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A Novel Approach of Machine Learning Application in Astrophysics: Morphological Feature Wrapping Based Ensemble Method for Galaxy Shape Classification Using GAMA Dataset 1

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International Conference on Advances in Communication technology and computer engineering, ICACTCE 2023

作者： Sinha, Anurag Shahid, Mohammad Nandan, Amritesh Iwendi, Celestine Giri, Abhijeet Kumar Anand, Saprit Department of Computer Science and Information Technology IGNOU New Delhi India Department of Computer Science and Information Technology Amity University Jharkhand Ranchi India Department of Computer Science and Information Technology Graphic Era Deemed to be University Dehradun India School of Creative Technologies University of Bolton BoltonBL3 5AB United Kingdom Department of Computer Science and Engineering KIIT Bhubaneswar India

ISBN: (数字)9783031371646

ISBN: (纸本)9783031371639

The numerous strategies for the automated morphological categorization of galaxies, which uses a variety of supervised machine learning techniques, have not been well examined or compared. As the majority of star galaxy classifiers in use today use condensed summary data from catalogues, rigorous feature extraction and selection are required. With the aid of Deep Convolutional Neural Networks (CNN), a development in machine learning, it may automate the process of feature detection from data by a computer, therefore lowering the demand for qualified human input. Low-level artificial classification has made great progress. While this is the case, Deep Learning consistently outperforms traditional computers. analyzing large datasets while learning. We examine three machine learning techniques for categorizing morphological galaxies: Support Vector Machines (SVM), Random Forests (RF), and Naive Bayes (NB). We examine the efficacy of several machine learning algorithms on each feature representation of a galaxy using a collection of morphological features produced by image analysis as well as the raw image pixel data compressed using PCA (Principal Component Analysis) into PCA features. According to our experiments, RF outperformed SVM and NB. The remainder of the time, morphological features outperformed our PCA features in performance. Thus, the current mechanism is not extremely scalable. A probabilistic classifier that can scale, is based on source data, and requires the least amount of human interaction is essential to resolving these problems. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Classification (of information)

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Prediction of Autoimmune Disease using Ensemble Machine Learning Approaches 2

Prediction of Autoimmune Disease using Ensemble Machine Lear...

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Khandhan, C. Rukumani Sailogaprakash, S. Sakthivel, K. Seenivasan, D. Mekala, R. Kongu Engineering College Computer Technology-PG Erode India K.S.Rangasamy College of Technology Computer Science and Bussiness Systems Tiruchengode India M.Kumarasamy College of Engineering Computer Science and Bussiness Systems Karur India Bannari Amman Institute of Technology Information Science and Engineering Sathyamangalam India

ISBN: (纸本)9798350325799

The term "autoimmune diseases"refers to a class of illnesses where the immune system attacks the body's own healthy cells. Effective treatment and management of many disorders depend on early detection and precise diagnosis. In this research, Using the clinical and laboratory data were it had been imported from the UCI data repository to develop an ensemble model for the prediction of autoimmune disorders. To create a reliable predictive model, the ensemble model incorporates many machine learning approaches like the Gradient boost classifier, SVC and RF, and the KNN. The suggested model was trained and assessed using a sizable dataset of individuals with autoimmune disorders. When the model's performance was compared to that of individual machine learning methods, it was exposed that the ensemble model gives out them all in terms of accuracy, and sensitivity, and set out with the help of hyper-parameter Tuning. © 2023 IEEE.

关键词： Machine learning

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MLPFormer: MLP-integrated transformer for colorectal histopathology whole slide image segmentation

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Neural Computing and Applications 2025年第6期37卷 4651-4661页

作者： Wang, Yuxuan Li, Dan Li, Xuechen Guo, Yan Zuo, Yanfei Shen, Linlin Department of Computer Science Wenzhou-Kean University Wenzhou China School of Electronics and Information Engineering Wuyi University Jiangmen China Department of Histo Pathology Shanghai Histo Pathology Diagnostic Center Shanghai China College of Computer Sicence and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

Colorectal intraepithelial neoplasia is a precancerous lesion of colorectal cancer, which is mainly diagnosed using pathological images. According to the characteristics of lesions, precancerous lesions can be classified into four different grades, i.e., background, normal, low and high level. However, due to the continuity of lesion development, tissue in different stages has high similarity. Recently, visual transformers have achieved impressive results in different visual tasks, due to their capacity of capturing long-range dependencies. However, due to computational cost, transformer cannot well extract detailed feature, which is important for lesion grading. To accurately segment pathological tissues with high similarity, in this work, we embed the multi-head MLP mixer module in the transformer network to extract cell and edge information. Furthermore, we propose a hierarchical MLP decoder to facilitate multi-scale feature fusion. The proposed transformer, namely MLPFormer, achieves remarkable results against competitive baselines on the Histo-CRC Biopsy dataset. The experimental results demonstrate that MLPFormer significantly outperforms the competitive baselines, i.e., a 3% dice improvement is achieved over the SegFormer series. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Image segmentation

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An Empirical and Comparative Study of Graph based Summarization Algorithms 1

An Empirical and Comparative Study of Graph based Summarizat...

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1st International Conference on Machine Learning, computer Systems and Security, MLCSS 2022

作者： Rautaray, Jyotirmayee Panigrahi, Sangram Nayak, Ajit Department of Computer Science and Engineering Bhubaneswar India Department of Computer Science and Information Technology Bhubaneswarline India

ISBN: (纸本)9781665454933

Textual content is growing exponentially on the Internet, and it is also available in the form of countless archives of news articles, scientific studies, legal papers, and other materials. To summarize a text by hand takes a considerable time, a lot of effort, and a great deal of money. As a result, the use of automated text summarization (ATS) or summarization is becoming more popular. Researchers have been working on developing ATS techniques since the 1950s. Text summarization is a branch of natural language processing (NLP) study areas that uses various NLP tools and techniques to provide us with a coherent and concise explanation of large text documents. Extractive, abstractive, and hybrid approaches are used to summarize text. The TextRank, LexRank, and Modified TextRank algorithms are compared in this article. These algorithms are based on graph-based extractive approach in which sentences are represented by vertices and the cosine similarity serves as edge between two sentences. The BBC and CNN news datasets has been used for this study. © 2022 IEEE.

关键词： Natural language processing systems

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Integrating Spatial Prior Adapter for Enhancing SAM Performance in Medical Image Segmentation 12

Integrating Spatial Prior Adapter for Enhancing SAM Performa...

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12th IEEE Global Conference on Consumer Electronics, GCCE 2023

作者： Hu, Jihong Li, Yinhao Lin, Lanfen Chen, Yen-Wei Ritsumeikan University Graduate School of Information Science and Engineering Kusatsu Japan Zhejiang University College of Computer Science and Technology Hangzhou China

ISBN: (纸本)9798350340181

The Segment Anything Model (SAM) has emerged as a focal point of interest within the domain of image segmentation. Capitalizing on extensive pretraining with vast datasets and an interactive prompting mechanism, SAM stands out as a formidable base model for natural image segmentation tasks. However, a number of empirical studies indicate that SAM's performance diminishes substantially when applied to standard medical segmentation tasks. This is largely attributed to the modal and feature disparities between natural and medical images. In response to this challenge, this study introduces a novel, parameter-efficient fine-tuning approach termed the Spatial Prior Adapter (SPA). SPA leverages convolutional neural networks (CNNs) to capture multi-scale features, which are seamlessly amalgamated with the features harnessed from the SAM image encoder. Notably, during the model's fine-tuning phase, the SAM image encoder is frozen, enabling exclusive refinement of the SPA for optimal parameter calibration. Experimental evaluations on two medical segmentation datasets validate the effectiveness of our proposed method. © 2023 IEEE.

关键词： Image segmentation

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Real-Time Monitoring and Control Systems Using IoT-Integrated Wireless Sensor Networks-Leveraging Machine Learning Algorithms for Enhanced Performance and Efficiency

Real-Time Monitoring and Control Systems Using IoT-Integrate...

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Swaminathan, Subbiah Poongavanam, N. Arun, M. Pushparani, S. Muthukrishnan, A. Johri, Prashant Rajalakshmi Engineering College Department of Information Technology Tamil Nadu Chennai India Saveetha University Department of Computer Science and Engineering Chennai602105 India Panimalar Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Meenakshi College of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai600 078 India Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Avadi Chennai India School of Computer Application and Technology Galgotias University India

ISBN: (纸本)9798331542344

This paper introduces a developed real-Time monitoring and control system using Internet of Things (IoT) incorporated Wireless Sensor Networks (WSNs) supported by machines learning algorithms. The system focuses at meeting core issues and questions in terms of productivity, reliability and timeliness within evolving settings. The current paper contributes to the ongoing research and development in the area of IoT and WSN to enable, always-on real-Time data gathering from a variety of discrete sensors which is invaluable for various applications including industrial, environmental and smart city applications. The heart of the system is based on the idea of utilising machine learning methods to acquire and elaborate sensor signals. More specifically, the deep learning models that are used include Long Short-Term Memory (LSTM) networks as well as Reinforcement Learning (RL) algorithms for predictive analytics, as well as anomaly detection. The work also entails a comparison of the system performance based on energy consumption and other factors such as latency, packet delivery ratio and even the accuracy of the predictions made by the system. The findings suggest improved utilisation within substantial ranges;decreased energy use by 30 per cent or more. 7%, latency decrease by 33. Although achieving this was accompanied by an increased data packet loss rate to 3%, the overall packet delivery ratio was boosted to 98%. Additionally, the system established overall accuracy rate of 95% with negligible error percentage. In conclusion, this research proves the applicability of incorporating the IoT and the advanced machine learning methods in the real-Time monitoring and control systems development to overcome cases of low efficiency and reduced performance in numerous real-world applications. © 2024 IEEE.

关键词： Predictive analytics

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An Operative Expectation of Parkinson’s Ailment Using a Hybrid Machine Learning and Artificial Intelligence Systems

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SN computer science 2024年第8期5卷 1146页

作者： Athithan, Senthil Sachi, Savya Singh, Ajay Kumar Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of information technology Lalit Narayan Mishra College of Business Management Bihar Muzaffarpur 842001 India Computer Science and Engineering Department Chandigarh University Uttar Pradesh Unnao India

Human biosignals can be used to detect early signs of Parkinson’s disease (PD). Electroencephalography (EEG) measures brain signals, while electromyography (EMG) measures muscle signals, both of which are useful in studying functional and neuronal changes in Parkinson’s patients. Researchers extracted various time and frequency domain features from EEG and EMG signals of early-stage PD patients and control subjects (CS) to identify motor symptoms. A diagnostic tool based on a mathematical model utilizing an artificial neural network (ANN) and a graphical user interface was developed. The ANN-based classifier, using the EEG and EMG features as inputs, accurately identified early-stage PD patients from control subjects with a classification rate of approximately 100%. The combination of EEG and EMG characteristics resulted in the highest recognition rate of 99.1%. This model demonstrated near-perfect accuracy, with an R-value of 0.9976, indicating its precision. Additionally, it achieved a low validation mean square error of 0.0068 and an elapsed time of 1.32 s. Studies show that combining EEG and EMG features as input parameters effectively distinguishes PD patients from control subjects. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： EEG Frontal Brain Neurological Disorder Parkinson’s Disease Temporal Brain

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Improved Dingo Optimization Algorithm for Human-robot Collaborative U-shaped Disassembly Line Balancing Problem

Improved Dingo Optimization Algorithm for Human-robot Collab...

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2024 International Conference on Cyber-Physical Social Intelligence, ICCSI 2024

作者： Li, Jialong Guo, XiWang Wang, Jiacun Qin, ShuJin Qi, Liang Kang, Qi Liaoning Petrochemical University Information and Control Engineering Fushun China Monmouth University Wes Long Branch Computer Science and Software Eng. NJ United States Shangqiu Normal University Economics and Management Shangqiu China Shandong University of Science and Technology Computer Science and Technology Qingdao China New Jersey Institute of Technology Electrical and Computer Engineering Newark United States

ISBN: (纸本)9798350376739

There is increasing attention on remanufacturing, with the primary focus in the industry on recovering reusable products to generate profit. In the process of optimizing remanufacturing, addressing the balance issue in the operation of the disassembly line is crucial. Therefore, this study proposes the use of an improved Dingo Optimization Algorithm (IDOA) and designs a mathematical model for human-robot collaboration to explore how to achieve higher profits under these conditions. Finally, the improved IDOA is experimentally compared with the commercial optimization solver CPLEX and another intelligent optimization method - the Battle Royale Optimization (BRO). The results demonstrate that this method offers high quality and efficiency. © 2024 IEEE.

关键词： Microrobots

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Explainable Chance-Constrained Programming for VR Video Resource Allocation 18th

Explainable Chance-Constrained Programming for VR Video Res...

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18th International Conference on Intelligent information Hiding and Multimedia Signal Processing, IIH-MSP 2022

作者： Pan, Jeng-Shyang Song, Pei-Cheng Chu, Shu-Chuan Watada, Junzo Li, Jun-Bao College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China Graduate School of Information Production and Systems Waseda University Kitakyushu Japan School of Electronics and Information Engineering Harbin Institute of Technology Harbin China

ISBN: (纸本)9789819906048

In view of the impact of uncertain factors faced by Virtual Reality (VR) video services in the Internet of Vehicles, if the impact of different uncertain factors can be identified, targeted optimization can be made to achieve a better and more personalized user experience. A stochastic resource allocation model for VR video services is developed and turned into a chance-constrained programming problem, which is solved using an optimization solver. This work discusses the concept of shaply value in cooperative games for the first time in order to analyze the marginal contributions of different random variables in chance-constrained programming, and proposes an Explainable Chance-Constrained Programming (ECCP). The marginal contributions of different random variables under different confidence levels and probability distributions are exhibited and evaluated in the experimental test, allowing the degree of influence of uncertainty factors to be explained and analyzed. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Random variables

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Future Prospects of Agriculture Using IoT and Machine Learning 1

Future Prospects of Agriculture Using IoT and Machine Learni...

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1st IEEE International Conference on Smart Power Control and Renewable Energy, ICSPCRE 2024

作者： Hazra, Sudipta Mondal, Arindam Dey, Prasenjit Prabhakar, Santosh Jha, Amit Kumar Rakshit, Nilendu Asansol Engineering College Computer Science and Engineering Burdwan Asansol India Dr. B. C. Roy Engineering College Electrical Engineering Burdwan Durgapur India National Institute of Technology Computer Science and Engineering Odisha Rourkela India Asansol Engineering College Artificial Intelligence and Machine Learning Burdwan Asansol India Asansol Engineering College Information Technology Burdwan Asansol India Nshm Knowledge Campus Computer Science and Engineering Burdwan Durgapur India

ISBN: (纸本)9798350377002

Agriculture balances both the world's food supply and the raw resources required by numerous businesses. The combination of Internet of Things (IoT) and Machine Learning (ML) technologies has revolutionized the agriculture industry, enabling efficient and data-driven decision-making processes. This paper explores the future prospects of using IoT and ML in agriculture, highlighting their potential applications, benefits, and challenges. It examines how IoT devices, such as sensors, drones, and smart irrigation systems, can collect present data on soil situations, weather patterns, crop condition, and resource utilization. ML algorithms can then analyze this data to provide actionable insights for optimizing agricultural practices, improving crop yield, and reducing resource wastage. The paper discusses emerging trends, such as precision agriculture (PA), smart farming, and autonomous farming, which leverage the combined power of IoT and ML. It also addresses the challenges of data security, privacy, and scalability associated with implementing IoT and ML in agriculture. By understanding the future prospects of IoT and ML in agriculture, stakeholders in the agricultural sector can harness these technologies to enhance productivity, sustainability, and profitability. © 2024 IEEE.

关键词： Smart agriculture

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