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SSRN 2023年

作者： Liang, Kang Xiukai, Zhang Krakhmalev, Oleg Engineering Training and Innovation Education Center Shanghai Polytechnic University Shanghai201209 China School of Computer and Information Engineering Shanghai Polytechnic University Shanghai201209 China Department of Data Analysis and Machine Learning Financial University Under the Government of the Russian Federation 4th Veshnyakovsky Passage 4 Moscow109456 Russia

SLSL-QPSO is a software that can find the optimal value of a function. It improves over the QPSO algorithms by leveraging the concept of living and death as swarm layers, the Lévy mutation, the scoped contradiction-expansion coefficient, and the selection of effective layers. Experimental results demonstrate that SLSL-QPSO has superior performance than QPSO and several other variants thus providing a competitive solution to optimization problems. © 2023, The Authors. All rights reserved.

关键词： Particle swarm optimization (PSO)

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An Investigation of Smart Detection for Small Lung Tumor with Tumor Pattern Recognition Algorithm

An Investigation of Smart Detection for Small Lung Tumor wit...

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2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent engineering, ICECONF 2023

作者： Hariharan, G. Prasanth, P. Devarani, P. Arthi Sajana, T. Indhumathi, C. Kumar, Ashok Malla Reddy University Deportment of Artificial Intelligence and Machine Learning Telangana Hyderabad500100 India Vel Tech Multi Tech Dr. Rangarajan Dr.Sakunthala Engineering College Department of Information Technology Tamil Nadu Chennai600062 India R. M K College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Thiruvallur601206 India KoneruLakshmaiah Education Foundation Department of Artificial Intelligence and Data Science Andhra Pradesh Vaddeswaram522502 India R.M. K. Engineering College Department of Computer Science and Business Systems Tamil Nadu Kavaraipettai601206 India BanasthaliVidyapith Department of Computer Science Rajasthan304022 India

ISBN: (纸本)9798350334357

The Small-cell lung tumor is the prime public concern, resulting in increased mortality. Various therapeutic approaches have made progress in the handling of small-cell lung tumor. It's considered the backbone of cytotoxic chemotherapy treatment. However, clinical research shows that because small-cell lung tumor is chemically approachable, high response rates among small-cell lung tumorinfecters are generally not translated into cure. Therefore, it is high time to make progress in small-cell lung tumor research because every success in clinical trials is fought, and every failure is a lesson. A recent study of small-cell lung tumor has improved the prognosis of patients. Some notable research areas have provided new options for patients by conducting multiple clinical trials. In this paper smart edge detection based small lung tumor identification method was proposed. This model operates with the pattern recognition algorithm model. This algorithm smartly finds the edges of the tumor cells and provides the edge results. This will helpful for the doctors to identify the tumor part exactly. © 2023 IEEE.

关键词： Tumors

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A Review of machine learning Algorithms in Diabetes Management

A Review of Machine Learning Algorithms in Diabetes Manageme...

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Sentiment Analysis and Deep learning (ICSADL), International Conference on

作者： Unnati Gayaki Subodh Daronde Abhay Tale Aditya Barhate Department of Computer Science and Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Biomedical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (Deemed to be University)) Wardha Maharashtra India Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Diabetes is a prevalent and chronic disease affecting millions worldwide, posing significant challenges in its management and treatment. This review article aims to explore the current and potential future roles of machine learning (ML) in diabetes management, focusing on progress, applications, and challenges. It discusses various ML techniques, including support vector machines (SVM), logistic regression, decision trees, ensemble methods, and advanced deep learning approaches such as long short-term memory (LSTM) networks and convolutional neural networks (CNN). These methods have demonstrated benefits in analytics, patient monitoring, decision support systems, personalised treatment, and patient engagement. However, significant challenges persist, such as computational complexity, data quality, limited interoperability of systems, and ethical concerns regarding AI use in healthcare. The article addresses these challenges by fostering collaboration between AI implementers and health care professionals. This collaboration is essential to develop long-term, legally compliant, and solution-oriented frameworks that enhance patient care and diabetes management and pave the way for more efficient and ethical AI applications in healthcare.

关键词： Deep learning Support vector machines Ethics machine learning algorithms Reviews Neural networks Medical services Diabetes Convolutional neural networks Long short term memory

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Comparative Analysis of Time Series Forecasting Models for Weather Prediction: ARIMA vs. STL

Comparative Analysis of Time Series Forecasting Models for W...

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International Conference on Computing Communication Control and Automation (ICCUBEA)

作者： Aahash Kamble Sanika Belsare Purva Chaudhari Palash Gourshettiwar Swapnil Gundewar Department of Artificial Intellegence and Data Science Datta Meghe Institute of Higher Education and Research Wardha India Department of Artificial Intellegence and Data Science Datta Meghe Institute of Higher Education and Research(DU) Wardha Maharashtra Department of Computer Science and Medical Engineering Datta Meghe Institute of Higher Education and Research Wardha India Department of Artificial Intellegence and Machine Learning Datta Meghe Institute of Higher Education and Research Wardha India

ISBN: (数字)9798350391770

ISBN: (纸本)9798350391787

This research compares two well-known models for predicting weather: ARIMA (Auto Regressive Integrated Moving Average) and STL (Seasonal-Trend Decomposition using Loess). Accurate weather forecasts are crucial for businesses and daily life, making strong forecasting methods necessary. ARIMA finds linear patterns and time dependencies in data, while STL splits time series into seasonal, trend, and random parts. Using real-world weather data, we train and test these models. We compare their performances using MSE (Mean Squared Error) and R2 (R-squared) metrics. The research demonstrates that the STL model performs better than the ARIMA model at accurately predicting weather patterns and fitting to the data. This superior performance makes the STL model a more dependable choice for weather forecasting. This study enhances techniques for weather prediction and offers valuable information to those making decisions in weather-sensitive businesses.

关键词： Visualization Accuracy Time series analysis Weather forecasting Predictive models Prediction algorithms Market research data models Forecasting Business

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Quantus: An explainable ai toolkit for responsible evaluation of neural network explanations and beyond

The Journal of Machine Learning Research

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The Journal of machine learning Research 2023年第1期24卷 1339-1349页

作者： Anna Hedström Leander Weber Dilyara Bareeva Daniel Krakowczyk Franz Motzkus Wojciech Samek Sebastian Lapuschkin Marina M.-C. Höhne Understandable Machine Intelligence Lab TU Berlin Berlin Germany Department of Artificial Intelligence Fraunhofer Heinrich-Hertz-Institute Berlin Germany Department of Computer Science University of Potsdam Potsdam Germany Department of Electrical Engineering and Computer Science TU Berlin and Department of Artificial Intelligence Fraunhofer Heinrich-Hertz-Institute Berlin Germany Understandable Machine Intelligence Lab TU Berlin Berlin Germany and BIFOLD - Berlin Institute for the Foundations of Learning and Data Berlin Germany

The evaluation of explanation methods is a research topic that has not yet been explored deeply, however, since explainability is supposed to strengthen trust in artificial intelligence, it is necessary to systematically review and compare explanation methods in order to confirm their correctness. Until now, no tool with focus on XAI evaluation exists that exhaustively and speedily allows researchers to evaluate the performance of explanations of neural network predictions. To increase transparency and reproducibility in the field, we therefore built Quantus--a comprehensive, evaluation toolkit in Python that includes a growing, well-organised collection of evaluation metrics and tutorials for evaluating explainable methods. The toolkit has been thoroughly tested and is available under an open-source license on PyPi (or on https://***/understandable-machine-intelligence-lab/Quantus/).

关键词： explainability responsible AI reproducibility open source Python

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Smart Attendance Management using a Self-Supervised learning Approach

Smart Attendance Management using a Self-Supervised Learning...

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Image Processing and Capsule Networks (ICIPCN), International Conference on

作者： D. Vikram H Ajina S Gokilapriya M Malini Department of Computer Science and Engineering Rathinam Technical Campus Coimbatore India Department of Artificial Intelligence and Data Science Rathinam Technical Campus Coimbatore India Department of Artificial Intelligence and Machine Learning Kalingarkarunanidhi Institute of Technology Coimbatore India Department of Information Technology Sri G.V.G Visalakshi College for Women Udumalpet India

ISBN: (数字)9798350367171

ISBN: (纸本)9798350367188

Organizational efficiency is significantly influenced by automated attendance management systems, yet traditional methods often lack flexibility and reliability. This study proposes a novel approach to transform the Smart Attendance Management System (SAMS) by leveraging recent advances in Self-Supervised learning (SSL). The proposed method enhances accuracy and adaptability without explicit supervision by integrating SSL techniques at multiple stages, including feature extraction, anomaly detection, and data preparation. The proposed system learns meaningful representations from unlabeled attendance data through SSL-based data preprocessing, thereby improving the performance of downstream tasks. Feature extraction by utilizing SSL enables the proposed system to extract robust features enhancing both anomaly detection and attendance tracking. Anomaly detection models trained with SSL frameworks can effectively identify unusual attendance patterns. Additionally, SSL-based continuous learning ensures that the proposed system can adapt to the changing user behaviors and various situations. Supported by SSL, the proposed SAMS offers a more precise, efficient, and flexible solution for managing attendance, positioning it to meet the evolving demands of contemporary businesses across various industries.

关键词： Accuracy Soft sensors Self-supervised learning Transforms Medical services Feature extraction Real-time systems

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A Review on Advancing Prosthetics with Artificial Intelligence: Enhancing Control, Sensory Feedback, and Adaptability

A Review on Advancing Prosthetics with Artificial Intelligen...

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International Conference on Trends in Electronics and Informatics (ICEI)

作者： Shivam Bhoyar Renuka Motghare Subodh Daronde Department of Artificial Intillengence and Data Science Datta Meghe Institute of Higher Education and Research(Demeed to be University) Wardha Maharashtra India Department of Artificial Intillengence and Machine Learning Datta Meghe Institute of Higher Education and Research (Demeed to be University) Wardha Maharashtra India Department of Biomedical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research. (Demeed to be University) Wardha Maharashtra India

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

Recent advancements in prosthetic technology have dramatically improved quality of life for amputees, with AI-powered devices now offering capabilities far beyond traditional prosthetics. This paper traces the evolution of prosthetic limbs from basic mechanical designs to today's intelligent systems featuring myoelectric control and neural interfaces. Modern AI integration enables three key benefits: (1) more natural movement through real-time pattern recognition, (2) enhanced sensory feedback that restores touch perception, and (3) adaptive functionality that reduces user fatigue. Breakthroughs in osseointegration and smart materials further improve comfort and usability. These innovations extend beyond limb prosthetics to include cardiac implants and spinal support systems. Looking ahead, we anticipate a new generation of affordable, autonomous prosthetics that will blur the line between human and machine capabilities.

关键词： Limbs Technological innovation Prosthetic limbs Reviews Tactile sensors Surgery Real-time systems Usability Smart materials Prosthetics

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Gradients should stay on Path: Better Estimators of the Reverse- and Forward KL Divergence for Normalizing Flows

arXiv

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arXiv 2022年

作者： Vaitl, Lorenz Nicoli, Kim Andrea Nakajima, Shinichi Kessel, Pan Machine Learning Group Department of Electrical Engineering & Computer Science Technische Universität Berlin Germany BIFOLD - Berlin Institute for the Foundations of Learning and Data Technische Universität Berlin Berlin Germany RIKEN Center for AIP Chuo City Tokyo103-0027 Japan

We propose an algorithm to estimate the path-gradient of both the reverse and forward Kullback–Leibler divergence for an arbitrary manifestly invertible normalizing flow. The resulting path-gradient estimators are straightforward to implement, have lower variance, and lead not only to faster convergence of training but also to better overall approximation results compared to standard total gradient estimators. We also demonstrate that path-gradient training is less susceptible to mode-collapse. In light of our results, we expect that path-gradient estimators will become the new standard method to train normalizing flows for variational inference. © 2022, CC BY.

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A Review on Soil Moisture Monitoring Methods using Satellite Images

A Review on Soil Moisture Monitoring Methods using Satellite...

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Artificial Intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International Conference on

作者： Prateek Verma Aahash Kamble Aditya Barhate Abhay Tale Amit Gudadhe Department of Artificial Intelligence and Machine Learning Faculty of Engineering & Technology Datta Meghe Institute of Higher Education & Research Wardha Maharashtra India Department of Artificial Intelligence and Data Science Faculty of Engineering & Technology Datta Meghe Institute of Higher Education & Research Wardha Maharashtra India Department of Basic Sciences and Humanities Faculty of Engineering & Technology Datta Meghe Institute of Higher Education & Research Sawangi (Meghe) Wardha Maharashtra India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

Soil moisture is an important parameter in the hydrological cycle and an important component for agricultural production and prediction. The regular monitoring of soil moisture plays a big role in the prediction and management of crops and droughts in a region. Climate change has led to irregular patterns in rainfall and extreme weather conditions worldwide. This has led to the farmers taking extreme steps. The traditional method is the best method of measuring soil moisture, but it requires a lot of time and is labor-intensive. Therefore, satellite remote sensing provides an alternative approach to measuring soil moisture covering a large area within less time. The application of satellite remote sensing provides comparative results to traditional methods. This study provides a discussion on spectral indices obtained from satellite data for measuring soil moisture. The current study also provides insights about the applications, challenges, and advantages of using satellite remote sensing for monitoring of soil moisture using remote sensing datasets. The study may be helpful to farmers and government agencies for planning and management of crops and water preservation over the drought prone regions of India.

关键词： Soil measurements Moisture measurement Soil moisture Droughts Crops Satellite images Monitoring Remote sensing Farming Vegetation mapping Indexes Climate change Normalized difference vegetation index

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A Review on Bayesian Methods for Uncertainty Quantification in machine learning Models Enhancing Predictive Accuracy and Model Interpretability

A Review on Bayesian Methods for Uncertainty Quantification ...

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Intelligent Cyber Physical Systems and Internet of Things (ICoICI), International Conference on

作者： Moses Makuei Jiet Prateek Verma Aahash Kamble Chetan Puri Department of Computer Science and Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Wardha Maharashtra India Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Wardha Maharashtra India

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

This research study reviews the statistical fundamentals of machine learning with a focus on Bayesian methods to quantify the uncertainty in model predictions. Bayesian statistics provides a framework for incorporating prior knowledge, updating beliefs, and expressing uncertainty in predictions. This research study will explore Bayesian techniques applied to various aspects of machine learning, including regression, classification, deep learning, and ensemble methods.

关键词： Uncertainty Accuracy Reviews Computational modeling Pipelines Predictive models Probabilistic logic Bayes methods Internet of Things Ensemble learning

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