani","doi":"10.1109/ICCKE57176.2022.9960042","DOIUrl":"https://doi.org/10.1109/ICCKE57176.2022.9960042","url":null,"abstract":"Nowadays, we have access to countless and diverse user data in various fields. Thus, it requires analysis to find a set of users. Several steps are taken to understand and identify users interactively to achieve such a goal. This article introduces a reinforcement learning model of interactive recommendations based on user groups. An agent learns the appropriate policy to discover users among groups based on feedback during a sequential decision-making process and recommends the best action for the next step. There are three datasets available for courses, jobs, and LinkedIn, but these three datasets are not related to each other, which causes errors in learning politics. Furthermore, taking different actions will significantly affect learning from these datasets. To improve learning, semantic similarity and text processing are used to extract relationships between datasets. As a result, a set of groups is constructed based on what the users have in common. Users are chosen from a set of groups represented by an agent. The results and experiments show that the agent can learn the policy without collecting previous sessions and finally provide an acceptable recommendation.","PeriodicalId":253277,"journal":{"name":"2022 12th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130125846","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}

引用次数: 0

Span-prediction of Unknown Values for Long-sequence Dialogue State Tracking 长序列对话状态跟踪中未知值的跨度预测

2022 12th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2022-11-17 DOI: 10.1109/ICCKE57176.2022.9960031

Marzieh Naghdi Dorabati, Reza Ramezani, M. Nematbakhsh

{"title":"Span-prediction of Unknown Values for Long-sequence Dialogue State Tracking","authors":"Marzieh Naghdi Dorabati, Reza Ramezani, M. Nematbakhsh","doi":"10.1109/ICCKE57176.2022.9960031","DOIUrl":"https://doi.org/10.1109/ICCKE57176.2022.9960031","url":null,"abstract":"Dialogue state tracking is one of the main components in task-oriented dialogue systems whose duty is to track the user’s goal during the conversation. Due to the diversity in natural languages and existing utterances, the user requests may include unknown values at different turns in these systems. However, predicting the actual values of the user requests is necessary for completing the intended task. In existing studies, these values are determined using span-based methods to predict a span in utterances or previous dialogues. However, the slots are not correctly filled when values are multi-word. In addition, in some scenarios, the slot values in a given turn may depend on previous dialogue states. However, due to the limitation of the input length of language models, it is impossible to access all the previous dialogue states. This study proposes a new approach that uses a span-tokenizer and adds the Bi-LSTM layer on top of the BERT model to predict the exact span of multi-word values. This approach uses parameters like user utterances, important dialogue histories, and all dialogue states as input to decrease the length of the sequences. The results show that this strategy has led to a 1.80% improvement in the joint-goal accuracy and 0.15% improvement in the slot accuracy metrics over the MultiWOZ 2.1 dataset compared to the SAVN model.","PeriodicalId":253277,"journal":{"name":"2022 12th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131156497","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}

引用次数: 0

An optimal workflow scheduling method in cloud-fog computing using three-objective Harris-Hawks algorithm 基于三目标Harris-Hawks算法的云雾计算工作流优化调度方法

2022 12th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2022-11-17 DOI: 10.1109/ICCKE57176.2022.9960123

Ahmadreza Montazerolghaem, Maryam Khosravi, Fatemeh Rezaee, M. Khayyambashi

{"title":"An optimal workflow scheduling method in cloud-fog computing using three-objective Harris-Hawks algorithm","authors":"Ahmadreza Montazerolghaem, Maryam Khosravi, Fatemeh Rezaee, M. Khayyambashi","doi":"10.1109/ICCKE57176.2022.9960123","DOIUrl":"https://doi.org/10.1109/ICCKE57176.2022.9960123","url":null,"abstract":"Today, the Internet of Things (IoT) use to collect data by sensors, and store and process them. As the IoT has limited processing and computing power, we are turning to integration of cloud and IoT. Cloud computing processes large data at high speed, but sending this large data requires a lot of bandwidth. Therefore, we use fog computing, which is close to IoT devices. In this case, the delay is reduced. Both cloud and fog computing are used to increasing performance of IoT. Job scheduling of IoT workflow requests based on cloud-fog computing plays a key role in responding to these requests. Job scheduling in order to reduce makespan time, is very important in realtime system. Also, one way to improve system performance is to reduce energy consumption. In this article, three-objective Harris Hawks Optimizer (HHO) scheduling algorithm is proposed in order to reduce makespan time, energy consumption and increase reliability. Also, dynamic voltage frequency scaling (DVFS) has been used to reduce energy consumption, which reduces frequency of the processor. Then HHO is compared with other algorithms such as Whale Optimization Algorithm (WOA), Firefly Algorithm (FA) and Particle Swarm Optimization (PSO) and proposed algorithm shows better performance on experimental data. The proposed method has achieved an average reliability of 83%, energy consumption of 14.95 KJ, and makespan of 272.5 seconds.","PeriodicalId":253277,"journal":{"name":"2022 12th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131857382","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}

引用次数: 1