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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： G. Deepika N.K Anushkannan Manikannan Palanivel Shiv Ashish Dhondiya S Suvitha S. Ramesh Department of Electronics and Communication Engineering. St. Peter's Engineering College Hyderabad Telangana India Department of Electronics and Communication Engineering Kathir College of Engineering Coimbatore Tamil Nadu India Department of Electrical and Electronics Engineering A.K.T Memorial college of Engineering Kallakurichi Tamil Nadu India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Electronics and Communication Engineering S.A. Engineering College Chennai Tamil Nadu India Department of Computational Intelligence Institute of Artificial Intelligence and Machine Learning Saveetha University Chennai Tamil Nadu

The advent of cutting-edge networking technologies like 5G and the Internet of Things portends well for the launch of innovative, data-intensive applications and services in the not too distant future. To employ these strategies, performance measurements must be quantified. Optical waveguide technology (OWC) has the potential to be a major enabling technology for high data-rate communications, with throughputs of up to Tbps. Light cannot propagate above or below the ocean's surface due to turbulence, which is caused by variations in the atmosphere's temperature and pressure, variations in the water's temperature and salinity, and motions of the sea in the marine medium. Other factors that impede light propagation include pointing error from transceiver movement and optical path-loss from scattering and absorption phenomena. Free-space optics (FSO) and underwater optical wireless communication (UOWC) are two examples. On the other hand, the potential application of RF energy harvesting (EH) to create self-sufficient devices and extend node battery life has garnered significant attention. Even while RF and FSO wireless communication systems have been the subject of extensive performance studies in the literature, it may still be difficult to determine their analytical performance requirements for specific setups and schemes. Research on UOWC system performance in the presence of turbulence and path-loss impairments is currently one of the most sought-after areas of study. We provided a numerical approach to compute the UOWC path-loss, which can be utilised to find the UOWC system's bit error rate and received power..

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learning from Time-Tracking Data: Gaussian Process Regression for Efficient Time Management Systems

Learning from Time-Tracking Data: Gaussian Process Regressio...

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Communication, Computing and Signal Processing (IICCCS), IEEE International Conference on

作者： Bandna Thakur Allirani P Prashant Chourasiya K Srinu Mohammed Al-Farouni Santhosh S Department of Computer Application Chandigarh Engineering College Chandigarh Group of Colleges Mohali Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Department of Computer Science & Engineering IES Institute of Technology and Management IES University Bhopal M.P. India Department of Artificial Intelligence & Machine Learning Aditya Engineering College Surampalem India Department Of Computers Techniques Engineering College Of Technical Engineering The Islamic University Najaf Iraq Department Of Computers Techniques Engineering College Of Technical Engineering The Islamic University Of Al Diwaniyah Al Diwaniyah Iraq Department of ISE Nitte (Deemed to be University) NMAM Institute of Technology (NMAMIT) Nitte India

ISBN: (数字)9798350390759

ISBN: (纸本)9798350390766

This paper aims at analyzing the use of Gaussian Process Regression (GPR) in working and creating effective time management system. It incorporates the GPR model that helps in predicting the future time usage by dissecting the time-study data this assists the users in scheduling. The approach adopted entails harvesting data from automated tools and manual logs, pre-processing the data collected, using k-fold cross-validation in training and validating the model. The forecasting accuracy features a low Mean Absolute Error and a high R-squared value, which signifies that the models perform well in terms of predictive capability. This research proves that GPR can actively help with data on time management and benefit time management systems as a whole. Some of the challenges that faced the research include quality of the data and the computation required to process the data was enhanced by effective preprocessing and refined algorithms respectively.

关键词： Training Schedules Computational modeling Signal processing algorithms Gaussian processes Predictive models Signal processing Data models Telecommunication computing Tuning

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CAUSAL BALANCING FOR DOMAIN GENERALIZATION

arXiv

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arXiv 2022年

作者： Wang, Xinyi Saxon, Michael Li, Jiachen Zhang, Hongyang Zhang, Kun Wang, William Yang Department of Computer Science University of California Santa Barbara United States David R. Cheriton School of Computer Science University of Waterloo Canada Department of Philosophy Carnegie Mellon University United States Machine Learning Department Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates

While machine learning models rapidly advance the state-of-the-art on various real-world tasks, out-of-domain (OOD) generalization remains a challenging problem given the vulnerability of these models to spurious correlations. We propose a balanced mini-batch sampling strategy to transform a biased data distribution into a spurious-free balanced distribution, based on the invariance of the underlying causal mechanisms for the data generation process. We argue that the Bayes optimal classifiers trained on such balanced distribution are minimax optimal across a diverse enough environment space. We also provide an identifiability guarantee of the latent variable model of the proposed data generation process, when utilizing enough train environments. Experiments are conducted on DomainBed, demonstrating empirically that our method obtains the best performance across 20 baselines reported on the benchmark. Copyright © 2022, The Authors. All rights reserved.

关键词： Benchmarking

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From Margins to Mainstream: Can artificial intelligence (Ai) Reshape Governance for Chittagong Hill Tracts Indigenous Communities?

SSRN

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SSRN 2024年

作者： Chakma, Vaskar Amin, Misbahul Rouf, Abdur Suruj, Al Mahmud Mia, Raju Rafid, Mustavi Computer Science and Technology School of Information Science and Technology Nantong University Seyuan Campus Seyuan Road – 9 Jiangsu Nantong226019 China Computer Science and Technology School of Computing and Artificial Intelligence Southwest Jiaotong University No. 999 Xi'an Road Pidu District Sichuan Chengdu611756 China Management Rabindra University Shahjadpur Upazila Sirajganj Bangladesh Machine Design Manufacturing and Automation College of Mechanical Engineering Yangzhou University Jiangsu China

Indigenous communities in the Chittagong Hill Tracts (CHT) of Bangladesh contend with multifaceted governance challenges, including representation, resource allocation, and access to services. This paper investigates the potential of artificial intelligence (AI) to address these issues and foster inclusive governance practices in the region. Grounded in the following research questions: 1) How are Indigenous communities currently governed in the CHT, and what are the primary challenges they face? 2) What are the opportunities and challenges of leveraging AI to enhance governance in the CHT? 3) How do Indigenous communities perceive the role of AI in governance, and what are their expectations and concerns? the study adopts a self-directed research approach, employing interviews, surveys, and online research to gather insights. Through a synthesis of existing literature and empirical findings, the paper identifies key considerations for deploying AI in governance contexts, including ethical implications, community engagement strategies, and capacity-building initiatives. The research underscores the importance of centering Indigenous perspectives and promoting participatory approaches in the design and implementation of AI-driven governance solutions. © 2024, The Authors. All rights reserved.

关键词： Resource allocation

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Advanced Process Control in Manufacturing Using IoT Devices and artificial Neural Networks

Advanced Process Control in Manufacturing Using IoT Devices ...

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Sustainable Expert Systems (ICSES), International Conference on

作者： V. Sumathi Ramesh S Chethan Chandra S Basavaraddi Visumathi J Ishwarya M.V S. Srinivasan Department of Mathematics Sri Sairam Engineering College Chennai Tamil Nadu India Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of Artificial Intelligence and Machine Learning R&D Don Bosco Institute of Technology VTU Belagavi Karnataka India Department of Information Technology Veltech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Artificial intelligence and Data science Department Agni College of Technology Chennai Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Optimization of industrial activities is significantly helped by Advanced Process Control (APC), which increases efficiency, decreases costs, and improves product quality. artificial Neural Networks (ANNs) and the Internet of Things (IoT) have greatly improved APC in manufacturing. It examines how APC systems may include ANNs and IoT devices in industrial processes. By connecting to a wide range of sensors placed strategically throughout a production facility, IoT devices may gather data in real time, allowing for managing and monitoring crucial parameters. To APC systems, ANNs are invaluable for predictive modeling and control due to their adaptability to changing process conditions and capacity to represent complicated nonlinear interactions. Manufacturers may gain better control over their operations, improving productivity and product quality, by using data from the IoT to train and update ANN models. Recent advances in APC using IoT devices and ANNs are reviewed in this study. Their applications in various industrial industries are discussed, and case examples are shown to demonstrate their usefulness. Further improvements in APC for manufacturing using IoT devices and ANNs may be guided by highlighting the field's problems and future research goals. Challenges with data security, integration challenges, and reliability impact the accuracy of predictive modeling, scalability, and performance of IoT systems.

关键词： Performance evaluation Adaptation models Process control artificial neural networks Predictive models Real-time systems Manufacturing Sensors Internet of Things Monitoring

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machine learning Effects on Consumer Decision-Making and Purchase Behavior in the Fashion Industry of India

Machine Learning Effects on Consumer Decision-Making and Pur...

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International Conference on Science Technology engineering and Management (ICONSTEM)

作者： Shivani Singh A. Rajasekaran Neerajkumar Sathawane Nisha Nandal Minal N Wagh Mayadevi Jadhav Amity Institute of Social Sciences Amity University Noida Department of Artificial Intelligence and Machine Learning Saveetha School of Engineering Chennai Business Administration MIMA Institute of Management Pune Maharashtra Department of Management Bharati Vidyapeeth (Deemed to be University) Institute of Management and Research Delhi Dr DY Patil Institute of Management Studies Akurdi Pune Maharashtra Department of Management Lexicon Management Institute of Leadership & Excellence Pune Maharashtra

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

In this study, we investigate how machine learning (ML) is changing the way Indian consumers shop for and make fashion-related decisions. This research uses a systematic approach that includes data collecting, feature engineering, ML modelling, and statistical analysis to reveal the critical function of ML-powered recommendation systems in influencing consumer decisions and behavior. While the logistic regression study revealed the outsized impact of demographic variables and customer interaction with ML-generated suggestions, the collaborative filtering recommendation model showed impressive performance. The necessity for openness and equity in ML algorithms is emphasized by ethical and legal concerns. The results provide light on the future of fashion retail in India, which will be enhanced by technology but will also adhere to ethical standards, and give practical insights for fashion entrepreneurs, governments, and academics. Research in the future should focus on comprehending this ever-changing environment by probing its intersections with new technologies, sustainability, and other fields.

关键词： Ethics Systematics Statistical analysis machine learning Mathematics Behavioral sciences Sustainable development

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Data Reduction Techniques in Wireless Sensor Networks with AI

Data Reduction Techniques in Wireless Sensor Networks with A...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Gagan Deep Arora Navdeep Kumar Chopra N. Gopinath Sathish Kumar Ravichandran M. Kalyan Chakravarthi Durgaprasad Gangodkar Department of Artificial Intelligence (AI) and Machine Learning (ML) Vardhaman College of Engineering Hyderabad India Department of CSE Chandigarh Engineering College Landran Mohali Punjab Department of Computer Science and Engineering Sri Sairam Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Christ University Bangalore Karnataka India School of Electronics Engineering VIT-AP University Amravati Andhra Pradesh India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

Due to their numerous uses in practically every part of life and their related problems, such as energy saving, a longer life cycle, and better resource usage, the research of wireless sensor networks is ongoing. Its extensive use successfully saves and processes a considerable volume of sensor data. Since the sensor nodes are frequently placed in challenging locations where less expensive resources are required for data collection and processing, this presents a new difficulty. One method for minimizing the quantity of sensor data is data reduction. A review of data reduction methods has been provided in this publication. The different data reduction approaches that have been put forth over the years have been examined, along with their advantages and disadvantages, ways in which they can be helpful, and whether or not using them in contexts with limited resources is worthwhile.

关键词： Wireless communication Wireless sensor networks Data collection Internet Data mining Informatics artificial intelligence

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Black-box reductions for zeroth-order gradient algorithms to achieve lower query complexity

The Journal of Machine Learning Research

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The Journal of machine learning Research 2021年第1期22卷 7685-7731页

作者： Bin Gu Xiyuan Wei Shangqian Gao Ziran Xiong Cheng Deng Heng Huang Department of Machine Learning Mohamed bin Zayed University of Artificial Intelligence UAE and JD Finance America Corporation School of Computer & Software Nanjing University of Information Science & Technology China Electrical and Computer Engineering University of Pittsburgh Pittsburgh PA School of Electronic Engineering Xidian University Xi'an Shaanxi China Electrical and Computer Engineering University of Pittsburgh Pittsburgh PA and JD Finance America Corporation

Zeroth-order (ZO) optimization has been the key technique for various machine learning applications especially for black-box adversarial attack, where models need to be learned in a gradient-free manner. Although many ZO algorithms have been proposed, the high function query complexities hinder their applications seriously. To address this challenging problem, we propose two stagewise black-box reduction frameworks for ZO algorithms under convex and non-convex settings respectively, which lower down the function query complexities of ZO algorithms. Moreover, our frameworks can directly derive the convergence results of ZO algorithms under convex and non-convex settings without extra analyses, as long as convergence results under strongly convex setting are given. To illustrate the advantages, we further study ZO-SVRG, ZO-SAGA and ZO-Varag under strongly-convex setting and use our frameworks to directly derive the convergence results under convex and nonconvex settings. The function query complexities of these algorithms derived by our frameworks are lower than that of their vanilla counterparts without frameworks, or even lower than that of state-of-the-art algorithms. Finally we conduct numerical experiments to illustrate the superiority of our frameworks.

关键词： zeroth order optimization black-box reduction stagewise training convex optimization non-convex optimization

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An Empirical Examination of Balancing Strategy for Counterfactual Estimation on Time Series

arXiv

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arXiv 2024年

作者： Huang, Qiang Meng, Chuizheng Cao, Defu Huang, Biwei Chang, Yi Liu, Yan School of Artificial Intelligence Jilin University Jilin Changchun China International Center of Future Science Jilin University Jilin Changchun China Department of Computer Science University of Southern California California Los Angeles United States Halicioğlu Data Science Institute University of California San Diego San DiegoCA United States Engineering Research Center of Knowledge-Driven Human-Machine Intelligence MOE Jilin Changchun China

Counterfactual estimation from observations represents a critical endeavor in numerous application fields, such as healthcare and finance, with the primary challenge being the mitigation of treatment bias. The balancing strategy aimed at reducing covariate disparities between different treatment groups serves as a universal solution. However, when it comes to the time series data, the effectiveness of balancing strategies remains an open question, with a thorough analysis of the robustness and applicability of balancing strategies still lacking. This paper revisits counterfactual estimation in the temporal setting and provides a brief overview of recent advancements in balancing strategies. More importantly, we conduct a critical empirical examination for the effectiveness of the balancing strategies within the realm of temporal counterfactual estimation in various settings on multiple datasets. Our findings could be of significant interest to researchers and practitioners and call for a reexamination of the balancing strategy in time series settings. Copyright © 2024, The Authors. All rights reserved.

关键词： Time series analysis

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AuxSegCount: Auxiliary Seg-Attention Based Network for Wheat Ears Counting in Field Conditions

AuxSegCount: Auxiliary Seg-Attention Based Network for Wheat...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Jie Zhang Hao Xiong Hecang Zang Meng Zhou Dong Liu Zhonghua Liu Hualei Shen College of Computer and Information Engineering Henan Normal University China Key Lab of Artificial Intelligence and Personalized Learning in Education of Henan China Big Data Engineering Lab of Teaching Resources & Education Quality Assessment of Henan China Centre for Health Informatics Australian Institute of Health Innovation Macquarie University Australia Institute of Agricultural Information Technology Henan Academy of Agricultural Sciences China School of Information Engineering Zhejiang Ocean University China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Accurate wheat ears counting is crucial to the wheat yield estimation. Existing counting methods explore various architectures using density maps for training, and a few works also incorporate an auxiliary network. However, the small wheat ear with background noises makes its detection hard, and the auxiliary network methods tend to ignore interactions with its main network. To mitigate these issues, we propose a novel framework AuxSegCount which includes a segmentation auxiliary network and a main network for wheat ears counting. Unlike density map, the segmentation mask provides more local contexts of wheat ears. We therefore utilise it and introduce the segmentation attention module (SAM) that aims to capture local features around wheat ears. To promote the interactions, we further present the BiAttention Fusion Module (BiFM) that exploits both global information and local contexts into the main network. The experimental results on two datasets show the superiority of our method.

关键词： Training Accuracy Fuses Ear Feature extraction Yield estimation Data mining

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