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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Kaswan, Kuldeep Singh Dhatterwal, Jagjit Singh Malik, Kiran Baliyan, Anupam School of Computing Science and Engineering Galgotias University UP Greater Noida India Koneru Lakshmaiah Education Foundation Artificial Intelligence and Data Science AP Guntur India Matu Ram Institute of Engineering and Management Computer Science and Engineering Haryana Rohtak India Chandigarh University Department of Computer Science and Engineering Chandigarh India

ISBN: (纸本)9798350369960

Generative Artificial Intelligence (AI) stands as a transformative paradigm in machine learning, enabling the creation of complex and realistic data from latent representations. This review paper comprehensively surveys the landscape of Generative AI, encompassing its foundational concepts, diverse models, training methodologies, applications, challenges, recent advancements, evaluation metrics, and ethical dimensions. The paper begins by introducing Generative AI's significance across various domains, presenting its pivotal role in producing synthetic data with applications spanning image synthesis, text generation, music composition, drug discovery, and more. The objectives lie in elucidating the foundational concepts, delving into model intricacies, unveiling the training procedures, exploring its application landscape, addressing challenges, envisioning future directions, and discussing ethical ramifications. The foundational section elucidates the diverse array of generative models, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), flow-based models, Generative Reinforcement Learning (GRL), and advanced hybrid architectures. Subsequently, evaluation metrics ranging from Inception Score to perceptual similarity metrics and human evaluations are surveyed to assess generative model performance. Finally, ethical considerations underscore the necessity for addressing biases, misuse, intellectual property concerns, and the call for responsible AI development and regulation in the Generative AI landscape. © 2023 IEEE.

关键词： Advanced Hybrid Architectures Flow-Based Models Generative Adversarial Networks (GANs) Generative Reinforcement Learning (GRL) Variational Autoencoders (VAEs)

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Cluster analysis: application of K-means algorithm to explore the sea surface temperature and salinity patterns

Cluster analysis: application of K-means algorithm to explor...

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2023 International Conference on Computer Application and Information Security, ICCAIS 2023

作者： Ding, Keyan Liu, Zihan Wang, Mo School of Computing and Data Science Xiamen University Malaysia Jalan Sunsuria Bandar Sunsuria Selangor Darul Ehsan Sepang43900 Malaysia School of Mathematics and Statistics The University of Sheffield Sheffield United Kingdom Department of Statistical Science University College London LondonWC1E 6BT United Kingdom

ISBN: (纸本)9781510675032

This paper investigates how the sea surface temperature (SST) and salinity (SSS) change with the seasons based on K-means clustering analysis. For index k, the elbow method is used to estimate the value. Then this paper analyzes the clustering of SSS and SST in a specific region that contains both characteristic SST and SSS features and compare the clustering figures from each season. Meanwhile, by comparing K-means cluster results computed from raw data to those obtained for classified histograms, we find that it is ideal to combine these two types of data to do an analysis. To illustrate our methodology, we use the Silhouette Coefficient to find the goodness of our K-means cluster, and the calculated Silhouette Coefficient equals 0.8, which illustrates our model has relatively high quality and accuracy. Lastly, we apply other clusters, such as DBSCAN and Gaussian Mixture Model to the same data, but the results indicate the difference between our data is too weak to be clustered by these two methods. © 2024 SPIE.

关键词： Surface temperature

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Deep Dynamic Probabilistic Canonical Correlation Analysis

Deep Dynamic Probabilistic Canonical Correlation Analysis

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Shiqin Tang Shujian Yu Yining Dong S. Joe Qin Department of Data Science City University of Hong Kong Hong Kong Department of Artificial Intelligence Vrije Universiteit Amsterdam Amsterdam Netherlands Department of Computing and Decision Science Lingnan University Hong Kong

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

This paper presents Deep Dynamic Probabilistic Canonical Correlation Analysis (D 2 PCCA), a model that integrates deep learning with probabilistic modeling to analyze nonlinear dynamical systems. Building on the probabilistic extensions of Canonical Correlation Analysis (CCA), D 2 PCCA captures nonlinear latent dynamics and supports enhancements such as KL annealing for improved convergence and normalizing flows for a more flexible posterior approximation. D 2 PCCA naturally extends to multiple observed variables, making it a versatile tool for encoding prior knowledge about sequential datasets and providing a probabilistic understanding of the system’s dynamics. Experimental validation on real financial datasets demonstrates the effectiveness of D 2 PCCA and its extensions in capturing latent dynamics.

关键词： Deep learning Analytical models Correlation Buildings Signal processing Probabilistic logic Encoding Nonlinear dynamical systems Speech processing Convergence

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Learning Mixture-of-Experts for General-Purpose Black-Box Discrete Optimization

arXiv

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arXiv 2024年

作者： Liu, Shengcai Wang, Zhiyuan Ong, Yew-Soon Yao, Xin Tang, Ke Department of Computer Science and Engineering Southern University of Science and Technology China Centre for Frontier AI Research Agency for Science Technology and Research Singapore College of Computing and Data Science Nanyang Technological University Singapore Department of Computing and Decision Sciences Lingnan University Hong Kong

Real-world applications involve various discrete optimization problems. Designing a specialized optimizer for each of these problems is challenging, typically requiring significant domain knowledge and human efforts. Hence, developing general-purpose optimizers as an off-the-shelf tool for a wide range of problems has been a long-standing research target. This article introduces MEGO, a novel general-purpose neural optimizer trained through a fully data-driven learning-to-optimize (L2O) approach. MEGO consists of a mixture-of-experts trained on experiences from solving training problems and can be viewed as a foundation model for optimization problems with binary decision variables. When presented with a problem to solve, MEGO actively selects relevant expert models to generate high-quality solutions. MEGO can be used as a standalone sample-efficient optimizer or in conjunction with existing search methods as an initial solution generator. The generality of MEGO is validated across six problem classes, including three classic problem classes and three problem classes arising from real-world applications in compilers, network analysis, and 3D reconstruction. Trained solely on classic problem classes, MEGO performs very well on all six problem classes, significantly surpassing widely used general-purpose optimizers in both solution quality and efficiency. In some cases, MEGO even surpasses specialized state-of-the-art optimizers. Additionally, MEGO provides a similarity measure between problems, yielding a new perspective for problem classification. In the pursuit of general-purpose optimizers through L2O, MEGO represents an initial yet significant step forward. Copyright © 2024, The Authors. All rights reserved.

关键词： Optimization

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Revolutionizing Public Transport: RFID-Enabled Occupancy Monitoring and Frequency Analysis System

Revolutionizing Public Transport: RFID-Enabled Occupancy Mon...

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International Conference on Communication, computing and Internet of Things (IC3IoT)

作者： J. Uma Mageswaran G. Indumathi A.V. Kalpana G. Elangovan S.D. Lalitha S. Nalini Mayank Kumar Sohanda Dept. of Computer Science Engineering Amrita School of Computing Vengal Campus Tiruvallur Dist Tamil Nadu SRM Institute of Science and Technology Ramapuram Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Department of Data Science and Business Systems SRM Institute of Science and Technology Department of Computer Science and Engineering R.M.K Engineering College Department of Computing Technologies SRM Institute of Science and Technolog Kattankulathur

ISBN: (数字)9798350352689

ISBN: (纸本)9798350352696

Public bus stops in India are becoming more crowded due to the country's fast population expansion. People wait a long time for buses to come, then suddenly congregate around them when they do, packing the buses with people and pushing them up into the footboards, which causes accidents. An additional concern of overcrowding is theft. All of this is the result of numerous bus stops not having adequate information on when busses would arrive. The proposed work paves a very important role for the passengers. The passengers will be provided with the information of the arrival of next bus, seat occupancy and the total passengers inside the bus. The information about the seat occupancy, in-passengers and out-passengers is done with the help of Radio Frequency Identification (RFID) tags. The arrival of the next bus is determined by calculating the duration of the buses that are near using RFID readers placed in different bus stations. The count and the frequency will be interfaced in the web application. The passengers can check the application to know the occupancy and today's society where people can catch the right bus at the station and at the right time. This will solve the problems like waiting in bus stops and wasting time and going in a crowded bus.

关键词： Sociology Telecommunication computing Internet of Things Statistics RFID tags Monitoring Accidents

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Design and Development of an IoT Based Smart Irrigation System to Enhance Agricultural Productivity Using ESP8266 Wi-Fi Enabled Controller 5

Design and Development of an IoT Based Smart Irrigation Syst...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Parashar, Vivek Singh, Megha Senthamarai, M. Poonkuzhali, P. Vadar, Parashuram Shankar Swapna, B. School of Computing Science and Engineering Vit Bhopal University Bhopal India Oriental University Department of Computer Science and Engineering Indore India Nandha Engineering College Department of Artificial Intelligence and Data Science Tamilnadu Erode India R.M.D. Engineering College Department of Ece Kavaraipettai India Yashwantrao Chavan School of Rural Development Shivaji University Maharashtra Kolhapur India Dr Mgr Educational and Research Institute Chennai India

ISBN: (纸本)9798350379945

Innovative solutions in many fields, including agriculture, have been made possible by the widespread use of Internet of Things (IoT) technology. Our goal in this research was to increase crop yields by developing a smart irrigation system that would use the IoT. An ESP8266 Wi-Fi-enabled controller serves as the center of the system, collecting data and making decisions all at once. Important parts include sensors for soil moisture, air temperature and humidity, light intensity, water level, a pump to move water, and the ability to connect to a cloud service for data storage and analysis. Water level, soil moisture, temperature, humidity, and light intensity are some of the environmental characteristics that must be continuously monitored in order for the system to function. The data is sent to the main controller in real-time by the sensors using Wi-Fi. Based on predefined thresholds and algorithms, the system dynamically adjusts irrigation schedules to optimize water usage and enhance crop growth. To evaluate the system's performance, sensor readings from multiple trials were analyzed. Results indicate that the system effectively maintains optimal environmental conditions for plant growth, leading to improved agricultural productivity. The integration with a cloud platform enables remote monitoring and control, facilitating efficient water resource management and decision-making for farmers. © 2024 IEEE.

关键词： Soil moisture

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Two-Tier Clustering with Routing Protocol for IoT Assisted WSN

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Computers, Materials & Continua 2022年第5期71卷 3375-3392页

作者： AArokiaraj Jovith Mahantesh Mathapati M.Sundarrajan N.Gnanasankaran Seifedine Kadry Maytham N.Meqdad Shabnam Mohamed Aslam Department of Networking and Communications College of Engineering and Technology SRM Institute of Science and TechnologyKattankulathur603203India Department of Computer Science and Engineering Rajarajeswari College of EngineeringBengaluru560074India Department of Computer Science and Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Computer Science Thiagarajar CollegeMadurai625019India Department of Applied Data Science Noroff University CollegeKristiansandNorway Al-Mustaqbal University College HillahIraq Department of Information Technology College of Computing and Information SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia

In recent times,Internet of Things(IoT)has become a hot research topic and it aims at interlinking several sensor-enabled devices mainly for data gathering and tracking *** Sensor Network(WSN)is an important component in IoT paradigm since its inception and has become the most preferred platform to deploy several smart city application areas like home automation,smart buildings,intelligent transportation,disaster management,and other such IoT-based *** methods are widely-employed energy efficient techniques with a primary purpose i.e.,to balance the energy among sensor *** and routing processes are considered as Non-Polynomial(NP)hard problems whereas bio-inspired techniques have been employed for a known time to resolve such *** current research paper designs an Energy Efficient Two-Tier Clustering with Multi-hop Routing Protocol(EETTC-MRP)for IoT *** presented EETTC-MRP technique operates on different stages namely,tentative Cluster Head(CH)selection,final CH selection,and *** first stage of the proposed EETTC-MRP technique,a type II fuzzy logic-based tentative CH(T2FL-TCH)selection is ***,Quantum Group Teaching Optimization Algorithm-based Final CH selection(QGTOA-FCH)technique is deployed to derive an optimum group of CHs in the ***,Political Optimizer based Multihop Routing(PO-MHR)technique is also employed to derive an optimal selection of routes between CHs in the *** order to validate the efficacy of EETTC-MRP method,a series of experiments was conducted and the outcomes were examined under distinct *** experimental analysis infers that the proposed EETTC-MRP technique is superior to other methods under different measures.

关键词： Wireless networks internet of things energy efficiency clustering multi-hop routing metaheuristics

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Autonomous Multi-Objective Optimization Using Large Language Model

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IEEE Transactions on Evolutionary Computation 2025年

作者： Huang, Yuxiao Wu, Shenghao Zhang, Wenjie Wu, Jibin Feng, Liang Tan, Kay Chen Department of Data Science and Artificial Intelligence Hong Kong Polytechnic University Hong Kong Department of Electrical and Elctronics Engineering Hong Kong Polytechnic University Hong Kong Chongqing University College of Computer Science Chongqing Key Laboratory of Big Data Intelligence and Privacy Computing Chongqing400044 China

Multi-objective optimization problems (MOPs) are ubiquitous in real-world applications, presenting a complex challenge of balancing multiple conflicting objectives. Traditional multi-objective evolutionary algorithms (MOEAs), though effective, often rely on domain-specific expertise for improved optimization performance, hindering adaptability to unseen MOPs. In recent years, the Large Language Models (LLMs) has revolutionized software engineering by enabling the autonomous generation and refinement of programs. Leveraging this breakthrough, we propose a new LLM-based framework that autonomously designs MOEAs for solving MOPs. The proposed framework includes a robust testing module to refine the generated MOEA through error-driven dialogue with LLMs, a dynamic selection strategy along with informative prompting-based crossover and mutation to fit textual optimization pipeline. Our approach facilitates the design of MOEA without the extensive demands for expert intervention, thereby speeding up the innovation of MOEA. Empirical studies across various MOP categories validate the robustness and superior performance of our proposed framework. © 1997-2012 IEEE.

关键词： Multiobjective optimization

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Harnessing the Power of Vocal Signals in COVID-19 Detection Utilizing Machine Learning

Harnessing the Power of Vocal Signals in COVID-19 Detection ...

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IEEE International Conference on Big data

作者： Aleesa Mann Ajinkya P. Jadhav Richard Matovu Vibhuti Gupta Department of Computer Science and Data Science School of Applied Computational Sciences Meharry Medical College Nashville TN USA Department of Computing and Information Science Gannon University Erie PA USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

The global COVID-19 pandemic has strained health-care systems and highlighted the need for accessible and efficient diagnostic methods. Traditional diagnostic tools, such as nasal swabs and biosensors, while accurate, pose significant logistical challenges and high costs, limiting their scalability. This paper explores an alternative, non-invasive approach to COVID-19 detection using machine learning algorithms to analyze vocal patterns, particularly cough and breathing sounds. Leveraging a publicly available dataset, we developed machine learning models capable of classifying audio samples as COVID-19 positive or negative. Our models achieve an AUC of up to 85% and an F1-score of 81%, demonstrating the potential of machine learning in enabling rapid, cost-effective COVID-19 diagnosis. These findings suggest that audio-based diagnostics could be a practical and scalable solution, particularly in resource-limited settings where traditional methods are less feasible.

关键词： COVID-19 Machine learning algorithms Limiting Costs Pandemics Scalability Biological system modeling Nose Machine learning Biosensors

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Does confidence calibration improve conformal prediction?

arXiv

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arXiv 2024年

作者： Xi, Huajun Huang, Jianguo Liu, Kangdao Feng, Lei Wei, Hongxin Department of Statistics and Data Science Southern University of Science and Technology China College of Computing and Data Science Nanyang Technological University Singapore Department of Computer and Information Science University of Macau China Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore

Conformal prediction is an emerging technique for uncertainty quantification that constructs prediction sets guaranteed to contain the true label with a predefined probability. Previous works often employ temperature scaling to calibrate classifiers, assuming that confidence calibration benefits conformal prediction. However, the specific impact of confidence calibration on conformal prediction remains underexplored. In this work, we make two key discoveries about the impact of confidence calibration methods on adaptive conformal prediction. Firstly, we empirically show that current confidence calibration methods (e.g., temperature scaling) typically lead to larger prediction sets in adaptive conformal prediction. Secondly, by investigating the role of temperature value, we observe that high-confidence predictions can enhance the efficiency of adaptive conformal prediction. Theoretically, we prove that predictions with higher confidence result in smaller prediction sets on expectation. This finding implies that the rescaling parameters in these calibration methods, when optimized with cross-entropy loss, might counteract the goal of generating efficient prediction sets. To address this issue, we propose Conformal Temperature Scaling (ConfTS), a variant of temperature scaling with a novel loss function designed to enhance the efficiency of prediction sets. This approach can be extended to optimize the parameters of other post-hoc methods of confidence calibration. Extensive experiments demonstrate that our method improves existing adaptive conformal prediction methods in classification tasks, especially with LLMs. Copyright © 2024, The Authors. All rights reserved.

关键词： Prediction models

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