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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421-430 订阅

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Context Aware data Synchronization in Ubiquitous Networks: cognitive Agent Approach 4th

Context Aware Data Synchronization in Ubiquitous Networks: C...

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4th international conference on Innovative data Communication Technologies and Application, ICIDCA 2022

作者： Bhajantri, Lokesh B. Ayyanavar, Vasudha V. Karnataka Bagalkot587103 India Karnataka Bagalkot587103 India

Ubiquitous networks comprise a diverse set of homogeneous or heterogeneous networks to perform ubiquitous computing operations. In the real-time application context, users can access data from any place and at any time by using mobile devices/nodes such as computer/laptop, mobile, tablet and so on. Nowadays, mobile computing paradigms have an impact on the spread of mobile computing devices, resulting in the transmission of data across ubiquitous networks. To avoid uncertainty, it is critical to maintain data consistency across all devices. This research work has proposed a context aware data synchronization using cognitive agents as a case study on shopping environments. The proposed data synchronization scheme detects network changes, resolves any potential conflicts, and updates various synchronizing devices with different device attributes. cognitive agents are used to track the data across multiple devices. Context aware ubiquitous networks respond to customer requests based on their personal profiles, which often include individual preferences while considering the technological restrictions of the mobile device and the context of operating environment. The proposed work is compared with other existing methods in terms of data accuracy, data synchronization time, and computation and communication overhead. © 2023 The Authors. Published by Elsevier B.V.

关键词： Mobile computing

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cognitive computing for Wheat Leaf Disease Detection System

Cognitive Computing for Wheat Leaf Disease Detection System

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data Science, Agents & Artificial Intelligence (ICDSAAI), international conference on

作者： P Indumathy Arockia Abins S. Maheswari B.S. Liya Y Justindhas Computer Science and Engineering Easwari Engineering College Ramapuram Chennai India

Precision agriculture relies on crop disease detection to boost production and food security. This paper introduces cognitive computing-based wheat leaf disease detection. The approach uses machine learning, natural language processing, and interpretability to identify diseases accurately and easily. The results demonstrate the advantage of cognitive computing over traditional methods. In comparison to CNNs and SVMs, our model had superior accuracy, precision, recall, and F1 scores. The model’s strong performance metrics across folds were confirmed by cross-validation. The cognitive computing model was faster than CNNs and SVMs in inference, making it appropriate for real-time illness identification. Interpretability was examined by showing how the model’s decision-making process may be presented in normal language to build user trust. These findings show that cognitive computing can revolutionize wheat leaf disease diagnosis, providing farmers and agriculture stakeholders with a realistic and effective solution. The work advances precision agriculture by showing how modern technologies may solve difficult crop health monitoring problems.

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9th international conference on Signal Processing and Integrated Networks, SPIN 2022

9th International Conference on Signal Processing and Integr...

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9th international conference on Signal Processing and Integrated Networks, SPIN 2022

ISBN: (纸本)9789819913114

The proceedings contain 21 papers. The special focus in this conference is on Signal Processing and Integrated Networks. The topics include: Preface;alzheimer’s Disease Classification Using Ensemble Methods;design of Helmet-Mounted Dual-Band Conformal Antenna for Military Applications;An Experimental Study of IEEE 802.11n Devices for Vehicular Networks with Various Propagation Loss Models;performance Evaluation of Genetic Algorithm and Flower Pollination Algorithm for Scheduling Tasks in Cloud computing;malicious Node Detection in Heterogeneous Internet of Things;Performance Evaluation of HSRP and GLBP Over OSPF and RIP Routing Protocols;comparative Analysis of Load Balancing Algorithm in Cloud computing;securely Importing of Back-Up File into Cryptowallet;trajectory Optimization of a Satellite;Artificial Intelligence-Aided Trajectory Planning for UAV;9T1R nvSRAM Cell with Improved Read Delay and Margin;difference-Attribute-Based Clustering for Ordinal Survey data;fractional-Order Capacitor Realization Based upon Active Inductor;modeling of Martian Ascent Launch Vehicle;machine Learning Algorithms for Binary Classification of Breast Cancer;applications, Issues, and Deep Learning Analysis of Electronic Health Records;design and Implementation of Multiple-User Variable Rate Channel Assignment Method in cognitive Radio System;Design of Frequency Beam Scanning Antenna Using SIW for mm-wave Applications;QUICLORIS: A Slow Denial-of-Service Attack on the QUIC Protocol;Preventing data Leakage by Trojans in Commercial and ASIC Applications Using TDM and DES Encryption and Decryption.

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Proceedings of the 9th international Ergonomics conference, ERGONOMICS 2022

Proceedings of the 9th International Ergonomics Conference, ...

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Proceedings of the 9th international Ergonomics conference, ERGONOMICS 2022

ISBN: (纸本)9783031339851

The proceedings contain 38 papers. The special focus in this conference is on Ergonomics. The topics include: Study of the Impact of Quantitative Lighting Parameters as a Physical Agent to the Health of Workers During the Manufacturing Process;qualitative Assessment of the Occupational Health and Safety Knowledge Management Practices of Hungarian Companies;powered Smart Textile-Based Exoskeleton for Human Support Movement;influence of Train Route Setting Functions Automation on Railway Traffic Efficiency;regional Differences and Body Mass Index for Population of Croatian Boys;the Formulation for Human Finger-Machine Contact Interaction Considering Nonlinear Flexibility and Non-smoothness Slipping by Multibody Dynamics Approach;using Eye-Tracking data to Investigate the Noticeability of Safety Pictogram on Transparent Packaging;ergonomics in Ukraine;decision Support for Solving Problems of Ergonomic Provision of Contact Centers;reachability Simulation of Car Dashboard Commands: A Comparison Between Delmia™ v5 and Unreal Engine™ v4;recommendations for an Innovative Distance Continuing Education Device Intended for Orthodontic Practitioners;cognitive Ergonomics of the Integrated Collaborative, Interactive E-Learning and E-Training- upon Innovative Networks Empowered with AI and Big data Analytics and computing- of the Remote E-Multidisciplinary Team Board in Breast Cancer Decision Making and Treatment;Interactive E-Learning and E-Training Enhanced by AI and Big data Analytics of cognitive Ergonomics of the Remote E- Multidisciplinary Board in Liver Cancer;effect of Gaming Controllers on Wrist Angles;Assessment of Acoustical Ergonomics Within Cessna Citation CJ2 Aircraft;ergonomic Design of the Hand Saw Handle;ergonomics for Employees’ Satisfaction in Lean Manufacturing Systems;evacuation Planning for Hospital Departments - Time Estimation.

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csl-MTFL: Multi-task Feature Learning with Joint Correlation Structure Learning for Alzheimer’s Disease cognitive Performance Prediction 9th

csl-MTFL: Multi-task Feature Learning with Joint Correlatio...

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19th international conference on Advanced data Mining and Applications, ADMA 2023

作者： Liang, Wei Zhang, Kai Cao, Peng Liu, Xiaoli Yang, Jinzhu Zaiane, Osmar R. Computer Science and Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education Northeastern University Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang110819 China DAMO Academy Alibaba Group Hangzhou China Alberta Machine Intelligence Institute University of Alberta EdmontonAB Canada

ISBN: (纸本)9783031466700

Alzheimer’s disease (AD) is a common chronic neurodegenerative disease and the accurate prediction of the clinical cognitive performance is important for diagnosis and treatment. Recently, multi-task feature learning (MTFL) methods with sparsity-inducing regularization have been widely investigated on cognitive performance prediction tasks. Although they have proved to achieve improved performance compared with single-task learning, the major challenges are still not fully resolved. They involve how to capture the non-linear correlation among the tasks or features, and how to introduce the learned correlation for guiding the MTFL learning. To resolve these challenges, we introduce a correlation structure learning method through self-attention learning and sequence learning for jointly capturing the complicated but more flexible relationship for features and tasks, respectively. Moreover, we develop a dual graph regularization to encode the inherent correlation and an efficient optimization algorithm for solving the nonsmooth objective function. Extensive results on the ADNI dataset demonstrate that the proposed joint training framework outperforms existing methods and achieves state-of-the-art prediction performance of AD. Specifically, the proposed algorithm achieves an nMSE (normalized Mean Squared Error)/wR (weighted R-value) of 3.808/0.438, obtaining a relative improvement of 3.84 % /7.35 % compared with the MTFL method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Neurodegenerative diseases

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international conference on computing, Intelligence and data Analytics, ICCIDA 2022

International Conference on Computing, Intelligence and Data...

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international conference on computing, Intelligence and data Analytics, ICCIDA 2022

ISBN: (纸本)9783031270987

The proceedings contain 44 papers. The special focus in this conference is on computing, Intelligence and data Analytics. The topics include: Preface;Priority-Based Pollution Management ITS;security Issues for Banking Systems;smart Autonomous Bike Hardware Safety Metrics;demand Side Management Based Cost-Centric Solution for a Grid Connected Microgrid System with Pico-Hydro Storage System;impact of Multimodal Model complexity on Classification of Diabetic Retinopathy Level;secure Image data Encryption Scheme for 5G Internet of Things Applications;Symptom Based Health Status Prediction via Decision Tree, KNN, XGBoost, LDA, SVM, and Random Forest;Multiband Microstrip Elliptical Monopole Antenna Design with Mushroom-Like Loadings for DCS, 5G and Ku-Band Applications;an IoT-Based Modular Avionics and Electrical System for Nanosatellite Systems;reinforcement Learning for Intrusion Detection;comparison of Small-Sized Deep Neural Network Models for Hyperspectral Image Classification;autonomous Mobile Robot Mapping and Exploration of an Indoor Environment;time-Sensitive Embedding for Understanding Customer Navigational Behavior in Mobile Banking;do Lunar Cycles Affect Bitcoin Prices?;Stock Price Prediction in Response to US Dollar Exchange Rate Using Machine Learning Techniques;A Service of RSU Communication in Internet of Vehicles (IoV) in Urban Environment;a Comparative Study on Subdural Brain Hemorrhage Segmentation;deep Learning Based Spectrum Sensing Method for cognitive Radio System;Colour Image Encryption Based on HACS and Knuth-Durstenfeld Algorithm;Turkish Sign Language Recognition by Using Wearable MYO Armband;effects of Different Parameters on Sleep Quality;predictive Maintenance on Industrial data Using Soft Voted Ensemble Classifiers;Medical Image Transmission Using a Secure Cryptographic Approach for IoMT Applications;volume Forecasting in Supply Chain: A Mixed Study of Boosting and Prophet Algorithms;blockchain-Based System for Traceability of Counterfeit Med

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Systematic cognitive computing Framework Application Using Medical Information Processing

Systematic Cognitive Computing Framework Application Using M...

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IEEE UP Section conference on Electrical Computer and Electronics (UPCON)

作者： R. Kishore Kanna A. Ambikapathy Mazin Riyadh AL-Hameed Ippa Sumalatha Navdeep Singh Department of Biomedical Engineering Jeruslaem College of Engineering Chennai India Department of of Electrical and Electronics Engineering Galgotias College of Engineering and Technology India Collage of Engineering National University of Science and Technology Dhi Qar Iraq Institute of Aeronautical Engineering Dundigal Hyderabad India Lovely Professional University Phagwara Punjab India

In recent years, the convergence of cognitive computing and natural language processing (NLP) has emerged as a critical field of study, promising significant breakthroughs in medical imaging. This research digs into the integration of cognitive computing approaches with NLP to increase the interpretation and comprehension of complicated medical narratives. We offer a unique framework that harnesses the cognitive capacities of computers to process, analyze, and interpret huge quantities of unstructured medical material. Our technique combines deep learning architectures and semantic analysis to extract therapeutically important information from radiology reports, patient histories, and other textual data sources. Preliminary findings suggest a considerable increase in the accuracy and efficiency of medical picture annotations, leading to more accurate diagnostic insights. Furthermore, the system exhibits an adeptness in understanding sophisticated medical jargons, acronyms, and context-dependent interpretations. This discovery not only emphasizes the promise of cognitive computing in changing medical imaging but also establishes a precedent for its use in other sectors needing complex language interpretation.

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An analysis of Network Security Attacks and their mitigation for cognitive Radio Communication

An analysis of Network Security Attacks and their mitigation...

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international conference on Advanced computing and Communication Systems (ICACCS)

作者： N. Gayathri H. Anandakumar S. Gowri J. Keerthika S. Nithyapriya Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore Tamilnadu Department of Artificial Intelligence and Data Science Sri Eshwar College of Engineering Coimbatore Tamilnadu Department of Artificial Intelligence and Data Science Bannai Amman Institute of Technology Coimbatore Tamilnadu

The primary motivator after the evolution of cognitive radio is the lack of utilization of the spectrum. This technology adds new functionality in the physical layer, data link sub-layer, Transmission Control Protocol/Internet Protocol layer, and Transport layers of the hierarchy of software layers. The majority of the currently available research in cognitive radio has concentrated on security challenges at the physical, MAC layers. Over the past two decades, cyber security threats and problems have grown significantly, especially in cognitive radio networks. This study describes and analyzes various attacks that target each layer at its protocol stack. Each attack's specific detection techniques and defenses are also examined and contrasted.

关键词： Protocols Computational modeling TCPIP Network security Physical layer Software cognitive radio

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Generative approach to Aspect Based Sentiment Analysis with GPT Language Models 12

Generative approach to Aspect Based Sentiment Analysis with ...

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12th international Young Scientists conference in Computational Science, YSC 2023

作者： Chumakov, Stanislav Kovantsev, Anton Surikov, Anatoliy National Center for Cognitive Research ITMO University St. Petersburg197101 Russia

Aspect Sentiment Triplet Extraction (ASTE) is a modern and effective form of sentiment analysis that enables the extraction of highly representative features of source textual data. Recent solutions rely on models built upon Bidirectional Encoder Representations from Transformers (BERT) embeddings and large manually-tagged datasets. This implies that usage of such methods requires large amounts of gold-tagged domain-specific data and is vulnerable to data drifts, while not being able to recognize segmented and summarize more complex terms. We propose an open-domain generative method for ASTE based on Generative pre-trained transformer (GPT) with few-shot and fine-tuning strategies. This method has shown to be applicable for the task, with the models being capable of consistent structuring of the output triplet, simplification of the terms without losing meaningful information, as well as successful analysis of data from unknown domains. Resulting models was tested on mixed domain Russian-language automatically tagged data with thorough manual editing by means of a large language model (LLM) with a few-shot approach and English data, which was only automatically tagged. The developed models have shown to take advantage of the ability to perform learning in a few-shot way, allowing knowledge distillation from larger to cardinally smaller ones. Models have also been tested on summarizing of large amounts of reviews and have shown results comparable to enterprise grade solutions. © 2023 The Authors. Published by Elsevier B.V.

关键词： Sentiment analysis

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Oral Cancer Classification Using GLRLM Combined with Fuzzy cognitive Map and Support Vector Machines from Dental Radiograph Images 1

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1st international conference on Intelligent computing for Sustainable Development, ICICSD 2023

作者： Anuradha, K. Fathima, H. Kavithamani, K. Uma, K.P. School of Computer Applications Karpagam College of Engineering Tamil Nadu Othakkal Mandapam India Department of Science and Humanities Hindusthan College of Engineering and Technology Tamil Nadu Coimbatore India

ISBN: (数字)9783031612985

ISBN: (纸本)9783031612978

In recent years, Fuzzy cognitive Maps (FCM) are widely used in medical decision schemes. According to data, oral cancer is the fifth most common cancer in India, with a higher occurrence among men than women. In order to save lives, correct diagnosis and prompt treatment is required. When oral cancer is identified early, the 5-year survival rate exceeds 80%, but advanced stages of the illness have 5-year survival rates that are less than 20–30%. But, the majority of cases are detected in advanced stages where treatment becomes unsuccessful. To achieve a better classification of oral cancers, Gray Level Run Length Matrix (GLRLM) is combined with Fuzzy cognitive Map and Support Vector Machines. In this work, fifty Dental Radiograph images are used for cancer classification. Initially, Linear contrast stretching algorithm is employed to remove the noise present in the image. The filtered picture is split to create a Region of Interest (ROI). The ROI picture is used to extract the GLRLM features. The classification of cancer is based on the features extracted. From the extracted features, a fuzzy cognitive map is employed. Cancer categorization is carried through Machine Learning approaches. Measures like sensitivity, specificity, precision, accuracy, and F-score have been used to describe the performance of the SVM classifier. The study’s findings demonstrate that GLRLM-based features are strong discriminating features for statistical analysis of Dental Radiograph images and can be helpful in the identification of cancer. By combining the texture features based on GLRLM and FCM, the accuracy (94%) as a whole is improved. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Support vector machines

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