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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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Exploring the Synergy of machine learning and Blockchain in The Education Sector

Exploring the Synergy of Machine Learning and Blockchain in ...

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

作者： T. Swathi P. Radhika Raj P. Srilakshmi M. Padma Department of CSE G. Pulla Reddy Engineering College Kurnool Andhra Pradesh India Department of CSE Jawaharlal Nehru Technological University Anantapur (JNTUA) Ananthapuramu India Department of CSE G. Pulla Reddy Engineering College Kurnool computer applications GDC Srisailamproject Sundipenta Nandyal district

The advent of blockchain and machine learning technologies has introduced revolutionary possibilities across various industries, and the education sector is no exception. Blockchain, as a decentralized and immutable distributed ledger, has the potential to transform the traditional education system by addressing some of its inherent challenges. This paper explores the application of blockchain and machine learning in the education system and its potential benefits

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Arabic Cyberbullying Detection from Imbalanced Dataset Using machine learning 3rd

Arabic Cyberbullying Detection from Imbalanced Dataset Using...

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3rd international conference on Soft Computing and its Engineering applications, icSoftComp 2021

作者： AlFarah, Meshari Essa Kamel, Ibrahim Al Aghbari, Zaher Mouheb, Djedjiga University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9783031057663

In recent years, the number of online social networks users is dramatically increased. Cyberbullying is a serious threat in online social networks especially toward children and teenagers. Victims are harassed by perpetrators even with no physical attendance. Cyberbullying causes psychological damage to victims resulting in anxiety, depression, and even suicide. Cyberbullying is language and culture sensitive. Many prior works in cyberbullying detection was conducted in English and only few papers studied Arabic cyberbullying detection. In this research, Arabic cyberbullying detection using five machine learning techniques were studied using real Arabic messages from Twitter and YouTube. Many performance techniques were used to evaluate the robustness of the model. Naïve Bayes (NB) achieves the highest Receiver Operating Characteristic (ROC) curve (AUC) of 89% while Support Vector machine (SVM) and Logistic Regression (LR) both achieve 88%. © 2022, Springer Nature Switzerland AG.

关键词： Social networking (online)

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A New machine learning Algorithm for Users' Movie Recommendation 3

A New Machine Learning Algorithm for Users' Movie Recommenda...

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

作者： Liu, Zhongyuan Wang, Xuefei Zhu, Hongzheng Faculty of rail transit Wuxi University Wuxi214105 China Shanghai Fuxing Senior High School Shanghai200434 China College of computer and information science Faculty of automation Southwest University Chongqing400700 China

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. © VDE VERLAG GMBH - Berlin - Offenbach.

关键词： Collaborative filtering

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machine learning Framework for Identification of Abnormal EEG Signal 3rd

Machine Learning Framework for Identification of Abnormal EE...

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3rd EAI international conference on Cognitive Computing and Cyber Physical Systems, IC4S 2022

作者： Rao, A. Prabhakara Bhaskar, J. Kumar, G. Prasanna Department of ECE Vishnu Institute of Technology AP Bhimavaram India

ISBN: (纸本)9783031289743

Epilepsy is a chronic, noncommunicable disease (NCD) causing disorder in the brain’s neurological activity. It may be due to genetic disorder or brain injuries caused by some accidents. This may cause seizures, loss of awareness, unusual sensations and behavior. Globally 50 million people are suffering from epilepsy, so it is one of the most prominent neurological diseases globally according to World Health Organization (WHO) statistics in 2021. It is estimated that up to 70% of people are suffering with epilepsy and they can be saved by timely diagnosis and proper treatment of epilepsy. Electroencephalograms (EEGs) are universally used to detect this chronic non-communicable disease. Furthermore, assessing a specific type of abnormality by visual examination of an EEG signal is an intuitive process that can vary from radiologist to radiologist. It is a challenging task for the radiologists to visually examine the EEG signal by looking for a shift in frequency or amplitude in long-duration signals. It may give rise to inaccurate categorization. Identification of epileptic seizure from the recorded EEG signal is a primary task in the treatment of epilepsy. In this work, wavelets were used to obtain the appropriate features from EEG signals. These features were fed to different classifiers. This work proposes a machine learning (ML) framework to detect the abnormality in the EEG signal automatically to assist the radiologists in their diagnosis. The ML framework uses 7 classifiers (KNN, SVM, Random Forest, Logistic Regression, Decision Tree, AdaBoost, and Bagging). Among these classifiers, Bagging Classifier was shown better performance in terms of accuracy and ROC. © 2023, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Electroencephalography

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Advanced Object Detection Algorithms and Its application Based on Deep learning 3

Advanced Object Detection Algorithms and Its Application Bas...

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3rd international conference on machine learning, Big Data and Business Intelligence (MLBDBI)

作者： Ma, Bohan Zheng, Chengyu Zheng, Zhuowen Henan Normal Univ Xinxiang Henan Peoples R China Ohio State Univ Columbus OH 43210 USA Harbin Inst Technol Shenzhen Shenzhen Peoples R China

ISBN: (纸本)9781665417907

Object detection is one of the most basic and challenging problems in such a field, which has become a paramount topic for scholars in recent years. In the past two decades, object detection has developed rapidly from the beginning to the application in all aspects of life, with the improvement both in detection accuracy and detection speed. In this paper, firstly, we focus on the research progress of object detection algorithm based on deep learning in the light of its technical evolution and application. Secondly, we compare and analyze the two-stage and single-stage detection framework from series algorithms based on R-CNN to series algorithms based on Yolo, and introduce the common data sets and index evaluation as well as the application process of the algorithm in the detection fields of pedestrian, face, text, medical image, sign language, etc. Finally, we predict the prospects of deep learning-based object detection algorithms according to the existing challenges.

关键词： deep learning object detection computer vision algorithm

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The Effect of Programming Language in Software Bug Prediction 3

The Effect of Programming Language in Software Bug Predictio...

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3rd international conference on Communication, Computing and Industry 4.0, C2I4 2022

作者： Jayadev, Gopika Gayathri, Kaparthi Babu, Tina Gandiboina, Harika Sudha, Kavya Venkteswalru, Bondu School of Engineering Dayananda Sagar University Department of AI and ML Bengaluru India School of Engineering Dayananda Sagar University Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9781665474979

Software maintenance is a very important phase in the life cycle of software development. As part of maintenance, we need to identify bugs within the code and fix them for every release. Software bug prediction (SBP) allows us to identify modules in the software that may have the tendency to be buggy. This enables us to perform targeted testing and properly plan maintenance cycles. In most research performed, we observed that dataset of software programs written in C language were used. Each programming language are inherently different and has it's own constructs, practices and nuances. In this research we focus on identifying if there exists a specific classifier that works well for each programming language which in turn is used to analyze the effect of programming language on Software Bug Prediction. Datasets of software written in C, C++ and Java has been collected and the most accurate classifier for each dataset of a programming language has been identified. The ML models used in this paper include Naive Bayes, Decision Tree and Random Forest. © 2022 IEEE.

关键词： Decision Tree machine learning Naïve Bayes Random Forest Software Bug Prediction

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IoT-based Battery Management System by Deploying an machine learning Model 3

IoT-based Battery Management System by Deploying an Machine ...

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3rd international conference on Smart Electronics and Communication, ICOSEC 2022

作者： Prabha, A. Saravanan, S. Balaji, S. Malini, V.K. Thopate, Kaushalya Sivakami, T. Ramkumar, M.Siva Sa Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai600077 India Psna College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Dindigul624622 India Kcg College of Technology Department of Electrical and Electronics Engineering Tamil Nadu Chennai600097 India Sri Sairam College of Engineering Department of Electrical and Electronics Engineering Bengaluru562106 India Vishwakarma Institute of Technology Department of Computer Engineering Maharashtra Pune411037 India Bharath Institute of Higher Education and Research Department of Electronics and Communication Tamil Nadu Chennai600126 India Karpagam Academy of Higher Education Department of Eee India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

A battery is a device that can be used to provide power for a variety of purposes. Regardless of the battery's size, the SoC is an important consideration. The abbreviation SoC stands for 'State of Charge."It refers to the battery's remaining capacity. SoC is used in both main and secondary batteries. However, accurately calculating the SoC necessitates a high level of skill. This research aims to solve this problem by developing an Artificial Intelligence (AI) model that can predict the SoC of *** construct this battery management system, Mendeley data was used to obtain a fundamental dataset encompassing all vital properties that compose a battery. An AI model is built using the Artificial Neural Network (ANN) architecture. This model is then trained using the data from the dataset. The model had the highest accuracy and a very low loss value at the end of the training and validation;thus, it could be ignored. After validation, the model's accuracy is greater than 0.98 and the loss value is less than 0.05, indicating that it is very effective at predicting the battery's SoC *** model is then implemented into a software application to make it more user-friendly for common people. The user can enter information about the battery, such as the voltage, current, and temperature values, using the application's straightforward user interface. The application shows the battery's SoC as well as the actual SoC for comparative purposes based on the numbers entered. This application reduces the need for an electrician for simple tasks like the prediction of the SoC of the battery. © 2022 IEEE.

关键词： Forecasting

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Research on fast extraction algorithm of big data information based on machine learning 22

Research on fast extraction algorithm of big data informatio...

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3rd Asia-Pacific conference on Image Processing, Electronics and computers, IPEC 2022

作者： Wu, Wenchen Yang, Xuliang Guangzhou Institute of Applied Science and Technology Guangdong Guangzhou511300 China Dongguan City College Guangdong Dongguan523000 China

ISBN: (纸本)9781450395786

With the development of the information industry, people are faced with an increasing amount of data. In order to obtain the intrinsic relationship and implicit information from these large-scale data, data mining as an important method has been paid more and more attention by people, and cluster analysis is an important research direction of data mining. In the era of big data, massive multi-modal data exists widely. How to mine the huge hidden value of data through complementary learning between modal data is the main problem of big data research at this stage, and it is also the task of big data and traditional data learning. Mixed attribute datasets are the most common type of datasets in the real world, especially in commercial financial databases, but there are very few clustering algorithms suitable for such datasets. The method used in this paper is machine learning, which enables computers to simulate human learning behavior, automatically acquire knowledge and skills through learning, continuously improve performance, and achieve self-improvement. © 2022 ACM.

关键词： Big data

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A Real-Time Virtual Yoga Assistant Using machine learning

A Real-Time Virtual Yoga Assistant Using Machine Learning

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Smart Data Intelligence (ICSMDI), international conference on

作者： Aruna Chittineni Yaswanth Sai Kotagiri Mohit Kolli Teja Kollipara John Raju Modepalli Sravan Kumar Namburi COMPUTER SCIENCE & ENGINEERING KKR & KSR INSTITUTE OF TECHNOLOGY AND SCIENCES Guntur India

Yoga is a practice that aims to develop an all-around personality by synchronizing the mind, body, and spirit. However, incorrect postures or techniques can cause damage. In ancient times, yoga was performed under the supervision of a teacher, but it is difficult to find a competent guru in today's fast-paced world. The goal of this project is to develop an application that can track and evaluate physical exercise, specifically yoga, through the use of human pose estimation. This application, called “A Real-Time Virtual Yoga Assistant,” uses machine learning methodologies to classify data on yoga positions in both pre-recorded and real-time videos. The research also examines various pose estimation and key point detection approaches and deep learning models used for posture classification.

关键词： Deep learning computer vision Pose estimation Medical services Forestry Real-time systems Indexes

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Neuroinformatics Deep learning Synthesizer Based on Impulse Control Disorder Using LSTM Cells 1

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3rd international conference on Innovations in Computational Intelligence and computer Vision, ICICV 2022

作者： Maheshwari, Vipul Soni, Akshat Sharma, Abhishek Department of Computer and Communication Engineering The LNM Institute of Information Technology Rajasthan Jaipur302031 India Department of Electronics and Communication Engineering The LNM Institute of Information Technology Rajasthan Jaipur302031 India

ISBN: (数字)9789819926022

ISBN: (纸本)9789819926015

computer-aided detection and diagnosis (CAD) systems are increasingly being used as an aid by clinicians for detection and interpretation of diseases medical analysis and identification. Given a person who stays in a remote area far from a healthcare facility, or doesn’t have the financial means to pay their clinic bill, or don’t have the time to take sick leave from their jobs. In such a case, disease prediction using excessive-cease state-of-the-art equipment can be really beneficial especially when it comes to decision-making. Two distinct research findings are also addressed to highlight the need of having a thorough understanding of procedures when diagnosing a condition. Deep learning requires the use of large neural networks with densely interconnected, each of which can adjust its hyper-parameters in response to incoming input. It is because of this technology that computer architectures are enabled to examine things without the need for particular programming from humans. Here are the most recent tendencies and breakthroughs in the deep learning of the field in this lookup, which might have a huge impact on the effective identification and diagnosis of a variety of ailments. AIM—The aim is to look into the use of deep learning in the accurate evaluation of positive disease risk indicators, with the goal of supporting health workers in their decision-making (Martini et al. in J Neural Trans, 2017 [1]). This big sample included participants in two distinct fMRI sessions, each of which included behavioral assessment for an hour and an hour-long scan within the same day. If participants had passed all prior testing sessions and did not also meet the following additional exclusion criteria, like any mood-altering medications on the day of the scan, a history of serious medical conditions, and pregnancy-related MRI contraindications, participants were recruited from the parent study to take part in the fMRI section. A deep learning model application in the field

关键词： computer architecture

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