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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Savita Jaison, Feon Nautiyal, Kshitij Sunitha, B.J. Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bengaluru India 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

ISBN: (纸本)9789819780426

Switch getting to know has become increasingly famous in many aspects of pc vision, including picture segmentation. This paper explores switch-gaining knowledge for automated brain tumor segmentation using a deep getting-to-know structure known as U-net. U-internet is a convolution neural community version that has been used efficiently to ramify scientific image segmentation obligations in the beyond. This work utilizes a U-internet model skilled on a huge-scale brain tumor segmentation dataset. The version plays nicely at the utility of brain tumor segmentation, achieving an average cube coefficient of zero. Ninety-three on check scans. Moreover, qualitative consequences display that the U-net version produces segmentation masks with few false-nice mistakes. These consequences reveal the capability of switch getting to know for automatic mind tumor segmentation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image segmentation

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Human Face Detection and Recognition using Artificial Intelligence 7

Human Face Detection and Recognition using Artificial Intell...

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7th International Conference on Trends in Electronics and Informatics, ICOEI 2023

作者： Varma, Mandapati Siva Sai Krishna Abhishek, S. Dandu, Sujatha Anjali, T. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri India Malla Reddy College of Engineering and Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350397284

The method of recognizing and finding a human by their face in an image is called Human Face Detection and Recognition (HFDR). Application of Biometric method in detecting a human's Identity is called as HFD system. HFDR is a verification system designed to determine one's identity. Innovation in science and technology is rapidly achieving the peak level day by day. Artificial Intelligence (AI) and Machine Learning (ML) are the two such innovative inventions that kicked it up a notch and have turned the world to the next phase. This research is intended to detect and recognize faces using HFDR using ML. The consequences of the simulation experiment clearly show that the HFDR using ML method is best known for its accuracy in HFDR compared to the other algorithms used for Human Face Detection (HFD). The exactness of the proposed HFDR using ML method is illustrated through the experimental outcomes. © 2023 IEEE.

关键词： Face recognition

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A New Model for Predicting Pandemic Impact on Worldwide Academic Rankings

A New Model for Predicting Pandemic Impact on Worldwide Acad...

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2023 International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023

作者： Sharada, K.A. Swathi, R. Basi Reddy, A. Selvan, R.Senthamil Sivaranjani, Lingala Hkbk College of Engineering Department of Computer Science and Engineering Bengaluru India Sri Venkateswara College of Engineering Department of Computer Science and Engineering Tirupati India Mohan Babu University School of Computing Department of Computer Science and Engineering Tirupati India Annamacharya Institute of Technology and Sciences Department of Electronics and Communication Engineering Tirupati India Mohan Babu University School of Computing Tirupati India

ISBN: (纸本)9798350317060

People must stay home during pandemics because physical life stops. Pandemic dread closes schools nationwide, hurting students academically. If so, educating kids properly to avoid academic issues is difficult. During the COVID-19 epidemic, schools provided pupils with online instruction. Career and academic tenure were unaffected. This runs counter to what happened during various pandemics throughout history when school years were lost due to widespread illness. Human-computer interface (HCI) technology advancements have made this all feasible. Interference. Many schools made substantial efforts to adapt to the modern teaching paradigm, yet they still ranked poorly internationally in at least one crucial area. As a result, they're worldwide standing took a dramatic nosedive, which will take a lot of time and work to recover from. This study highlights why certain top universities fell in the worldwide rankings during global disruption, such as the recent COVID-19 epidemic, and what may be done to reverse this trend. Potential future pandemics. The unique methodology provided in this paper aids academics in anticipating the influence of epidemics on their overall worldwide rankings, allowing them to make timely recovery choices and strategies. The task is to build a model using sophisticated Machine Learning techniques, with a massive ranking dataset as input. © 2023 IEEE.

关键词： COVID-19

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Enhancement of High Angular Resolution Diffusion-Weighted Imaging Through Parallel Imaging and Motion Correction 5th

Enhancement of High Angular Resolution Diffusion-Weighted Im...

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Sandhya, Prajapati Sreejith, V. Alam, Intekhab Haripriya Department of Electrical Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Manduwala Dehradun Navgaon India 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

ISBN: (纸本)9789819780426

Excessive Angular decision Diffusion-Weighted Imaging (HARDI) is an effective imaging modality that produces high decision pictures of fiber tracts within the brain. Reconstruction of those pictures calls for a huge quantity of computational power and reminiscence, because it acquires appreciably extra diffusion weighted photographs than the conventional diffusion-weighted imaging (DWI) strategies. It makes use of multiple receiver coils in parallel, with every coil measuring the same records set. MC improves the accuracy of HARDI reconstruction by using correcting for motion artifacts. The application of PI and MC along with HARDI allows high-decision reconstruction of fiber tracts inside the mind, with stepped forward sign-to-noise ratio and decreased test instances. When used together, PI and MC also can improve the accuracy of HARDI tractography, providing extra designated records on fiber tract micros. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： measuring movement reconstruction

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Pneumonia Disease Assessment with Full Diagnostics Evaluation 2

Pneumonia Disease Assessment with Full Diagnostics Evaluatio...

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2nd IEEE International Conference on Trends in Quantum computing and Emerging Business Technologies, TQCEBT 2024

作者： Nithin, Rayala Nagarajan, G. Sai Prathap, Thammishetti Venkat Pradeep, Srikonda Sampreeth, Itharaju School of Computing Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Anand Nagar Krishnankoil Virudhnagar India

ISBN: (纸本)9798350384277

Pneumonia remains a critical global health issue, causing significant illness and mortality, especially in vulnerable populations. Early and precise diagnosis is essential for effective management and timely the intervention. This research paper introduces a novel integrated approach for the prediction and comprehensive diagnostics of pneumonia, harnessing cutting-edge advancements in medical imaging, machine learning, and clinical data analysis. The proposed framework merges three fundamental components: (1) Chest X-ray Image Analysis, (2) Clinical Data Integration, and (3) Machine Learning-Based Prediction. The chest X-ray image analysis employs state-of-the-art deep learning techniques to automatically identify and classify pneumonia-related anomalies, offering detailed insights into pulmonary conditions. Simultaneously, clinical data, encompassing patient history, demographics, and laboratory results, are integrated into the predictive model. A hybrid machine learning algorithm, combining Convolutional Neural Networks (CNN) and Gradient Boosting, is utilized to ensure robust and precise disease prediction. The research demonstrates the efficacy of this integrated approach through a thorough evaluation on a substantial dataset of pneumonia cases. Findings indicate a notable enhancement in pneumonia prediction accuracy compared to conventional diagnostic methods. Moreover, the model's interpretability is enhanced, allowing healthcare professionals to scrutinize the rationale behind each prediction, promoting trust and transparency in the decision-making process. © 2024 IEEE.

关键词： Support vector machines

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A Novel Cluster Analysis-Based Crop Dataset Recommendation Method in Precision Farming

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computer Systems science & engineering 2023年第9期46卷 3239-3260页

作者： K.R.Naveen Kumar Husam Lahza B.R.Sreenivasa Tawfeeq Shawly Ahmed A.Alsheikhy H.Arunkumar C.R.Nirmala Department of Computer Science&Engineering Bapuji Institute of Engineering&TechnologyDavangereKarnatakaIndia Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Information Science&Engineering Bapuji Institute of Engineering&TechnologyDavangereKarnatakaIndia Department of Electrical Engineering Faculty of Engineering at RabighKing Abdulaziz UniversityJeddahSaudi Arabia Department of Electrical Engineering College of EngineeringNorthern Border UniversityArarSaudi Arabia

Data mining and analytics involve inspecting and modeling large pre-existing datasets to discover decision-making *** agriculture uses datamining to advance agricultural *** farmers aren’t getting the most out of their land because they don’t use precision *** harvest crops without a well-planned recommendation *** crop production is calculated by combining environmental conditions and management behavior,yielding numerical and categorical *** existing research still needs to address data preprocessing and crop categorization/***,statistical analysis receives less attention,despite producing more accurate and valid *** study was conducted on a dataset about Karnataka state,India,with crops of eight parameters taken into account,namely the minimum amount of fertilizers required,such as nitrogen,phosphorus,potassium,and pH *** research considers rainfall,season,soil type,and temperature parameters to provide precise cultivation recommendations for high *** presented algorithm converts discrete numerals to factors first,then reduces ***,the algorithm generates six datasets,two fromCase-1(dataset withmany numeric variables),two from Case-2(dataset with many categorical variables),and one from Case-3(dataset with reduced factor variables).Finally,the algorithm outputs a class membership allocation based on an extended version of the K-means partitioning method with lambda *** presented work produces mixed-type datasets with precisely categorized crops by organizing data based on environmental conditions,soil nutrients,and ***,the prepared dataset solves the classification problem,leading to a model evaluation that selects the best dataset for precise crop prediction.

关键词： Data mining crop prediction k-prototypes k-means cluster machine learning

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Hybridized CNN-Densenet Model for Dermatological Classification of Histopathological Skin Images 2

Hybridized CNN-Densenet Model for Dermatological Classificat...

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2nd International Conference on Advancements in Smart, Secure and Intelligent computing, ASSIC 2024

作者： De, Anubhav Mishra, Nilamadhab Chang, Hsien-Tsung School of Computing Science & Engineering Vit Bhopal University Madhya Pradesh India Chang Gung University Department of Computer Science and Information Engineering Taoyuan333 Taiwan

ISBN: (纸本)9798350370188

This research paper presents a deep neural network model for the classification and prediction of skin diseases from dermatoscopic images. The model integrates a traditional Convolutional Neural Network with a Pre-trained Densenet model, achieving state-of-the-art performance on the HAM10000 dataset. The methodology involved rigorous data preprocessing, including data cleaning, exploratory data analysis, data splitting, normalization, and label encoding. Techniques such as Model Hybridization, Batch Normalization, Max Pooling, and Data Fitting were employed to optimize the model architecture and data fitting. Initial iterations of the CNN model achieved an accuracy of 76.22% on Test Data and 75.69% on Validation Data. To improve, the model was hybridized with DenseNet architecture and hyperparameter tuning was employed. The model demonstrated an impressive accuracy of approximately 89% on the HAM10000 dataset. This model offers customization potential for more nuanced clinical uses and presents a significant advancement in automating skin disease diagnosis. © 2024 IEEE.

关键词： Neural network models

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Heart Disease Classification using Crow Search Algorithm 5

Heart Disease Classification using Crow Search Algorithm

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Maragatharajan, M. Naveen, P. Nirmala, E. Vit Bhopal University School of Computing Science and Engineering Madhya Pradesh Kothrikalan India Kpr Institute of Engineering and Technology Department of Electrical and Electronics Engineering Tamilnadu Coimbatore India

ISBN: (纸本)9798331504403

This work introduces cutting-edge research to predict heart diseases. This application designed for accurate and personalized assessment of cardiovascular health. This work aims to provide an overall understanding of an individual's heart health by integrating these multifaceted data points. This work utilizes a vast dataset to establish correlations and patterns, enabling it to categorize users into specific risk groups or diagnose potential heart diseases. Through continuous learning and adaptation, the work refines its predictive capabilities, ensuring that it remains up-to-date with emerging medical knowledge. The work not only assists in early detection of cardiovascular issues but also aids in personalized risk assessment, guiding users towards lifestyle modifications and preventive measures based on their unique health profiles. This innovative solution holds the potential to revolutionize the field of cardiovascular health assessment, offering a proactive and data-driven approach to identify and manage heart diseases efficiently. This study considered two well-known machine learning models namely Extreme learning machine algorithms (ELM) and Crow search algorithm (CSA). The collected dataset consists of 10000 samples. We have attained 92.5% accuracy with the data set. The incorporation of machine learning models ensures scalability, adaptability, and precision, making it a valuable tool for both healthcare professionals and individuals striving for proactive heart care. © 2024 IEEE.

关键词： Accuracy Crow search algorithm Data preprocessing Extreme learning machine algorithm Feature selection Heart diseases

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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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Data-driven electrical resistance tomography for robotic large-area tactile sensing

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science China(Information sciences) 2024年第9期67卷 336-337页

作者： Wendong ZHENG Huaping LIU Xiaofeng LIU Fuchun SUN School of Electrical Engineering and Automation Tianjin University of Technology Department of Computer Science and Technology Tsinghua University Key Laboratory of Maritime Intelligent Cyberspace Technology (Hohai University) Ministry of Education

Tactile sensing plays a crucial role in enabling robots to safely interact with objects in dynamic environments [1].Given that potential physical contact can occur at any location during robot interaction, there is a ... 详细信息

关键词： Robot applications

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