Cognitive Computation and Systems最新文献_第4页

Review on human-like robot manipulation using dexterous hands 灵巧手类人机器人操作综述

Cognitive Computation and Systems Pub Date : 2023-02-18 DOI: 10.1049/ccs2.12073

Suhas Kadalagere Sampath, Ning Wang, Hao Wu, Chenguang Yang

引用次数: 2

Adaptive classification helps hybrid visual brain computer interface systems handle non-stationary cortical signals 自适应分类有助于混合视觉脑机接口系统处理非平稳皮层信号

Cognitive Computation and Systems Pub Date : 2023-02-13 DOI: 10.1049/ccs2.12077

Deepak D. Kapgate, Krishna Prasad. K

引用次数: 0

Review and analysis of deep neural network models for Alzheimer's disease classification using brain medical resonance imaging 脑医学共振成像用于阿尔茨海默病分类的深度神经网络模型综述与分析

Cognitive Computation and Systems Pub Date : 2023-02-10 DOI: 10.1049/ccs2.12072

Shruti Pallawi, Dushyant Kumar Singh

{"title":"Review and analysis of deep neural network models for Alzheimer's disease classification using brain medical resonance imaging","authors":"Shruti Pallawi, Dushyant Kumar Singh","doi":"10.1049/ccs2.12072","DOIUrl":"https://doi.org/10.1049/ccs2.12072","url":null,"abstract":"Alzheimer's disease is a type of progressive neurological disorder which is irreversible and the patient suffers from severe memory loss. This disease is the seventh largest cause of death across the globe. As yet there is no cure for this disease, the only way to control it is its early diagnosis. Deep Learning techniques are mostly preferred in classification tasks because of their high accuracy over a large dataset. The main focus of this paper is on fine-tuning and evaluating the Deep Convolutional Networks for Alzheimer's disease classification. An empirical analysis of various deep learning-based neural network models has been done. The architectures evaluation includes InceptionV3, ResNet with 50 layers and 101 layers and DenseNet with 169 layers. The dataset has been taken from Kaggle which is publicly available and comprises of four classes which represents the various stages of Alzheimer's disease. In our experiment, the accuracy of DenseNet consistently improved with the increase in the number of epochs resulting in a 99.94% testing accuracy score better than the rest of the architectures. Although the results obtained are satisfactory, but for future research, we can apply transfer learning on other deep models like Inception V4, AlexNet etc., to increase accuracy and decrease computational time. Also, in future we can work on other datasets like ADNI or OASIS and use Positron emitted tomography, diffusion tensor imaging neuroimages and their combinations for better result.","PeriodicalId":33652,"journal":{"name":"Cognitive Computation and Systems","volume":"5 1","pages":"1-13"},"PeriodicalIF":0.0,"publicationDate":"2023-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/ccs2.12072","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50127206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Speech emotion recognition with artificial intelligence for contact tracing in the COVID-19 pandemic 新冠肺炎大流行中用于接触者追踪的人工智能语音情感识别

Cognitive Computation and Systems Pub Date : 2023-02-08 DOI: 10.1049/ccs2.12076

Francesco Pucci, Pasquale Fedele, Giovanna Maria Dimitri

{"title":"Speech emotion recognition with artificial intelligence for contact tracing in the COVID-19 pandemic","authors":"Francesco Pucci, Pasquale Fedele, Giovanna Maria Dimitri","doi":"10.1049/ccs2.12076","DOIUrl":"https://doi.org/10.1049/ccs2.12076","url":null,"abstract":"If understanding sentiments is already a difficult task in human-human communication, this becomes extremely challenging when a human-computer interaction happens, as for instance in chatbot conversations. In this work, a machine learning neural network-based Speech Emotion Recognition system is presented to perform emotion detection in a chatbot virtual assistant whose task was to perform contact tracing during the COVID-19 pandemic. The system was tested on a novel dataset of audio samples, provided by the company Blu Pantheon, which developed virtual agents capable of autonomously performing contacts tracing for individuals positive to COVID-19. The dataset provided was unlabelled for the emotions associated to the conversations. Therefore, the work was structured using a sort of transfer learning strategy. First, the model is trained using the labelled and publicly available Italian-language dataset EMOVO Corpus. The accuracy achieved in testing phase reached 92%. To the best of their knowledge, thiswork represents the first example in the context of chatbot speech emotion recognition for contact tracing, shedding lights towards the importance of the use of such techniques in virtual assistants and chatbot conversational contexts for psychological human status assessment. The code of this work was publicly released at: https://github.com/fp1acm8/SER.","PeriodicalId":33652,"journal":{"name":"Cognitive Computation and Systems","volume":"5 1","pages":"71-85"},"PeriodicalIF":0.0,"publicationDate":"2023-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/ccs2.12076","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50139375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An efficient routing protocol for coherent energy using mayfly optimization algorithm in heterogeneous wireless sensor networks 异构无线传感器网络中基于mayfly优化算法的高效相干能量路由协议

Cognitive Computation and Systems Pub Date : 2023-01-01 DOI: 10.1049/ccs2.12074

Pathrose Jasmine Lizy, Natarasan Chenthalir Indra

引用次数: 0

Cognitive Computation and Systems: First International Conference, ICCCS 2022, Beijing, China, December 17–18, 2022, Revised Selected Papers 认知计算与系统:第一届国际会议，ICCCS 2022，中国北京，2022年12月17-18日，修订论文选集

Cognitive Computation and Systems Pub Date : 2023-01-01 DOI: 10.1007/978-981-99-2789-0

引用次数: 0

CORRIGENDUM: [Guest editorial: Music perception and cognition in music technology] 勘误:[客座社论:音乐技术中的音乐感知和认知]

Cognitive Computation and Systems Pub Date : 2022-09-01 DOI: 10.1049/ccs2.12071

引用次数: 0

Medical image encryption algorithm based on hyper-chaotic system and DNA coding 基于超混沌系统和DNA编码的医学图像加密算法

Cognitive Computation and Systems Pub Date : 2022-08-24 DOI: 10.1049/ccs2.12070

Mingzhen Li, Shuaihao Pan, Weiming Meng, Wang Guoyong, Zhihang Ji, Lin Wang

{"title":"Medical image encryption algorithm based on hyper-chaotic system and DNA coding","authors":"Mingzhen Li, Shuaihao Pan, Weiming Meng, Wang Guoyong, Zhihang Ji, Lin Wang","doi":"10.1049/ccs2.12070","DOIUrl":"10.1049/ccs2.12070","url":null,"abstract":"With the international development of the medical service informatisation, medical information sharing has become the key standard to measure the degree of medical informatisation. In this process, it is important to ensure the security of patients' medical information such as patients' records, examination reports, images and so on. In this article, an image encryption scheme based on Secure Hash Algorithm 3 (SHA-3), DNA coding and high dimensional chaos system is proposed to promote the security level of medical images in information sharing processes such as over the network. First, SHA-3 algorithm is used to calculate the hash value of the input image, and the result is taken as the initial value of the hyper-chaotic system. Second, the intensity value of the input image is converted into a serial binary digital stream. Third, the pseudo-random sequence generated by a 4-dimensional hyper-chaotic system is used to perturb the bit stream globally so as to achieve the purpose of hiding the effective information of the input image. During the operation of hyper-chaotic sequence and DNA sequence, algebraic and complementary operations are performed on DNA encoding values to enhance encryption performance. Finally, simulations have been applied on an open-source medical image database, and the results demonstrate obvious encryption effectiveness and high security level of the proposed encryption algorithm in the robustness of noise and clipping attacks.","PeriodicalId":33652,"journal":{"name":"Cognitive Computation and Systems","volume":"4 4","pages":"378-390"},"PeriodicalIF":0.0,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/ccs2.12070","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127253446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Day-ahead optimal dispatching of hybrid power system based on deep reinforcement learning 基于深度强化学习的混合电力系统日前最优调度

Cognitive Computation and Systems Pub Date : 2022-08-22 DOI: 10.1049/ccs2.12068

Yakun Shi, Chaoxu Mu, Yi Hao, Shiqian Ma, Na Xu, Zhiqiang Chong

{"title":"Day-ahead optimal dispatching of hybrid power system based on deep reinforcement learning","authors":"Yakun Shi, Chaoxu Mu, Yi Hao, Shiqian Ma, Na Xu, Zhiqiang Chong","doi":"10.1049/ccs2.12068","DOIUrl":"10.1049/ccs2.12068","url":null,"abstract":"The problem of optimal dispatching of a power system containing a high proportion of renewable energy is of great significance for the realisation of new energy consumption and the economic and reliable operation of the power system. For the solution of non-linear, non-convex, multi-objective problems for the optimal operation design of a power system with wind and photovoltaic access, traditional methods have difficulties in terms of computational real-time and iterative convergence. To address this issue, a deep reinforcement learning-based optimal scheduling method for the hybrid power system is proposed, which enables continuous action control to obtain an optimal scheduling strategy through the interaction between the agent and the hybrid power system. Firstly, a mathematical description of the optimal scheduling problem containing wind power and photovoltaic power system is presented, and the state space, action space, and reward function of the agent are designed. Secondly, the basic framework of the deep reinforcement learning optimal scheduling model is constructed, and the basic principles of the twin delayed deep deterministic policy gradient algorithm are introduced. Finally, the effectiveness of the deep reinforcement learning model for day-ahead optimal scheduling of the hybrid power system is verified by means of an arithmetic analysis of the modified New England 39-bus system.","PeriodicalId":33652,"journal":{"name":"Cognitive Computation and Systems","volume":"4 4","pages":"351-361"},"PeriodicalIF":0.0,"publicationDate":"2022-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/ccs2.12068","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122876871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Cognitive excursion analysis of uncertainty concepts based on cloud model 基于云模型的不确定性概念认知偏移分析

Cognitive Computation and Systems Pub Date : 2022-08-22 DOI: 10.1049/ccs2.12069

Xu C. Lin

{"title":"Cognitive excursion analysis of uncertainty concepts based on cloud model","authors":"Xu C. Lin","doi":"10.1049/ccs2.12069","DOIUrl":"10.1049/ccs2.12069","url":null,"abstract":"Based on the characteristics of human cognition and the cloud model, the excursion of uncertain concepts by the similarity between uncertain concepts from the perspective of conceptual cognition is studied. Firstly, considering the different meanings of the numerical characters in cloud concept, the properties of cloud concepts similarity are given. Furthermore, in order to reflect the various similarities that may exist in uncertain concept, five similarities between cloud concepts are constructed, specifically include the similarity between concept expectations, the similarity between concept entropy, the similarity between concept hyper entropy, the shape similarity and the overall similarity. Secondly, the rationality of these proposed similarity measurements is illustrated by comparing with the existing methods through the specific data analysis. Finally, two cognitive experiments, including concept cognitive processes without prior knowledge and concept cognitive processes with prior knowledge (including positive prior knowledge and negative prior knowledge), are designed. These experiments are all used to study the excursion in the process of concept cognition by the similarity of cloud concepts. The experiment results show that the proposed similarities are reasonable by comparing the proposed method with the existing ones, and the effectiveness of the proposed method is also verified by the excursion of concept cognition.","PeriodicalId":33652,"journal":{"name":"Cognitive Computation and Systems","volume":"4 4","pages":"362-377"},"PeriodicalIF":0.0,"publicationDate":"2022-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/ccs2.12069","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121617196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0