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检索条件"任意字段=2023 International Conference on Cognitive Computing and Complex Data, ICCD 2023"

共 718 条记录，以下是551-560 订阅

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cognitive Disability Prediction & Analysis using Machine Learning Application

Cognitive Disability Prediction & Analysis using Machine Lea...

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Advance computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： R. Kishore Kanna V. Subha Ramya Asraa Ahmed Khafel Kadim A. Jabbar Mustafa Al-Tahee Raed Khalid Department of Biomedical Engineering Jerusalem College of Engineering Chennai India Chennai India Collage of Engineering National University of Science and Technology Dhi Qar Iraq Medical Laboratory Techniques Department Al-Farahidi University Baghdad Iraq

A person with mild cognitive disability (MCD), a kind of memory loss that affects both memory and thinking skills, may be at an increased risk of acquiring dementia brought on by Alzheimer's disease or other neurological diseases. MCD affects between 13 and 19% of those who are 60 years of age or older. People who suffer from cognitive abnormalities should seek therapy and diagnosis as soon as they can. The major effect of MCD on the target is its effect on memory. Accurate MCD diagnosis is quite challenging with the current approaches. A hybrid approach is put forward in this study to identify MCD at an early stage. EEG data from MCD individuals and healthy controls was collected for this purpose. With the use of machine learning models including Support Vector Machines (SVM), Decision Trees (DT), k-Nearest Neighbour (KNN), and the hybrid approach ACO KNN, Renyi entropy (RE) and Discrete Wavelet Transform (DWT) characteristics were retrieved (combined Ant Colony Optimisation with k-Nearest Neighbour). The performance of the system is assessed based on an accuracy comparison with machine learning models. When compared to other models, RE and ACO KNN had an accuracy of 85.0%.

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Predicting Suicidal Behavior Among Indian Adults Using Childhood Trauma, Mental Health Questionnaires and Machine Learning Cascade Ensembles 4th

Predicting Suicidal Behavior Among Indian Adults Using Child...

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4th international conference on Frontiers in computing and Systems, COMSYS 2023

作者： Rao, Akash K. Trivedi, Gunjan Y. Trivedi, Riri G. Bajpai, Anshika Chauhan, Gajraj Singh Menon, Vishnu K. Soundappan, Kathirvel Ramani, Hemalatha Pandya, Neha Dutt, Varun Applied Cognitive Science Laboratory Indian Institute of Technology Mandi Himachal Pradesh Kamand India Society for Energy and Emotions Wellness Space LLP Ahmedabad India Department of Community Medicine and School of Public Health Post Graduate Institute of Medical Education and Research Chandigarh India

ISBN: (纸本)9789819726103

Among young adults, suicide is India's leading cause of death, accounting for an alarming national suicide rate of around 16%. In recent years, machine learning algorithms have emerged to predict suicidal behavior using various behavioral traits. But to date, the efficacy of machine learning algorithms in predicting suicidal behavior in the Indian context has not been explored in literature. In this study, different machine learning algorithms and ensembles were developed to predict suicide behavior based on childhood trauma, different mental health parameters, and other behavioral factors. The dataset was acquired from 391 individuals from a wellness center in India. Information regarding their childhood trauma, psychological wellness, and other mental health issues was acquired through standardized questionnaires. Results revealed that cascade ensemble learning methods using support vector machine, decision trees, and random forest were able to classify suicidal behavior with an accuracy of 95.04% using data from childhood trauma and mental health questionnaires. The study highlights the potential of using these machine learning ensembles to identify individuals with suicidal tendencies so that targeted interventions could be provided efficiently. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Decision trees

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EEG Channel Selection Based on Neuron Proportion with SNN for Motor Imagery Classification

EEG Channel Selection Based on Neuron Proportion with SNN fo...

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Neuromorphic computing (ICNC), international conference on

作者： Zhihui Sun Chaoqiong Fan Tianyuan Jia Qing Li Xia Wu School of Artificial Intelligence Beijing Normal University Beijing China State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal University Beijing China

The brain computer interface (BCI) technology based on motor imagery has great potential for various control and communication tasks. However, the presence of a large number of EEG channels leads to redundant information, which affects processing speed and classification accuracy. Spiking neural networks (SNN) have the potential to process EEG data by transmitting pulsing activity between synapses and neurons situated in space. Neucube is an SNN architecture inspired by the human brain structure that allows for end-to-end learning, classification, and understanding of spatiotemporal data at low power consumption, saving computing power and reducing operational complexity. By utilizing this model, the temporal and spatial information of EEG signals can be considered to explore the importance and correlation of spatial neurons corresponding to EEG channels during the classification process. Thus, this study aimed to use the Neucube model based on SNN to select the most influential EEG signal channels in the classification process. This improvement mainly focuses on improving classification accuracy and reducing energy consumption to enhance the practical application performance of BCI systems. The proposed method was tested on the BCI Competition IV dataset 2A. After deleting several unimportant EEG channels, the classification accuracy was improved, and the energy consumption was reduced.

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Embodied Epistemology: A Meta-cognitive Exploration of Chatbot-Enabled Document Analysis

Embodied Epistemology: A Meta-Cognitive Exploration of Chatb...

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Evolutionary Algorithms and Soft computing Techniques (EASCT), international conference on

作者： Atman Ainapure Shreyash Dhamane Sudhir Dhage Computer Engineering Sardar Patel Institute of Technology Mumbai India

In this paper, novel research is presented that constructs a PDF chatbot using ChatGPT 3.5 Turbo and the LLM Model. The framework, chatGPT 3.5, streamlines chatbot creation, enabling scalable AI/LLM applications. The potent LLM Model facilitates text generation, language translation, and original content creation, along with insightful responses to user queries. The approach combines chatGPT 3.5 Turbo and the LLM Model to develop a chatbot proficient in addressing PDF-related inquiries. Utilizing data from uploaded PDFs and the LLM Model, the chatbot generates informative text responses to customer questions. The research relies on the LangChain framework, while a Streamlit based homepage enhances user interactions. This work exemplifies the LangChain and LLM Model’s potential to craft engaging chatbots, adaptable across domains like customer service, education, and research. The chat bot, a valuable resource for addressing product queries, offering educational aid, and providing tutoring, effectively responds to user inquiries about PDF files.

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Design of an Intelligent Voice-Controlled Home System Based on Raspberry Pi

Design of an Intelligent Voice-Controlled Home System Based ...

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IEEE international conference on High Performance computing and Communications (HPCC)

作者： Kaiyi Wu Jiaying Li Jiabin Yu Yuhao Hua Binggang Xiao Wu Wang Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province College of Information Engineering China Jiliang University Hangzhou China School of Computer Science and Technology Xidian University Xi'an China Multisensory Integration Laboratory School of Psychological and Cognitive Sciences Peking University Beijing China

A complete intelligent voice-controlled home system should include voice capture, voice recognition, authentication, voice result processing, and other processes. Based on the intelligent voice interaction model of Raspberry Pi 3b+ and Alibaba Cloud platform, this paper realized a high recognition accurate, stable, and efficient intelligent voice-controlled home system. We used the Raspberry Pi to control the hardware interface, collected voice data through an external microphone, and performed command control with external sensors and controllable devices in the hardware part; The software was based on Alibaba Cloud intelligent voice interaction platform to realize voice data recognition. The system constructed can achieve the expected function after repeatedly testing the controlled device. The test results show that the recognition accuracy and recognition efficiency of the system's voice control instructions are both high in a relatively quiet environment.

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TagVibra: push the limit of RFID-based vibration sensing

TagVibra: push the limit of RFID-based vibration sensing

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international conference on Big data computing and Communications (BIGCOM)

作者： Jian Li Ping Li Biaokai Zhu Chong Dong Chao Gong Zhao Lv Anhui Province Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei China Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet Anhui University of Technology Ma’anshan China Shanxi Police College Taiyuan China

RFID-based mechanical vibration detection is considered a promising method for many Internet of Things (IoT) applications. However, existing methods are affected by ambient interference and leakage from the reader signature, which makes it difficult to obtain high-precision detection results. To overcome this limitation, this paper introduces TagVibra, a high-precision non-contact vibration sensing system based on RFID technology. By leveraging the nonlinear effect inside the tag and the modulation mechanism of RFID, TagVibra can achieve sub-millimeter level vibration detection accuracy. We implement TagVibra on our Universal Software Radio Peripheral (USRP) platform and evaluate its performance via experiments. The experimental results show that TagVibra can detect vibration with amplitude as low as 0.3mm, and the average estimation error below 0.7Hz when the vibration frequency is 100Hz.

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Graph Contrastive Learning with Intrinsic Augmentations 1

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17th international conference on Bio-Inspired computing: Theories and Applications, BIC-TA 2022

作者： Sun, Dengdi Cao, Mingxin Ding, Zhuanlian Luo, Bin Ministry of Education School of Artificial Intelligence Anhui University Hefei230601 China Anhui Provincial Key Laboratory of Multimodal Cognitive Computing School of Computer Science and Technology Anhui University Hefei230601 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230026 China School of Internet Anhui University Hefei230039 China

ISBN: (数字)9789819915491

ISBN: (纸本)9789819915484

Graph contrastive learning has become an important approach for learning unsupervised representations of graphs, with the key idea of maximizing the consistency of representations in both augmented views through data augmentation. Existing graph contrastive learning models concentrate on topology enhancement of the graph structure by simply removing/adding edges between nodes randomly, which may not only destroy the important structure of the graph but also generate meaningless graphs, leading to a significant degradation of the contrastive learning performance. In addition, current research is too focused on minimizing losses, while ignoring the intrinsic factors that can affect the quality of node representations, which is detrimental to the training of models and the generation of high-quality node representations. To address these issues, we propose intrinsic augmented graph contrastive learning, named IAG, which consists of two components: 1) In the topology augmentation part, we propose a novel topology augmentation strategy based on potential connections in the feature space, which complements the traditional topology augmentation by allowing different graphs to obtain augmentation strategies more suitable for their own characteristics to advance the traditional graph contrastive learning. 2) We explored the effect of temperature coefficient in the loss function on the quality of the final representation and proposed dynamic temperature with penalty terms, which helps to generate high-quality node representations. Finally, we conducted extensive node classification experiments on 8 real-world datasets. The experimental results show that our proposed method is highly competitive with the existing state-of-the-art baselines and even surpasses some supervised methods. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Graph neural networks

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cognitive Fire Alert Framework for Realizing Fire in Unfriendly Environment using Wireless Sensor Network

Cognitive Fire Alert Framework for Realizing Fire in Unfrien...

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international conference on computing Methodologies and Communication (ICCMC)

作者： A. Sathiya A. K. Velmurugan P. Santhuja G. Indra Department of Artificial Intelligence and Data Science Sri Sairam Institute of Technology Chennai Tamil Nadu India Department of Computer Science And Engineering Koneru Lakshmaiah Education Foundation Vijayawada Andhra Pradesh India Department of Computer Science Engineering B V Raju Institute of Technology Hyderabad Telangana India Department of Computer Science Engineering R.M.K College of Engineering and Technology Chennai Tamil Nadu India

Nowadays, Wireless Sensor Networks (WSNs) are extensively distributed in environmental monitoring, operational health monitoring, and industrial monitoring. It is an inexpensive application. It contains small, less energy-consuming, lower-cost devices combined with restricted computation, observation, and radio transmission capacities. People nowadays use fire alarms with sensors to detect fires. Fire sensors are unreliable. With the fast growth of WSNs, people can now remotely watch their surroundings to ascertain the current status. A standard fire alarm system is only able to detect one of the environmental parameters at a time. It is possible that there is a fire if the detection value is greater than the threshold; this would indicate that there is a fire. Copper, insulated, or cables are used to make the connections between detectors and alarms in a traditional fire alarm system. Due to the presence of a large number of elements at the site of the fire, the smoke alarm system will be unable to make an accurate determination as to the location of the flames. This article proposes a cognitive fire alert framework (CFAF) for realizing fire in unfriendly environments using WSN. It has a phase for wireless transmission, a phase for cognitive fire recognition, and a phase for the Random Forest Method (RFM). This approach utilizes the RFM to classify the fire by data classification. The results of the experiments show that the suggested technique minimizes the number of false alarms by evaluating the delay value by utilizing data samples and comparing the temperature, humidity, pressure, and dust level.

关键词： Temperature sensors Temperature measurement Wireless communication Wireless sensor networks Temperature distribution Humidity Forestry

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DOMINO++: Domain-Aware Loss Regularization for Deep Learning Generalizability 26th

DOMINO++: Domain-Aware Loss Regularization for Deep Learning...

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26th international conference on Medical Image computing and Computer-Assisted Intervention, MICCAI 2023

作者： Stolte, Skylar E. Volle, Kyle Indahlastari, Aprinda Albizu, Alejandro Woods, Adam J. Brink, Kevin Hale, Matthew Fang, Ruogu Gainesville United States Torch Technologies LLC ShalimarFL United States Center for Cognitive Aging and Memory McKnight Brain Institute UF Gainesville United States Department of Clinical and Health Psychology College of Public Health and Health Professions UF Gainesville United States Department of Neuroscience College of Medicine UF Gainesville United States United States Air Force Research Laboratory Eglin Air Force Base ValparaisoFL United States Department of Electrical and Computer Engineering Herbert Wertheim College of Engineering UF Gainesville United States Department of Computer Information and Science and Engineering Herbert Wertheim College of Engineering UF Gainesville United States Department of Mechanical and Aerospace Engineering Herbert Wertheim College of Engineering UF Gainesville United States

ISBN: (纸本)9783031439001

Out-of-distribution (OOD) generalization poses a serious challenge for modern deep learning (DL). OOD data consists of test data that is significantly different from the model’s training data. DL models that perform well on in-domain test data could struggle on OOD data. Overcoming this discrepancy is essential to the reliable deployment of DL. Proper model calibration decreases the number of spurious connections that are made between model features and class outputs. Hence, calibrated DL can improve OOD generalization by only learning features that are truly indicative of the respective classes. Previous work proposed domain-aware model calibration (DOMINO) to improve DL calibration, but it lacks designs for model generalizability to OOD data. In this work, we propose DOMINO++, a dual-guidance and dynamic domain-aware loss regularization focused on OOD generalizability. DOMINO++ integrates expert-guided and data-guided knowledge in its regularization. Unlike DOMINO which imposed a fixed scaling and regularization rate, DOMINO++ designs a dynamic scaling factor and an adaptive regularization rate. Comprehensive evaluations compare DOMINO++ with DOMINO and the baseline model for head tissue segmentation from magnetic resonance images (MRIs) on OOD data. The OOD data consists of synthetic noisy and rotated datasets, as well as real data using a different MRI scanner from a separate site. DOMINO++’s superior performance demonstrates its potential to improve the trustworthy deployment of DL on real clinical data. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Magnetic resonance

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8th international conference on Computers, Management and Mathematical Sciences, ICCM 2022

8th International Conference on Computers, Management and Ma...

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8th international conference on Computers, Management and Mathematical Sciences, ICCM 2022

ISBN: (纸本)9783031251931

The proceedings contain 28 papers. The special focus in this conference is on Computers, Management and Mathematical Sciences. The topics include: Simulation of GA-Based Harmonics Elimination in CHMLI Using DTC for Dynamic Load;Discussing the Future Perspective of Machine Learning and Artificial Intelligence in COVID-19 Vaccination: A Review;embedded Platform-Based Heart-Lung Sound Separation Using Variational Mode Decomposition;MIMO: Modulation Schemes for Visible Light Communication in Indoor Scenarios;image Resampling Forensics: A Review on Techniques for Image Authentication;the Impact of Perceived Justice and Customer Trust on Customer Loyalty;performance Analysis of Wind Diesel Generation with Three-Phase Fault;C-RPI: Cluster-Based Rendezvous Point Identification and Mobile Sink-Based data Collection in LR-WPAN;Effect of Weather on the COVID-19 Pandemic in North East India;sentiment Analysis Using Social Media and Product Reviews;a Comparative Study on the Performance of Soft computing Models in the Prediction of Orthopaedic Disease in the Environment of Internet of Things;Maximization of Active Power Delivery in WECS Using BDFRG;Identification and Detection of Credit Card Frauds Using CNN;different Degradation Modes of Field-Deployed Photovoltaic Modules: A Literature Review;impact of Security Attacks on Spectrum Allocation in cognitive Radio Networks;human Activity Detection Using Attention-Based Deep Network;software Vulnerability Classification Using Learning Techniques;service Recovery: Past, Present, and Future;an In-depth Accessibility Analysis of Top Online Shopping Websites;a Stable Model for Learningand Memorization in Over-Parameterized Networks;analysis of Depth Sensing and Lane Detection Algorithms for Advanced Driver Assistance Systems;implementation of E-Commerce Application with Analytics.

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