Cognitive Computation and Systems最新文献_第5页

Detection of emotional and behavioural changes after traumatic brain injury: A comprehensive survey 创伤性脑损伤后情绪和行为变化的检测：一项综合调查

Cognitive Computation and Systems Pub Date : 2023-03-07 DOI: 10.1049/ccs2.12075

Neha Vutakuri

{"title":"Detection of emotional and behavioural changes after traumatic brain injury: A comprehensive survey","authors":"Neha Vutakuri","doi":"10.1049/ccs2.12075","DOIUrl":"https://doi.org/10.1049/ccs2.12075","url":null,"abstract":"Traumatic brain injury (TBI) can affect normal brain function and may be caused by a vehicle accident, falling, and so on. The purpose of this survey is to provide clear knowledge of TBI, the causes of TBI, the impacts of TBI, and the role of family members and friends in recovery. TBI affects the daily life of the patients, both physically and mentally. After TBI, the patients may experience many emotional and behavioural changes because of a lack of certain brain functions. These changes affect their personal and social relationships. On the other hand, these changes depend on the severity of the TBI (i.e. mild, moderate, or severe), which is measured using the Glasgow coma score. Generally, three processes are used for emotion recognition: preprocessing, feature extraction, and emotion recognition. Preprocessing is performed for landmark detection and pose normalisation, which improves the performance of emotion detection. Feature extraction and emotion recognition are performed by various deep learning techniques, such as convolution neural networks and long short-term memory. These techniques recognise the behavioural and emotional changes (depression, anxiety, anger, personality changes etc.) of TBI patients using facial expressions. Family members and friends play an important role in TBI patients' recovery, the extent of which is based on the severity of the TBI. The care of family members and friends leads to quick recovery and rehabilitation of patients from TBI. Finally, testing is performed using Computed Tomography images, Magnetic Resonance Imaging images, Electroencephalography signals, and patient demographics, which together show that the deep learning methods achieve better performance in terms of accuracy, precision, recall, and F-measure in recognising emotional and behavioural changes after TBI. The authors conclude with a summary of the future of emotional and behavioural change prediction methods for TBI patients.","PeriodicalId":33652,"journal":{"name":"Cognitive Computation and Systems","volume":"5 1","pages":"42-63"},"PeriodicalIF":0.0,"publicationDate":"2023-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/ccs2.12075","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50137433","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

Detection of pedestrians and vehicles in autonomous driving with selective kernel networks 基于选择性核网络的自动驾驶中行人和车辆检测

Cognitive Computation and Systems Pub Date : 2023-03-03 DOI: 10.1049/ccs2.12078

Zhenlin Zhang, Gao Hanwen, Xingang Wu

{"title":"Detection of pedestrians and vehicles in autonomous driving with selective kernel networks","authors":"Zhenlin Zhang, Gao Hanwen, Xingang Wu","doi":"10.1049/ccs2.12078","DOIUrl":"https://doi.org/10.1049/ccs2.12078","url":null,"abstract":"Accurate detection of pedestrians and vehicles on the road is an important content in autonomous driving technology. In this article, a method to optimise the object detection network using the channel attention mechanism is proposed. In general, small object detection problems and difficult sample detection problems in object detection tasks can be solved by using feature pyramids. Different from building a feature pyramid, the authors did not make extensive changes to the network, but used the channel attention mechanism to dynamically adjust the output of a layer during the feature extraction process, allowing each neuron to adjust its receptive field size adaptively according to multiple scales of the input information, so that the network pays attention to the extraction of important features, especially the features of small objects and difficult samples. In order to evaluate the performance of the proposed method, experiments were conducted on standard benchmark data sets. It has been observed that the proposed method is superior to the original object detection network in terms of the detection accuracy of pedestrians and vehicles, especially the detection of small objects.","PeriodicalId":33652,"journal":{"name":"Cognitive Computation and Systems","volume":"5 1","pages":"64-70"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/ccs2.12078","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50119479","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

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