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检索条件"任意字段=3rd International Conference on Machine Learning and Computer Application, ICMLCA 2022"

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Healthcare Information Systems and Study of machine learning Technology for Cancer Detection

Healthcare Information Systems and Study of Machine Learning...

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Automation, Computing and Renewable Systems (ICACRS), international conference on

作者： T. Naresh B. Gouri S.Riyaz Banu Y. Sravanthi R. Lakshmi Narayana Y. Suneel Kumar Department of Electrical and Electronics Engineering KSRM College of Engineering (Autonomous) Kadapa Kadapa India Department of Computer Science Engineering KSRM College of Engineering (Autonomous) Kadapa Kadapa India Department of Computer Science Engineering Annamacharya University Rajampeta Kadapa India Department of Electronics and Communcation Engineering KSRM College of Engineering (Autonomous) Kadapa Kadapa India Department of Instrumentation SK University Anathapuram Ananthapuram India Department of Electrical and Electronics Engineering SRIT College of Engineering Kadapa India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

Cancer remains a significant global health challenge, with early and accurate diagnosis crucial for effective treatment. While traditional methods have relied on statistical approaches, the complexity of cancer necessitates more sophisticated techniques. This study explores the application of machine learning in cancer diagnosis, focusing on the classification of tumors as benign or malignant. By analyzing multidimensional medical data, machine learning algorithms can effectively identify patterns and predict cancer risk with greater accuracy than traditional methods. The study investigates the potential and limitations of machine learning in cancer diagnosis, addressing challenges such as data quality, model interpretability, and ethical considerations. The findings of this research contribute to the advancement of cancer diagnosis and pave the way for improved patient outcomes.

关键词： Renewable energy sources Accuracy machine learning algorithms Focusing machine learning Medical services Medical diagnostic imaging Cancer Tumors Information systems

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Utilizing machine learning for Sport Data Analytics in Cricket: Score Prediction and Player Categorization

Utilizing Machine Learning for Sport Data Analytics in Crick...

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Mysore Sub Section international conference (MysuruCon), IEEE

作者： R Suguna Y Praveen Kumar J Suriya Prakash P.S Neethu S Kiran Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Engineering Vidhya Jyothi Institute of Technology Hyderabad India Department of Computer Science and Engineering JAIN (Deemed-to-be-University) Bengaluru India School of Engineering and Technology CHRIST (Deemed to be University) Bengaluru India Department of Mathematics Nitte Meenakshi Institute of Technology Bangalore India

Cricket is a popular sport with complex gameplay and numerous variables that contribute to team performance. In recent years, sports analytics has gained significant attention, aiming to extract valuable insights from large volumes of cricket data. Cricket has many fans in India. With a strong fan following, many try to use their cricket intuition to predict the outcome of a match. A set of rules and a points system govern the game. The venue and the performance of each player greatly affect the outcome of the match. The game is difficult to predict accurately as the various components are closely related. The CRR (Current Run Rate) approach is used to predict the final score of the first innings of a cricket match. Total points are calculated by multiplying the average number of runs scored in each over by the total number of overs. For ODI cricket, these methods are useless as the game can change very quickly regardless of the current run rate. The game may be decided by 1 or 2 overs. For more accurate score predictions, a system is needed that can more accurately predict the outcome of an inning. This research paper explores the application of machine learning techniques to predict scores and classify players based on their roles in the squad. The study utilizes a comprehensive dataset comprising various attributes of cricket matches, including player statistics, match conditions, and historical performance. Linear Regression, Logistic Regression, Naive Bayes, Support Vector machines (SVM), Decision Tree, and Random Forest regression models are employed to predict scores. Additionally, player categorization is performed using a classification approach. The results demonstrate the effectiveness of machine learning techniques in enhancing performance analysis and decision-making in the game of cricket.

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StraightLine: An End-to-End Resource-Aware Scheduler for machine learning application Requests

StraightLine: An End-to-End Resource-Aware Scheduler for Mac...

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Artificial Intelligence, Internet of Things and Cloud Computing Technology (AIoTC), international conference on

作者： Cheng-Wei Ching Boyuan Guan Hailu Xu Liting Hu Department of Computer Science and Engineering University of California Santa Cruz Santa Cruz CA USA Geographic Information System (GIS) Center of Florida International University Miami FL USA Department of Computer Engineering and Computer Science California State University Long Beach CA USA

ISBN: (数字)9798331530280

ISBN: (纸本)9798331530297

The life cycle of machine learning (ML) applications consists of two stages: model development and model deployment. However, traditional ML systems (e.g., training-specific or inference-specific systems) focus on one particular stage or phase of the life cycle of ML applications. These systems often aim at optimizing model training or accelerating model inference, and they frequently assume homogeneous infrastructure, which may not always reflect real-world scenarios that include cloud data centers, local servers, containers, and serverless platforms. We present StraightLine, an end-to-end resource-aware scheduler that schedules the optimal resources (e.g., container, virtual machine, or serverless) for different ML application requests in a hybrid infrastructure. The key innovation is an empirical dynamic placing algorithm that intelligently places requests based on their unique characteristics (e.g., request frequency, input data size, and data distribution). In contrast to existing ML systems, StraightLine offers end-to-end resource-aware placement, thereby it can significantly reduce response time and failure rate for model deployment when facing different computing resources in the hybrid infrastructure.

关键词： Training Cloud computing Technological innovation Schedules Heuristic algorithms machine learning Containers Data models Time factors Servers

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Vehicle Pose Detection and application Based on Grille Net

Vehicle Pose Detection and Application Based on Grille Net

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Electronic Information Technology and computer Engineering (EITCE), international conference on

作者： Zhenxin Yao Xinping Song College of Mechanical and Automotive Engineering Shainghai University of Engineering Science Shanghai China

ISBN: (数字)9781728135847

ISBN: (纸本)9781728135854

With the rapid development of technology, autonomous driving technology has also received widespread attention. Among them, the vehicle's pose information provides important information for automatic driving decision and vehicle active safety. To solve this problem, this paper proposes to use the deep learning method to improve the traditional pose network and use the random area connection network to reduce the difficulty of vehicle orientation detection. In order to improve the performance of the random area connection network, this paper innovatively proposes raster convolution and designs a grid network based on raster convolution. Experiments were performed on the famous autopilot data set KITTI, achieving an average angular similarity of 89.12% and an average similarity of 87.32%. The experimental results show that compared with the traditional pose detection method, the proposed method has better robustness and detection effect.

关键词： Convolution Pose estimation Kernel Feature extraction Training machine learning Object detection

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Covid-19 and Diabetic Retinopathy (Dr) Detection Using Ai & Deep learning

Covid-19 and Diabetic Retinopathy (Dr) Detection Using Ai & ...

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international conference on Computation, Automation and Knowledge Management (ICCAKM)

作者： Abhishek Srivastava Gaurav Singh M.K Shukla Rana Majumdar Amity School of Engineering and Technology Amity University Uttar Pradesh Noida 201313 India Professor School of Engineering Sister Nivedita University Kolkata

ISBN: (数字)9781665453196

ISBN: (纸本)9781665453202

Artificial intelligence (AI), deep learning (DL), and neural networks (NN), though these words sound flashy and may leave you perplexed, represent powerful technologies that have the capabilities to transform the world. It is just now emerging how valuable these machine learning-based techniques are and how they can solve many real-world problems ranging from fraud detection, resource management to driver-less *** such field where the application of AI systems is progressively growing is in medical diagnosis. A lot of research is going on to enhance computer-aided diagnosis and detection of diseases. Recent world events have tested the healthcare systems all around the world. Suppose we have sophisticated deep learning systems (DLS) that could help in faster and efficient disease detection and diagnosis; how beneficial it would be to assist both medical professionals and *** study explores how AI and machine learning techniques could be used for disease detection, giving COVID-19 and Diabetic Retinopathy detection examples. We present two deep learning (DL) models, one to detect COVID-19 from chest x-ray image scans and the other to detect Diabetic Retinopathy at various stages of the disease from retinal fundus images. With reasonably high accuracy, >95% for the COVID-19 detection model and >80% for the Diabetic Retinopathy detection model, these results highlight AI and deep learning potential to assist general practitioners.

关键词： Deep learning COVID-19 Retinopathy Computational modeling Transforms Retina Diabetes

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Optimizing Deep learning application for Edge Computing

Optimizing Deep Learning Application for Edge Computing

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international conference on Embedded & Distributed Systems (EDiS)

作者： Smail Niar Université Polytechnique Hauts-de-France France

ISBN: (数字)9798350397543

ISBN: (纸本)9798350397550

eep learning (DL) models such as convolutional neural networks (CNN) are being deployed to solve various computer vision and natural language processing tasks at the edge. It is a challenge to find the right DL architecture that simultaneously meets the accuracy, power and performance budgets of such resource-constrained devices. Hardware-aware Neural Architecture Search (HW-NAS) has recently gained steam by automating the design of efficient DL models for a variety of target hardware ***, such algorithms require excessive computational resources. Thousands of GPU days are required to evaluate and explore modern DL architecture search space. In this talk I will present state-of-the-art approaches that are based on two components: a) Surrogate models to predict quickly architecture accuracy and hardware performances to speed up HW-NAS, b) Efficient search algorithm that explores only promising hardware and software regions of the search space.

关键词： computer architecture Hardware Computational modeling Task analysis Software algorithms Embedded systems Convolutional neural networks

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Sentiment Analysis and Topic Modeling of E-Grocery application Reviews Using Naive Bayes and Support Vector machine: A Case Study of Segari Data Review on the Google Play Store

Sentiment Analysis and Topic Modeling of E-Grocery Applicati...

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Electronic and Electrical Engineering and Intelligent System (ICE3IS), international conference on

作者： Jefka Dhammananda Indra Budi Aris Budi Santoso Faculty of Computer Science Universitas Indonesia Jakarta Indonesia

Segari is a customer-centric company with a core value of being obsessed with its customers. The lack of human resources and the abundance of customer reviews that need to be analyzed hinder the process of extracting information from these reviews. Therefore, a machine learning model is needed to automatically perform sentiment analysis and topic modeling. The information extracted from sentiment analysis can be used as a reference to maintain service quality based on positive sentiments, while the results of negative sentiments can be used for evaluation to improve Segari's services and application. The data used on this research were customer reviews from the Google Play Store. The model development process includes data collection, data labeling, data preprocessing, feature extraction, sentiment classification model, model evaluation, and topic modeling. The researcher utilized two classification algorithms, NB and SVM, on a total of 10,507 reviews. The data shows that 74.37% express positive sentiments, while 25.63% express negative sentiments. The results of the study indicate that SVM with oversampling achieved the best model performance, with a recall of 89.98%. Additionally, the researcher used LDA to identify topics related to customer perspectives on Segari, which will be communicated to the relevant team. The analysis revealed that some customers are satisfied while others are disappointed with the product delivery process, application, prices, promotions, and vouchers. Customers generally expressed satisfaction with the quality and freshness of the products. Some customers felt disappointed due to missing or incomplete items in their orders, also to customer service.

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Deep learning Based Event Detection Using Multilingual Tweets During Disaster

Deep Learning Based Event Detection Using Multilingual Tweet...

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Smart Generation Computing, Communication and Networking (SMART GENCON), international conference on

作者： C. Valliyammai D. Manikandan M. Bharathkumaran S. Gopi Agasthia P.S Logeswari Department of Computer Technology Madras Institute of Technology Campus Anna University Chennai India

Concerning the calamity, users typically provide data via social media in their native language, which is difficult to process and understand for others who are unfamiliar with that language. The proposed approach is intended to determine the location during a disaster using multilingual tweets. The study analysis with the tweets, extracted using # 2015 Chennai floods and # heavy rain in Tamil language. Multilingual tweets are trained using an auto-encoder model and then disaster-related tweets are grouped using the K-means clustering method. Integration of natural language processing and machine learning techniques helps to extract the location from the tweets. The trained model results in producing the encoded representation of tweets and the number of clusters is determined by the elbow method using K-mean clustering. IndicNER Fine-tuned model with 93% accuracy is used to identify the location entities of the Tamil Tweets extracted. DisMan application is designed to visualize the disaster location in the geographical map and it helps in quick response with the rescue team to identify the disaster-affected area.

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A New machine learning Algorithm for Users’ Movie Recommendation

A New Machine Learning Algorithm for Users’ Movie Recommend...

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CIBDA 2022;3rd international conference on computer Information and Big Data applications

作者： Zhongyuan Liu Xuefei Wang Hongzheng Zhu

ISBN: (纸本)9783800758760

In this information era, even a movie lover may feel overwhelmed when facing numerous movies on the movie website. Therefore, an efficient movie recommendation system is needed. In this paper, two different recommendation systems were compared, based on the user-based collaborative filtering and the content-based collaborative filtering respectively. Basically, several relevant studies were referred to, and a huge dataset was used from MovieLens. After pre-processing the data, where the data was transformed into matrixes of each user’s rating and the corresponding movie (the rating matrix represented the eigenvector of each user), cosine similarity was used to obtain the similarity relationship between users by calculating the cosine similarity between those eigenvectors. Then, K-NearestNeighbor (KNN) algorithm was used to select a certain number of users having the highest similarity with the target, based on which predicted how the target would rate a movie and recommended the movie with the highest predicted score to the target. In the part of using the content-based collaborative filtering, films in each category were ranked based on the average and the number of the scores and the user’s favorite movie types were obtained by the user-movie genre preference matrix based on the scores that users gave to each type of movies. The movies with the highest score in each target user’s favorite movie type would be recommended to the target. Experimental outcomes indicate that both algorithms generally meet the needs of users despite of the differences in the results.

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A Hybrid Model of GA-K Means-BP Neural Network for Financial Risk Warning

A Hybrid Model of GA-K Means-BP Neural Network for Financial...

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computer Science, Electronic Information Engineering and Intelligent Control Technology (CEI), IEEE international conference on

作者： Jin Wang Zhen Tang Sinounited Investment Group Corporation LIMITED POSTDOCTORAL PROGRAMME Beijing P.R. CHINA Agricultural Bank of China SuZhou P.R. CHINA

ISBN: (数字)9798350372052

ISBN: (纸本)9798350372069

With the continuous development of computer technology, artificial intelligence has penetrated into various fields and guided corresponding innovations. This also leads to increasing competition in various fields, requiring corresponding participants to engage in technological innovation. In this context, machine learning tools are often used to mine data in order to obtain the hidden patterns behind a large amount of complex data. BP neural network, as an important machine learning tool, has been used by many scholars for related research. In the field of enterprise financial risk research, BP neural networks can also be used for financial risk warning. Therefore, this paper focuses on the application of BP neural network in enterprise financial crisis warning. In the research, this paper constructs a financial crisis warning model based on the K-means clustering and BP neural network, and achieved good warning effects. To further improve the early warning effect of the model, this paper further introduces genetic algorithm to optimize the BP neural network, and constructs a hybrid model of GA-K-means-BP neural network to predict enterprise financial crises. Meanwhile, based on the hybrid model, the research shows that the early warning effect can be improved by introducing new enterprise characteristic indicators.

关键词： Technological innovation machine learning algorithms Computational modeling Neural networks machine learning Predictive models Prediction algorithms

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