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检索条件"任意字段=5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023"

共 2608 条记录，以下是131-140 订阅

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Real-Time Classification of Defects During Automated Valve Testing and Inspection 5th

Real-Time Classification of Defects During Automated Valve T...

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5th international conference on data science, machine learning and applications, icdsmla 2023

作者： Singh, Shweta Agarwal, Trapty Haripriya Hemlata School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Dehradun India

ISBN: (纸本)9789819780426

this paper offers a singular method for actual-time type of defects at some point of computerized valve testing and inspection. Mainly, an image processing gadget is developed to discover and classify various defects which includes cracks, dents, foreign items, and different floor irregularities. the machine includes a high-decision digital camera, LED lighting fixtures, processing unit, and defect reputation algorithm. the image processing system captures the picture and methods it to pick out the illness regions. the gadget then extracts the features from every of the detected regions and classifies them the usage of a guide Vector device. the model is demonstrated with manually received pics, and the outcomes show true overall performance in terms of accuracy and speed. Similarly, the system can alert the consumer whilst a potential disorder is identified and the defects can then be in addition inspected and repaired. this approach has the ability to lessen inspection time, enhance manufacturing efficiency, and reduce charges. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lighting fixtures

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Comparison of Different Neural Network Architectures for Cardiac Arrest Risk Prediction 5th

Comparison of Different Neural Network Architectures for Car...

引用

5th international conference on data science, machine learning and applications, icdsmla 2023

作者： Rahman, Mohammed Ziaur Mishra, Awakash Ojha, Ananta Goswami, Shubhashish School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Computer Science & Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India

ISBN: (纸本)9789819780426

Using a realistic dataset based on the United Kingdom clinical exercise studies datalink, various architectures are evolved and evaluated to decide the simplest for predicting the risk of cardiac arrest. the architectures examined include several modified lengthy short-term memory networks, multi-input/multi-output architectures, Convolutional Neural Networks (CNNs), and Residual Neural Networks. Moreover, the consequences of the architectures on the predictions are compared while the statistics are supplemented with external capabilities. Furthermore, the results recommend increasing the facts with external functions, enhancing prediction accuracy for several architectures. Via this observation, conclusions may be drawn about which architectures are satisfactory-desirable for predicting cardiac arrest chance, as well as the consequences of external capabilities on the effectiveness of the architectures. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Characterizing the Weed Species of Agricultural Landscapes Using Hyper Spectral Image Analysis 5th

Characterizing the Weed Species of Agricultural Landscapes U...

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5th international conference on data science, machine learning and applications, icdsmla 2023

作者： Nagaraja, S.R. Mishra, Awakash Prabhu, A. Sivaraman, Himani School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Manduwala Dehradun Navgaon India

ISBN: (纸本)9789819780426

this paper explores using hyper spectral photo evaluation to discover and characterize weed species inside agricultural landscapes. By using excessive-decision hyperspectral imaging, the specific spectral fingerprints of the different weed species attempting to penetrate the crop canopy can be diagnosed. this fact can then be compared to spectral libraries for the precise identity of the weed species gift. With these spectral facts, researchers can map the spread of weeds, understand the composition of weed species present, and verify weed responses to exceptional farming strategies or weather changes. this information can then assist in the development of extra effective and selective weed management strategies in agriculture. In conclusion, hyperspectral picture analysis may provide a treasured tool for characterizing and dealing with weed species within agricultural landscapes. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： characterizing hyperspectral weather

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Leveraging machine learning to Optimize Real-Time data Analysis Performance 5th

Leveraging Machine Learning to Optimize Real-Time Data Analy...

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5th international conference on data science, machine learning and applications, icdsmla 2023

作者： Singh, Vaishali Kaushik, Harshita Verma, Archana Raghavendra, R. Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science and Application Vivekananda Global University Jaipur India Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India

ISBN: (纸本)9789819780426

Leveraging system mastering (ML) algorithms has become an increasingly exciting technique for optimizing actual-time facts analysis performance. In the ever-growing age of massive facts, ML permits us to effectively make the experience of this information extra quickly and correctly. Traditionally, real-time statistics analysis requires complex guide evaluation or is constrained by the insufficient skills of conventional rule-based total strategies. ML can automate this method and identify patterns in the records that may not be apparent at once, even optimizing performance significantly. In addition, ML algorithms, including supervised and unsupervised methods, can be applied to label facts because it comes in, making it simpler to identify styles inside the statistics before a manual evaluation. this technique is called predictive analytics, and it dramatically reduces the time spent manually exploring the statistics. Usually, leveraging system learning to optimize the performance of actual-time records evaluation can improve the process of creating a sense of these facts. through that specialization in predictive analytics and utilizing supervised and unsupervised techniques, we will quickly and appropriately analyze the statistics and make powerful selections. It can be pretty helpful in a selection of industries and conditions. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Predictive analytics

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Development of Fuzzy Logic System for Directional Beamforming in Underwater Communication 5th

Development of Fuzzy Logic System for Directional Beamformin...

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5th international conference on data science, machine learning and applications, icdsmla 2023

作者： Krishnamoorthy, Ramkumar Agrawal, Jaishree Kaipa, Sandhya Agarwal, Trapty Karnataka Bangalore India Department of Computer Science & Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

this paper describes the development of an advanced fuzzy logic system for directional beamforming in underwater communication systems. the fuzzy logic system utilizes a combination of fuzzy and proportional integral derivative controllers, state machines, and signal processing algorithms to create directional beamforming beams that align with the environment. the fuzzy logic system is able to detect changes in the environment, such as changes in water current, and adjust the beamforming beams accordingly. the fuzzy logic system is also optimized to adjust the beamforming beams dynamically. Experiments were carried out to evaluate the performance of the fuzzy logic system in terms of directionality, communication range, and robustness in changing environments. the results showed that the fuzzy logic system provided the best performance compared to all the other systems tested. this system provides an effective solution for directional beamforming in underwater communication systems. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Beamforming

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Hyper Spectral Image Analysis Integrating Fuzzy C-Means Clustering and Feature Selection 5th

Hyper Spectral Image Analysis Integrating Fuzzy C-Means Clus...

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5th international conference on data science, machine learning and applications, icdsmla 2023

作者： Agarwal, Trapty Rengarajan Goyal, Rohit Khan, N. Zafar Ali Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Computer Science & Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India

ISBN: (纸本)9789819780426

Hyper spectral picture evaluation is an effective tool for analyzing large volumes of excessive-dimensional facts. In this paper, a brand new method to hyper spectral photo analysis is provided that integrates fuzzy C-means clustering and function choice. the proposed approach permits for significant quantification of the exclusive additives within a hyper spectral image and allows for the identification of key spectral functions. Additionally, the fuzzy C-way clustering approach is used to identify awesome clusters within the photograph, supplying a useful method for studying complicated datasets. the characteristic choice technique facilitates to further lessen the data set and to achieve the maximum meaningful capabilities for further evaluation. the overall performance of the proposed technique is evaluated via the use of an actual-international hyper spectral photo, and the effects exhibit the effectiveness of the proposed technique for hyper-spectral photograph analysis. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image analysis

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Application of AI/ML Algorithms for Enhancing Precision in Medical Diagnostic Processes 5th

Application of AI/ML Algorithms for Enhancing Precision in M...

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5th international conference on data science, machine learning and applications, icdsmla 2023

作者： Savita Raghavendra, R. Bhatt, Rahul Yamanappa Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India

ISBN: (纸本)9789819780426

the utility of artificial intelligence (AI) and gadget getting to know (ML) algorithms to medical diagnostic processes has been developing in significance in recent years. AI/ML models were evolved to help clinical professionals in imparting correct and green diagnostic approaches. those models are capable of providing precise and correct diagnoses at the same time as reducing the complexity of conventional decision making. AI/ML algorithms are getting used to discover styles in medical statistics that could in any other case be hard for human beings to come across or to offer contextual information to refine and enhance the accuracy of diagnoses. the benefits of using AI/ML algorithms for enhancing precision in medical diagnostic tactics consist of rapid and accurate prognosis, extended performance, and better clinical consequences. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Diagnosis

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Investigating the Role of Applied Cryptography in Digital Forensics 5th

Investigating the Role of Applied Cryptography in Digital Fo...

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5th international conference on data science, machine learning and applications, icdsmla 2023

作者： Mishra, Awakash Nachappa, M.N. Negi, Bichitra Singh Dakhole, Dipali Khushalrao Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Civil Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India

ISBN: (纸本)9789819780426

this paper examines the position of cryptography in virtual forensic evaluation. It aims to discover the one-of-a-kind sorts of cryptographic techniques available to investigators and to talk about how they may be applied to beautify the accuracy of digital forensic work. It also considers the character of cryptographic algorithms and their relative strengths and weaknesses, as well as the numerous applications of cryptographic software and devices in digital forensics. the paper then argues that using cryptography in digital forensic analysis can toughen virtual forensic effects by introducing additional safety features, allowing encrypted facts to be reliably recovered and investigated, and presenting a further degree of warranty while proof is given. It additionally addresses the ethical concerns that must be taken into consideration by virtual forensic investigators during the usage of cryptography in their work. Eventually, it identifies numerous avenues for future studies into using cryptography and its ability applications in virtual forensics. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Digital forensics

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Early Detection of Amyotrophic Lateral Sclerosis with Time Collection Evaluation 5th

Early Detection of Amyotrophic Lateral Sclerosis with Time C...

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5th international conference on data science, machine learning and applications, icdsmla 2023

作者： Mehar, Palavancha Tirumala Krishna Agarwal, Trapty Suneetha, K. Ahmad, Naseem School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bengaluru India Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Dehradun India

ISBN: (纸本)9789819780426

this paper discusses the usage of time series analysis for the early detection of Amyotrophic Lateral Sclerosis (ALS). A time series approach was used to expand a set of rules that may detect the early stages of ALS via analyzing symptomatic changes in signs and symptoms consisting of muscle energy, taking walks potential, speech, swallowing, and different bodily motion. this technique become examined on a retrospective dataset, and proven if you want to come across changes in the circumstance of ALS in a well-timed way. the outcomes suggest that this technique has ability as a tool for early detection in ALS sufferers. the proposed algorithm ought to potentially be used to improve the present day standard of care and help lead to in advance analysis and higher scientific effects. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Motion Potential Swallowing

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Unraveling Disease-Specific Diffusion Patterns in Human Brain Using Diffusion Tensor Image Analysis 5th

Unraveling Disease-Specific Diffusion Patterns in Human Brai...

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5th international conference on data science, machine learning and applications, icdsmla 2023

作者： Prajapati, Sandhya Bhagawati, Rupam Alam, Intekhab Jebakumari, S. Adlin Department of Electrical Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India

ISBN: (纸本)9789819780426

Diffusion tensor imaging (DTI) is a feasible approach to have a look at the structural connectivity of neuronal networks in vivo, as it presents insights into the microstructural and molecular properties of white depend fiber bundles. On this study, we used superior DTI and diffusion tensor image evaluation to analyze the unique diffusion patterns of several distinct diseases in human mind. We obtained statistics from healthful controls and cohorts of sufferers with a range of sicknesses, along with stroke, annoying mind injury, Alzheimer’s ailment, and Parkinson’s ailment, in addition to from postmortem brains of each wholesome and diseased people. Our findings emphasize the capability of the use of this technique to increase ailment-precise structural networks biomarkers for diagnosis and control of neurological illnesses. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Diffusion tensor imaging

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