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检索条件"任意字段=2024 IEEE International Conference on Cognitive Computing and Complex Data"

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TriplePlay: Enhancing Federated Learning with CLIP for Non-IID data and Resource Efficiency 23

TriplePlay: Enhancing Federated Learning with CLIP for Non-I...

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23rd ieee international conference on Machine Learning and Applications, ICMLA 2024

作者： Imteaj, Ahmed Hossain, Md Zarif Zaman, Saika Shahid, Abdur R. School of Computing Southern Illinois University CarbondaleIL United States United States

ISBN: (纸本)9798350374889

The recent advancement of pretrained models shows great potential as well as challenges for privacy-preserving distributed machine learning technique called Federated Learning (FL). With the growing demands of foundation models, it is now an urgent need to explore the potential of such foundation models in a distributed setting. In this paper, In this paper, we delve into the complexities of leveraging foundation models, like CLIP into FL frameworks to preserve data privacy, and efficiently training distributed network clients across heterogeneous data landscapes. We specifically aim to address the issues related to non-IID data distributions, skewed class representation of FL clients' local dataset, communication overhead and high resource consumption due to large, complex model training in an FL setting. To address these, we propose TriplePlay, a framework that tailors CLIP foundation model as an adapter to strengthen FL model's performance and adaptability across heterogeneous data distributions among the clients. Besides, we address the long-tail distribution problem in an FL environment to maintain fairness and optimize the computational resource demands of the FL clients through quantization and low-rank adaptation techniques. A comprehensive simulations results with two distinct datasets and different FL settings demonstrate that TriplePlay efficiently reduces GPU usage and accelerates the convergence time that ultimately reduces the communication cost. © 2024 ieee.

关键词： Federated learning

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Cloud-Based Foot Pressure Analysis for Diabetic Care Using ANN 5

Cloud-Based Foot Pressure Analysis for Diabetic Care Using A...

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5th international conference on data Intelligence and cognitive Informatics, ICDICI 2024

作者： Pradeepa, H. Amarnath, Raveendra N. Sivakumar, V.G. Thamizhamuthu, R. Manikandan, G. Bhuvaneswari, G. Thiagarajar College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Madurai India Mphasis Campbell Dr MelissaTX United States Vidya Jyothi Institute of Technology Department of Electronics and Communication Engineering Telangana Hyderabad India Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Campus-Chengalpattu Tamil Nadu Chennai India Rmk Engineering College Department of Artificial Intelligence and Data Science RSM Nagar Tamil Nadu Chennai India Saveetha Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350389609

It introduces a new method for caring for diabetic feet combines smart pressure monitoring for the feet with wearable gear that is cloud-based and uses artificial neural networks (ANNs). It is essential to recognize diabetic foot ulcers early and monitor them continuously to avoid complications. Wearable pressure sensors record data on the user's foot pressure in real time make up the proposed system. ANNs trained to detect patterns recommend a possible risk of ulceration by analyzing this data once it is transferred to a cloud-based platform. By using cloud-based architecture, healthcare practitioners can remotely monitor patients and intervene as necessary. Clinical trials show the technology improves diabetic foot care by detecting irregularities in pressure distribution and lowering the risk of ulcers and their sequel. Improved patient outcomes and decreased healthcare expenses are achieved via the preventive treatment of diabetic feet made possible by this system, which makes use of breakthroughs in wearable technology and artificial intelligence (AI). © 2024 ieee.

关键词： Remote patient monitoring

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cognitive Science: Towards a Multi-Disciplinary Sense for solving a complex mechanism 5

Cognitive Science: Towards a Multi-Disciplinary Sense for so...

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5th ieee international conference on Advances in computing, Communication Control and Networking, ICAC3N 2023

作者： Akhil, Karnam Sanganbhatla, Suraj Boddapati, Bhavana Vnr Vignana Jyothi Institute Of Engineering And Technology Department Of Computer Science And Engineering Hyderabad India

ISBN: (纸本)9798350330861

cognitive Science is a complex mechanism of solving a problem based on the impact of behavior, analysis of interpretation, studying mental representations functioning of the neurons at the level of brain, perceiving and sensing an object: a living: nature & nurture, a deeper understanding of psychological aspects, relating mind to the brain, handling emotional sense with Intelligent quotient, a meta-analysis study of automation, handling the issues dealing with biasedness due to lack of optimal Hypothesis, correlating to develop ontological representations and illustrating the language of thought on empirical evidence, observational phenomena and applying technologies of Deep learning, machine learning, statistics subset to Artificial Intelligence for optimality. This comprehensive literature study navigates through the historical evolution and interdisciplinary dimensions of cognitive science, spanning from ancient Greek philosophy to its contemporary amalgamation with neuroscience, psychology, linguistics, and artificial intelligence. Examining theoretical paradigms like formal logic, rules, concepts, analogies, images, and connectionism, the study elucidates their pivotal roles in deciphering the mind's intricate representations and computations. Focused on the challenges encountered by cognitive science, the study underscores the integration of analytics, foundational concepts, and diverse methodologies such as statistical tools, machine learning, and prescriptive analytics. A Structured outlined working procedure showcases the application of cognitive science principles to enhance student performance, conducting and evaluating surveys, statistical analysis, machine learning models, and association rule mining. The methodology progresses systematically from data collection and persona analysis to prediction hypothesis formulation, mismatch analysis, marks prediction, rule-based classification, association rule mining, data analytics, and exploratory data analysi

关键词： Association rules

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Internet of Things(IoT) based data Analysis for Feature Selection by Hybrid Swarm Intelligence(SI) Algorithm 2

Internet of Things(IoT) based Data Analysis for Feature Sele...

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2nd ieee international conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Subrahmanyam, Voore Janaki, V. Rao, Pulluri Srinivas Gurrapu, Neelima Mandala, Suresh Kumar Roshan, Rakesh Anurag University Dept. of Cse Telangana State Hyderabad India Jayamukhi Institute of Tech.&Science Dept. of Cse Telangana State Warangal India Sr University Dept. of Cse&ai Telangana State Warangal India Anurag University Dept. of Data Science Telangana State Hyderabad India

ISBN: (纸本)9798350360523

The feature selection is a pre-processing concept for big data and it is a complex to solve. The aim of feature selection is to remove irrelevant and redundant attributes of a specific dataset and to minimize the dimension of the feature set to manage the accuracy. Different types of techniques develop to classify the datasets. The proposed paper providing importance of Swarm Intelligence algorithms(SI). These are one of the flavour of Nature Inspired computing(NIC) algorithms. The Nature Inspired computing algorithms are non-deterministic algorithms and these are inspired by nature. These NIC algorithms are a model for effectively solving real-time computation problems. NIC algorithms posses inbuilt features are collective behaviour, decentralized, social behaviour for efficiently solve the computational problems. The binary variants of meta-heuristic algorithms proposed for efficiently solving feature selection problem by hybridized SI algorithms. The aim of the research paper introduce a hybrid algorithms are combination of standard algorithms Particle Swarm Optimization (PSO) combined with Gray Wolf Optimization (GWO) to become more efficient and erased complexities to solve feature selection problem. In this hybrid approaching is mainly based on meta-heuristics swarm intelligence algorithms is to simulate social behaviour impact on local and global best position of particle swarm intelligence algorithms and hunting behaviour of gray wolf algorithm. This hybrid approach is acquire balance between various search methods to find optimum for features selection. As per available datasets and evaluate metrics to assess the quality and effectiveness of HGWOPSO. © 2024 ieee.

关键词： Internet of things

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Integrating Machine Learning and Epidemiological data to Forecast Zoonotic Disease Outbreaks from Avian Influenza and Other Animal

Integrating Machine Learning and Epidemiological Data to For...

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2024 international conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Priya Dharshini, B. Saranya, A. Senthilnayaki, B. St. Joseph's Institute of Technology Department of Information Technology OMR Chennai India

ISBN: (纸本)9798331543617

There are growing concerns about zoonotic diseases, such as avian influenza and other zoonotic pathogens, that increasingly threaten global health security. Advanced methods for predicting outbreaks are thus needed. This article pools epidemiological data with machine learning in order to develop a time series forecasting model capable of predicting the potential of new outbreaks using historical information. Analyses of past outbreaks, pathogen strains, geographic patterns, and temporal tr capture these relations and allow for early warnings of future zoonotic spill overs. Yet another important advantage for using machine learning in time-series analysis is that it can more effectively process complex high-dimensional data, and it might just be the key to finding some of these hidden patterns. In this way, the tools for dynamic and accurate outbreak prediction will be met, given how unpredictable and ever more frequent zoonotic diseases have become. It would be able to empower public health authorities better to foresee the outbreak potential, allocate appropriate resources beforehand, prepare relevant preventive measures, and mitigate more significant risks of transmission among the whole population. Second, this framework can be used to predict all other zoonotic diseases, including further enhancement of global public health surveillance and pandemic preparedness efforts © 2024 ieee.

关键词： Adversarial machine learning

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cognitive Farming in Agriculture: Deploying CNN-RF for Maize Disease Precision Diagnostics 2

Cognitive Farming in Agriculture: Deploying CNN-RF for Maize...

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2nd ieee international conference on Contemporary computing and Communications, InC4 2024

作者： Nisha Chandran, S. Garg, Ashish Namdev, Dheeraj Kukreja, Vinay Mehta, Shiva School of Computing Graphic Era Hill University Uttarakhand Dehradun India Graphic Era Deemed to be University Computer Science and Engineering Uttarakhand Dehradun India IIMT College of Engineering Artificial Intelligence & Data Science Uttar Pradesh Greater Noida201310 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350383652

A hybrid approach integrating both CNN and RF is investigated in this research study of the Survey regarding the classification of maize leaf diseases with the help of CNN and RF. Our research carefully contrasts five created disease severities as a reported diagnostic tool for agriculturalists. The high accuracy of the model was extensively validated by applying various performance metrics with excellent precision of the different classes ranging from 95.90% to 98.47% for recall ranged between 95.01% to 99.21%, and F1- scores ranged between 96.55% to 98.4. The model achieved a general accuracy of 97.42851 %. We employed various averaging approaches to generalize the performance results across varying illness levels. A macro average yielded an accuracy of 97.37%, recall of 97.48%, and F1-score of 97.42%, implying similarity of performance across classes. Considering class imbalance, the weighted average was accurate at 97.44%, recalled at 97.43%, and the F1-score was similar at 97.43%. The micro average, as the aggregation of results across classes, was remarkably stable at 97.43 %, representing the performance of the model well and especially favoring frequent courses. Early identification and classification of diseases affecting maize leaves significantly contributed to precision agriculture, and this paper presents a reliable and efficient method for a timely diagnosis. It shows the stable performance of the CNN-RF model because many statistical measures are indicated. This implies that it possesses reasonable practicality in agricultural applications that highly support pest control and increase crop yields. © 2024 ieee.

关键词： Agriculture Convolutional neural networks (CNNs) Image classification Maize Leaf Diseases Random Forest

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Integrating Artificial Intelligence for Adaptive Decision-Making in complex System 1

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2nd international conference on Advances in data-driven computing and Intelligent Systems, ADCIS 2023

作者： Verma, Ajay Singhal, Nisha Department of Mechanical Engineering Maulana Azad National Institute of Technology Bhopal Bhopal India Department of Mathematics Indian Institute of Information Technology Bhopal Bhopal India

ISBN: (数字)9789819995219

ISBN: (纸本)9789819995202

The integration of AI techniques in systems engineering can revolutionize decision-making in complex systems. This research investigates AI's role in enhancing adaptive decision-making and addresses integration challenges. AI technologies, like machine learning and cognitive computing, handle large data volumes, identify patterns, and make accurate predictions, enabling decision-makers to gain valuable insights into system behavior, risks, and performance optimization. The research develops intelligent algorithms for real-time data analysis, pattern recognition, and anomaly detection, facilitating proactive decision-making. Intelligent decision support systems integrate AI technologies, providing real-time insights and recommendations to optimize performance and enhance system resilience. The research evaluates AI-based approaches through case studies, assessing their performance and effectiveness. It also addresses challenges such as data privacy, transparency, and system reliability, offering practical guidelines for successful AI integration. In conclusion, this research explores AI integration for adaptive decision-making in complex systems, advancing systems’ engineering and providing insights for practitioners and researchers implementing AI for effective decision-making in dynamic environments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Decision making

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Review of data Security Methods using Low-Earth Orbiters for High-Speed Encryption 4

Review of Data Security Methods using Low-Earth Orbiters for...

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4th international conference on Ubiquitous computing and Intelligent Information Systems, ICUIS 2024

作者： Bakyt, Makhabbat Spada, Luigi La Moldamurat, Khuralay Kadirbek, Zhanserik Yermekov, Farabi L.N. Gumilyov Eurasian National University Department of Information Security Astana Kazakhstan School of Computing Engineering and the Built Environment Edinburgh Napier University 10 Colinton Road EdinburghEH10 5DT United Kingdom L.N. Gumilyov Eurasian National University Department of Space Engineering and Technology Astana Kazakhstan Radio broadcasting complex Llp 'KazMedia ortalygy' Astana Kazakhstan Scientific and Educational Technological Platform of the Kazakh National Agrarian Research University Astana Kazakhstan

ISBN: (纸本)9798331529635

This paper presents an overview of state-of-the-art data security techniques using low-orbit satellites (LOS) such as CubeSats for high-speed encryption. The paper compares various encryption techniques including quantum key distribution (QKD), chaotic mapping-based encryption algorithms, and genetic algorithm and random feature methods. Particular attention is paid to both the advantages and limitations of each approach and their potential applicability to LEO. The key challenges associated with the development and implementation of high-speed encryption on LEO are identified, such as limited resources, power requirements, and the need to ensure reliability in the space environment. Finally, future research directions are suggested, including the development of specialized encryption algorithms, optimization of hardware implementation, and integration of different techniques to achieve maximum efficiency and security. © 2024 ieee.

关键词： Quantum cryptography

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Classification for Stress Release Management using Machine Learning 1

Classification for Stress Release Management using Machine L...

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1st ieee international conference on Advances in computing, Communication and Networking, ICAC2N 2024

作者： Hasan, Yazdani Gupta, Anuj Upadhyay, Diwakar Pandey, Mahima Shankar Malik, Suraj Gochhait, Saikat Noida International University Department of Computer Science India Sharda University School of Engineering and Technology Department of Computer Science & Engineering Uttar Pradesh Greater Noida India Graphic Era Hill University School of Computing Department Haldwani India Galgotia College of Engineering and Technology Department of Computer Science & Engineering Uttar Pradesh Greater Noida India IIMT University Department of Computer Science & Engineering Uttar Pradesh Meerut India Constituent of Symbiosis International Deemed University Symbiosis Institute of Digital and Telecom Management Pune India

ISBN: (纸本)9798350356816

Manipulating the words in some useful ways makes the mind refreshed and worth seeking. Instead of the website, the music software is not able to grab the attention of the user in most of the moody cases. Also, the companies which are running this facility are not able to provide the demanded services. As per the opportunity, the providers are also working hard for more user satisfaction. By adding classification to the existing technology, the providers are designing attractive platforms for the users. The common challenge here is to automate the system. There should be automatic identification system instead of manually operated. Machine learning and data mining techniques are best for classification and deployment of any complex category. This is the only advantage of machine learning, as it drives the optimized method as well as the concept of basic probability. To develop the automated system, the mood classification should be identified from the user's point of view according to their own preferences. Hindi and Bollywood songs are taking heavy traffic in the online channel of music. Also, these are the best for calming the mind. When calculating the response through ratio display, it seems like 70% to 75% accuracy with calls. This paper is also showing the percentage and useful features by implementing the system with machine learning method i.e. Random Forest method. © 2024 ieee.

关键词： Adversarial machine learning

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Multi-scale complex terrain wind power output prediction based on multi-feature refinement fusion 2

Multi-scale complex terrain wind power output prediction bas...

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2nd international conference on Signal Processing and Intelligent computing, SPIC 2024

作者： Deng, Weisi Li, Chonghao Wang, Haohuai Lu, Cong Ma, Xiyuan Hu, Xudong Xu, Yize China Southern Power Grid Power Dispatch Control Center Hydropower and New Energy Division China Technology Research and Development Center Southern Power Grid Digital Grid Group Limited China

ISBN: (纸本)9798350368888

Reliable wind power output (WPO) prediction is a security guarantee for high penetration power systems from wind power. Currently, the utilization of artificial intelligence prediction models in WPO prediction does not fully exploit data features, leading to a lack of accuracy in output prediction. To improve the accuracy and robustness of WPO prediction, this study proposes a model based on K-Medoids-temporal convolutional networks-gated recurrent unit (K-Medoids-TCNGRU) for multi-feature refinement fusion, considering multi-scale complex terrain. Firstly, the raw data is divided into multiple spatial scales utilizing a multi-scale approach. Secondly, dual channel input of weather data and historical WPO data. Thirdly, weather data are divided by the K-Medoids clustering into two separate classes for prediction utilizing GRU and historical WPO for prediction utilizing TCN. Finally, the prediction results are fed into a fully connected neural network for decision-level fusion. In addition, this study replaces all dropout layers with Monte Carlo dropout layers and averages the multiple predictions to improve the certainty of the results. A dataset provided by a region of Natal, Brazil, is utilized to predict the WPO of the region for a day. Experimental results show that K-Medoids-TCN-GRU has a 17.4421 lower root mean square error than the highest performing GRU of the comparison model, validating the K-Medoids-TCN-GRU as more accurate and robust. © 2024 ieee.

关键词： Mean square error

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