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Predicting Liver Disease from MRI with Machine learning-Based Feature Extraction and Classification Algorithms 3rd

Predicting Liver Disease from MRI with Machine Learning-Base...

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3rd international conference on Machine learning, Internet of Things and Big data, ICMIB 2023

作者： Laddha, Snehal V. Yadav, Manish Dube, Dhaval Dhone, Mahansa Sharma, Madhav Ochawar, Rohini S. Department of Electronics Engineering Shri Ramdeobaba College of Engineering and Management Nagpur India

ISBN: (纸本)9789819939312

Globally, liver disease is the leading cause of death for a huge number of people. Inflammation of the liver is caused by a number of factors. Diagnosing liver infection early is essential for more effective treatment. In the current scenario, sensors are employed to identify liver diseases. Precise classification methods are necessary for the automatic diagnosis of illness samples. The cost of diagnosing this illness is high and complicated. The purpose of this study is to decrease the high cost of chronic liver disease diagnosis through prediction. This paper reviews the emerging techniques of data pre-processing, feature extraction, and classification on liver MRI. The primary goal of the current work is to use clinical data to predict the presence or absence of liver disease from MRI by applying various Machine learning methods. In this paper, we have performed feature extraction from liver MRI using the HOG method followed by the Random Forest algorithm for the classification of images. With our approach, the accuracy achieved is 91.67%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Feature extraction

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Annular gap width measurement based on sub-pixel edge fitting 3

Annular gap width measurement based on sub-pixel edge fittin...

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3rd international conference on Electronic Information engineering and data Processing, EIEDP 2024

作者： Zhang, Jiawei Xu, Yongli Liu, Mingyang Liu, Yufeng Du, Guangrong School of Information Science and Engineering Shenyang University of Technology Shenyang110870 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China Northwest Industries Group Co. Ltd. Xi’an710043 China

ISBN: (纸本)9781510680531

To address the difficulty in measuring the annular joint gap structure with structured light, a detection method based on sub-pixel edge fitting is proposed. Firstly, the centerline is extracted by combining the directional template method with the gray centroid method, and then the laser groups are positioned accordingly. Secondly, The Zernike moment method is used to detect the sub-pixel contours of each pair of laser lines. Subsequently, the sub-pixel feature points of the light pattern are obtained by sliding the feature circle along the centerline. Finally, the gap value is determined by calculating the average distance between the fitted straight lines of the sub-pixel feature points. A single annular gap is selected for both visual measurement and manual feeler gauge measurement. The measurement results show that the error between the final measurement value of the proposed method and the manual feeler gauge measurement is no greater than 0.05mm. This indicates that this detection method exhibits good accuracy and high stability. This visual measurement method can meet the requirements for the measurement accuracy of annular joint gap widths. © 2024 SPIE.

关键词： Method of moments

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intelligent Distribution Grid Reliability Simplification Algorithm and Online Computing Implementation 3

Intelligent Distribution Grid Reliability Simplification Alg...

引用

3rd international conference on New Energy and Power engineering, ICNEPE 2023

作者： Wen, Hongjun Chen, Bowei Li, Zuxin Chen, Jiqiao Nie, Zhi Wu, Qian State Grid Zhejiang Electric Power Co. Ltd. Lishui Company Power Grid Development Department Lishui China Technology Co. Ltd Beijing China

ISBN: (纸本)9798350393224

At present, reliability calculation is mostly focused on the calculation of current years, and the calculation of planned years is often carried out using fuzzy estimation. The calculation methods are complex and diverse, which is not conducive to the modular implementation of software. This article deeply analyzes the essence of reliability calculation based on the topology simplification model of power flow, and summarizes the parts related to the grid structure in its classic calculation formula as the grid coefficient. The parts related to the equipment are collected as the equipment coefficient, and the calculation is simplified based on practical applications. Then, based on the power grid resource business center and data center, a centralized (microservices/micro applications) + collaborative computing framework construction model is adopted, integrating basic data such as reliability facility ledger, topology, and graph model, and obtaining important parameters such as fault rate and average recovery time of distribution grid equipment components. Based on simplified calculation formulas, a power supply reliability online calculation micro application module is developed, The calculation results can provide convenient data support for reliability management and decision-making of distribution grids. This article also provides the specific implementation of software architecture design and interface display effects. © 2023 IEEE.

关键词： business architecture data architecture equipment coefficient grid coefficient power flow calculation topology power supply reliability technical architecture

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A Review on Analyzing and Predicting the State of Cancer Disease using Machine learning Algorithms 3

A Review on Analyzing and Predicting the State of Cancer Dis...

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3rd international conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Selvaraj, Babu Aruna, M. SRM Institute of Science and Technology Department of Computer Science and Engineering Chengalpattu Distric Tamil Nadu Kattankulathur603203 India SRM Institute of Science and Technology Department of Computing Technologies Chengalpattu District Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798350343632

Cancer represents a chronic anomaly induced by mutations within genes that regulate the functioning of body cells, particularly their growth and replication. Over 100 variations of cancer exist, each named after the tissue or organs in which they originate. Cancer stands as the leading global cause of death. Timely detection and prognosis of cancer have become imperative, aiding subsequent medical intervention for patients. This research offers a comparison of machine learning, deep learning, and optimization strategies for cancer detection. Numerous researchers have focused on cancer diagnosis and prognosis, with each approach exhibiting distinct accuracy rates influenced by specific circumstances, tools, and datasets. Navigating challenges associated with obtaining high-quality labeled datasets, addressing the diverse nature of cancer, and ensuring the ethical integration of machine learning into clinical workflows are essential obstacles. Additionally, dealing with class imbalance, extracting features from intricate biological data, ensuring dependable validation, and integrating various data sources pose crucial challenges for developing robust algorithms in cancer analysis. The main aim of this study is to evaluate various existing Machine learning and optimization approaches, identifying the most suitable methods to accommodate extensive datasets with high prediction accuracy. This revision primarily aims to showcase previous research on machine-learning techniques employed for cancer detection. The study presents comprehensive information for novices wishing to explore machine learning methods and establish a foundation in deep learning. © 2023 IEEE.

关键词： Cancer Diagnoses Deep learning (DL) Machine learning Optimization Techniques.

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EMG Feature Selection Approach to Improve Classification Accuracy - A Review 3

EMG Feature Selection Approach to Improve Classification Acc...

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3rd international conference on Secure Cyber Computing and Communications, ICSCCC 2023

作者： Agarwal, Shweta Chadha, Raman Bhati, Bhoopesh Singh Chandigarh University Computer Science & Engineering Punjab Mohali India Indian Institute of Information Technology Computer Science & Engineering Haryana Sonepat India

ISBN: (纸本)9798350300710

Nowadays, many computing systems are a part of daily life;therefore, it is more comfortable to communicate with them naturally. The field of human-computer interaction (HCI) was created in order to break down the obstacles to human-computer communication. One of the types of HCI that is, Hand Gesture Recognition (HGR), which predicts the type of a certain hand action. The electrical activity of skeletal muscles is one potential input for these types of models. The purpose of movement produced by the human brain is communicated through electromyography (EMG) signals. In order to identify EMG data that is precise for any class, the most pertinent collection of EMG attribute values must be used to train a system. With the aid of a combination of machine learning and EMG data, this paper aims to present the most recent real-time feature selection techniques and classification algorithms in a comprehensive review of the literature. Finally, several gaps have been found that may point the way for fresh lines of inquiry in the field of EMG-based gesture detection, and a proposed approach for improving the overall classification accuracy of the EMG signal is presented. © 2023 IEEE.

关键词： Human computer interaction

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Secure data Reduplication System with Efficient and Reliable Multi-Key Management in Cloud Storage 3

Secure Data Reduplication System with Efficient and Reliable...

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3rd international conference on Innovative Practices in Technology and Management, ICIPTM 2023

作者： Jayaram, B. Suguna, T. Fairooz, Shaik Arul Selvam, P. Vimal, S. Rmk Engineering College Department of Computer Science and Business Systems Tamil Nadu Kavaraipettai India Panimalar Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Malla Reddy Engineering College Department of Cse -AIML Telangana Secunderabad India Hindusthan College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India School of Computing Srm Institute of Science and Technology Department of Computational Intelligence Tamil Nadu Kattankulathur India

ISBN: (纸本)9798350336238

Malicious hackers may compromise hundreds or millions of machines with little effort, allowing them to shut down networks, steal sensitive data, and compromise critical infrastructure. data is stolen and used for malicious purposes by hackers who exploit security flaws in the network. And the sophisticated technology they use, like the bucks they do, are continually feeding them the information they needed, not just once. Moreover, even if Internet firewalls have improved, cybercrime has been on the rise. Then, we have a long way to go before we approach the limit, what with all the different kinds of cloud server devices that can connect to the internet nowadays. It's certain that in the future, little devices, no matter how helpful they are really in our everyday lives, will become the preferred tool of villains. In this research, we offer a novel security technique for protecting sensitive data on cloud servers. This intelligent cloud server was protected from any hacking with the aid of this algorithm. The system's output was measured against those of pre-existing algorithms. More than 96% of inputs are authenticated by the proposed technique, while password cracking operations are reduced by about 3%. The source code security management is improved to 99% and the updating issues for various devices entries is reduced 0.02%, and further the privacy breach is reduced up to 0.32%. Hence the proposed method was more secured from cyber security issues. © 2023 IEEE.

关键词： Sensitive data

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Direction of arrival estimation for coherent signals based on convolutional neural network with attention mechanism 3

Direction of arrival estimation for coherent signals based o...

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3rd international conference on Electronic Information engineering, Big data, and Computer Technology, EIBDCT 2024

作者： Qi, Jiajie Cui, Yue Wang, Junfeng Kong, Aokun Yang, Feiyu Zhang, Xiaoyuan College of Computer and Information Engineering Tianjin Normal University Tianjin China

ISBN: (纸本)9781510680449

A direction of arrival (DOA) estimation method for coherent signals is proposed based on an improved convolutional neural network (CNN) with attention mechanism. The real, imaginary and phase components of the covariance matrix are employed as CNN multi-channel inputs. A regression model is adopted as CNN output which avoids the grid mismatch problem caused by multi-classification output. Moreover, to enhance the accuracy of the estimation, attention mechanism is added to the CNN, which could give more weight to the main features effectively. The simulation results demonstrate that the proposed method surpasses the performance of other approaches, which is suitable to resolve coherent signals in multipath environments. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Covariance matrix

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IoT-based Efficient Streetlight Controlling, Monitoring and Real-time Error Detection System for Smart Cities in Bangladesh 3

IoT-based Efficient Streetlight Controlling, Monitoring and ...

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3rd international conference on Electrical, Computer and Communication engineering, ECCE 2023

作者： Chowdhury, A.T.M Mustafa Masud Sultana, Jeenat Sourav, Md Sakib Ullah Southern University Bangladesh Department of Computer Science and Engineering Chattogram Bangladesh School of Management Science and Engineering Shandong University of Finance and Economics Jinan China

ISBN: (纸本)9798350345360

Meeting the increasing electricity demand has become a major issue for every nation, especially for a third world country like Bangladesh. A huge wastage of electricity can be seen in Bangladesh due to improper streetlight management that leads to an enormous financial loss every year. Many noteworthy works have been done by researchers from different parts of the world in tackling this issue by using the Internet of Things (IoT) yet very few in Bangladesh's perspective. In this work, we propose an efficient IoT-based integrated streetlight framework that offers cloud powered monitoring, controlling through light dimming as per external lighting conditions and traffic detection, as well as fault detecting system to ensure low power and electricity consumption. We analyzed data from Dhaka North and South City Corporation, Narayanganj City Corporation and Chattogram City Corporation where our proposed model demonstrates a reduction in energy cost of up to approx. 60% than that of the existing system. This intelligent and efficient system can be implemented in major cities of Bangladesh as part of a smart city and the surplus of the electricity can be utilized for household use and other essential purposes. © 2023 IEEE.

关键词： Energy efficiency

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Supervised learning Approaches on the Prediction of Diabetic Disease in Healthcare 3rd

Supervised Learning Approaches on the Prediction of Diabetic...

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3rd international conference on Machine learning, Internet of Things and Big data, ICMIB 2023

作者： Patel, Riyam Sivaiah, Borra Patel, Punyaban Sahoo, Bibhudatta Kattankulathur Chennai India Department of Computer Science and Engineering CMR College of Engineering and Technology Kandlakoya Hyderabad India Department of Computer Science and Engineering CMR Technical Campus Kandlakoya Hyderabad India Department of Computer Science and Engineering National Institute of Technology Odisha Rourkela India

ISBN: (纸本)9789819939312

There are many chronic diseases out of which Diabetes is one;that increases sugar level in the blood and is one of the most fatal that effect different organs in the human body. Diabetes can cause a variety of slow bad consequences if not detected and left without given medical care. The emergence of machine learning approaches, on the other hand, solves this crucial issue. The purpose and objectives of this work is to build a prototypical model that can properly forecast diabetes whether or not a person will suffer from it. To detect diabetes at an early stage, our work employs three classification algorithms based on supervised learning: Random Forest, Naïve Bayes Classifier and Multilayer Perceptron Network. The PIDD database has been used in the experiments. The Precision, Accuracy, Recall, F-Measure, and ROC Area are all used to calculate the efficiency of the above three algorithms. The correctness and accuracy of a classification system is measured by the number of occurrences that are correctly classified and those that are mistakenly classified. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Multilayer neural networks

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An Evaluation of AI-Driven data Mining Techniques for Large-Scale data Processing 3

An Evaluation of AI-Driven Data Mining Techniques for Large-...

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3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Choudhary, Sheetal Khatoon, Naghma Singh, K. Ranjith Divya, K. Kumar, Santosh D. Gupta, Abhishek Kumar Himalayan University Department of Computer Science Itanagar India Usha Martin University Department of Coputing & Information Technology Ranchi India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Mangalayatan University Department of Computer Science Aligarh India

ISBN: (纸本)9798350319125

Information mining and synthetic intelligence (AI) have gained a lot interest in recent years in the discipline of large-scale statistics processing. AI-driven statistics mining strategies are especially effective for identifying beneficial styles and functions in huge datasets. This paper presents an assessment of several typically used AI-pushed information mining techniques for processing big datasets and compares their performance in phrases of pace, accuracy, and scalability. Not unusual algorithms including precept component evaluation and clustering analysis are evaluated and in comparison to recent advances in AI-driven records mining which includes deep mastering and associative statistics mining. The overall performance of every technique is measured throughout 3 categories: speed, accuracy, and scalability. The effects of the evaluation show that deep learning and associative information mining outperform the conventional strategies in terms of accuracy and scalability whilst they're fairly slower than the other techniques. Therefore, these algorithms can be more suitable for positive types of massive-scale statistics processing tasks that require excessive accuracy and scalability. © 2023 IEEE.

关键词： component evaluation learning processing scalability

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