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检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

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Design of Quantum machine learning Course for a computer Science Program 4

Design of Quantum Machine Learning Course for a Computer Sci...

引用

4th IEEE international conference on Quantum Computing and Engineering (QCE) / IEEE Quantum Week / 2nd IEEE Quantum Science and Engineering Education conference (QSEEC)

作者： Kumar, Sathish Adeniyi, Temitope Alomari, Ahmad Ganguly, Santanu Cleveland State Univ Cleveland OH 44115 USA Photon Inc London England

ISBN: (纸本)9798350343236

In this work, we present the design and plan of Quantum machine learning (QML) course in a computer science (CS) University program at senior undergraduate level / first year graduate level. Based on our survey, there is a lack of detailed design and assessment plan for the delivery of QML course. In this paper we have presented the QML course design with week by week details of QML concepts and hands on activities that are covered in the course. We also present how this QML course can be assessed from CS program learning outcomes perspective.

关键词： Quantum machine learning Quantum computing Course Assessment Course Design QML Workflow

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4th Crowd Science Workshop - CANDLE: Collaboration of Humans and learning Algorithms for Data Labeling 23

4th Crowd Science Workshop - CANDLE: Collaboration of Humans...

引用

16th international conference on Web Search and Data Mining

作者： Ustalov, Dmitry Savage, Saiph van Berkel, Niels Liu, Yang Toloka Belgrade Serbia Northeastern Univ Boston MA USA Aalborg Univ Aalborg Denmark Univ Calif Santa Cruz Santa Cruz CA USA

ISBN: (纸本)9781450394079

Crowdsourcing has been used to produce impactful and large-scale datasets for machine learning and Artificial Intelligence (AI), such as ImageNET, SuperGLUE, etc. Since the rise of crowdsourcing in early 2000s, the AI community has been studying its computational, system design, and data-centric aspects at various angles. We welcome the studies on developing and enhancing of crowdworker-centric tools, that offer task matching, requester assessment, instruction validation, among other topics. We are also interested in exploring methods that leverage the integration of crowdworkers to improve the recognition and performance of the machine learning models. thus, we invite studies that focus on shipping active learning techniques, methods for joint learning from noisy data and from crowds, novel approaches for crowd-computer interaction, repetitive task automation, and role separation between humans and machines. Moreover, we invite works on designing and applying such techniques in various domains, including e-commerce and medicine.

关键词： crowdsourcing data labeling human-in-the-loop human-machine collaboration fairness and inclusion

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the application of machine learning in Android Malware Detection 4

The Application of Machine Learning in Android Malware Detec...

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4th international conference on Neural Networks, Information and Communication Engineering, NNICE 2024

作者： Xie, Wei Zhang, Xueying Rdfz Chaoyang Branch School Beijing China

ISBN: (纸本)9798350394375

the android system had been a popular system among mobile phone users, there are lots of applications based on android system, which contain malware and good software. Malware is very harmful to the computer because it can embed rubbish advertisements, applications and viruses in order to disrupt the functioning of your mobile phone or steal your personal information. Network information security has gained its significance on the internet which is related to our safety directly. the traditional method to detect malware is to use anti-virus software to check the mobile phone. However, with the rapid uploading and increase in the number of software, some malware cannot be detected well and also the anti-virus software may embed some advertisements or download bundle software (usually hard to uninstall) automatically by itself. With development, machine learning 's calculation efficiency has been improved significantly and it has been used in many regions. In this paper, we select several typical machine-learning algorithms to build the malware detection model. Considering the unbalanced data, we made an unweighted and weighted model simultaneously. As a result, the weighted random forest model has the best effect and its accuracy is 98.94% © 2024 IEEE.

关键词： machine learning

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Implementing IoT and Deep learning-Based Educational Systems for Assessing Practical learning with Cloud Computing applications 4

Implementing IoT and Deep Learning-Based Educational Systems...

引用

4th international conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： thakur, Hardeo Kumar Bafna, Minal Singh, Jagendra Naresh, Usikela Biswas, Susmi Kiran, Saggurthi School of Computer Science Engineering and Technology Bennett University Greater Noida India Agrawal Pg College Department of Physics Jaipur India Rangareddy India Haldia Institute of Management Makaut Department of Management Science West Bengal India Hyderabad India

ISBN: (纸本)9798350387490

In this study, the practical application of technologies related to IoT and machine learning in the robotics education context is examined based on the use of a robot with two proximity sensors. the trial system was designed to leverage the real-time sensor data and monitor and optimize student interactions with the robot in the course of performing such tasks as object manipulation and palletization. the sensor data is used to critically track the accuracy of student actions and help mentors provide feedback on the issue. the machine learning models used in the study, which are Recurrent Neural Network (RNN), K-Nearest Neighbors (KNN), Support Vector machine (SVM), and Long Short-Term Memory (LSTM), are applied to analyze the sensor data and predict and classify the student's actions so that corrective measures can be taken in time. the RNN model has the highest accuracy and efficiency for the task, as it reaches the level of 96.7% compared to 80% for KNN, 71% for SVM, and 75% for LSTM. this advantage is explained by the capability to model temporal dependencies or otherwise capture with its memory the sequences present in the sensor data. the data is transmitted securely and efficiently to the cloud platforms and back due to the implementation of the IoT, which also helps to deploy the model more effectively. Overall, it is concluded that the integration of IoT and machine learning is beneficial and transformative in educational contexts as it provides students with personalized real-time feedback. In this way, the level of proficiency of the students in dealing with robotic and IoT-based systems is significantly enhanced, preparing them to successfully address the future challenges of industry and academia in the sphere. © 2024 IEEE.

关键词： Proximity sensors

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Survey on Visual Speech Recognition using Deep learning Techniques 4

Survey on Visual Speech Recognition using Deep Learning Tech...

引用

4th international conference on Communication Systems, Computing and IT applications, CSCITA 2023

作者： Chand, Ritika Jain, Pushpit Mathur, Abhinav Raj, Shiwansh Kanikar, Prashasti Department of Computer Engineering SVKM's NMIMS Mukesh Patel School of Technology Management and Engineering Mumbai India

ISBN: (纸本)9781665459877

Lip Reading has evolved and from where it began to help deaf people has slowly turned into a service where in the Digital Entertainment industry has started utilizing it. With the recent rise of AI, automated technologies have touched the boundaries of Lip Reading as well. Various Algorithms have been devised using Neural Network Methodologies. We observe that a lot of the algorithms reviewed, have been exploring various techniques whether it be a variation from detecting lip features to the text generation process *** the amount of research done in the field, one can always look out towards a better & optimized lip detection. the study emphasizes more towards looking at the utilization of the machine learning & Deep learning technologies and thus provides a vivid view at the bigger picture of the interpolation of AI in the Visual based Lip Reading domain. © 2023 IEEE.

关键词： Deep learning

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Investigating the Effectiveness of machine learning Techniques for Real Estate Price Prediction 4

Investigating the Effectiveness of Machine Learning Techniqu...

引用

4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Singh, Baljap Singh, Jaspreet Kumar, Anubhav Gupta, Shikha Chandigarh University Computer Science and Engineering Mohali India

ISBN: (纸本)9798350300888

In the real-estate industry, the capacity to extract significant information from raw data makes it extremely valuable for predicting home values, housing attributes, and much more. the cost of housing varies from day to day and is frequently increased arbitrarily rather than using calculations. the housing market and homeowners are routinely impacted by changes in property values. A literature study is done to evaluate the most important factors and reliable models for predicting housing values. the results of this analysis confirmed the use of Support Vector Regression, Linear Regression, and Artificial Neural Networks as the most effective models when compared to others. Furthermore, our data imply that geographical and real estate agents are important determinants in forecasting property values. this research will be very helpful in determining the most important variables for estimating housing costs and the best machine learning model to employ when doing this type of research, especially for academics and housing developers. © 2023 IEEE.

关键词： Housing

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4th international conference on Intelligent Engineering and Management, ICIEM 2023

4th International Conference on Intelligent Engineering and ...

引用

4th international conference on Intelligent Engineering and Management, ICIEM 2023

ISBN: (纸本)9798350341126

the proceedings contain 68 papers. the topics discussed include: app based implementation of modern agriculture utilities for farmers;application of intelligent computing (IC) in human resources (HR) analytics: towards better tomorrow;artificial intelligence based chatbot for promoting equality in high school advising;assessment of human activity recognition based on impact of feature extraction prediction accuracy;biometric and RFID passive tag based student identification system for secure attendance management;blockchain and federated learning enabled smart traffic management system for smart cities;climatic temperature forecasting with regression approach;covid-19 detection on X-ray image using deep learning;critical review on applications of self-healing concrete in reinforced concrete structures;decipherable for artificial intelligence in Medicare: a review;dementia identification using machine learning algorithms: comparative analysis;and design and implementation of an easy-to-use tracking device for logistic applications.

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Analysis of machine learning Algorithms for Classification of Osteosarcoma 4

Analysis of Machine Learning Algorithms for Classification o...

引用

4th IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Deepa, B.G. Ayshwarya, A. Lokesh, C.K. Pooja, N.G. Christ University Department of Computer Science Bangalore India Kristu Jayanti College Department of Computer Science Bangalore India School of Computer Science and Application Reva University Bangalore India

ISBN: (数字)9798331528348

ISBN: (纸本)9798350352931

the exploration of histopathological osteosarcoma data is a profitable technique to observe the pathological textures of osteosarcoma. Osteosarcoma classification is complex and inefficient due to inter-class similarities and variations. In this research, we employ two ML methods to classify OST: RF alias Random Forest and SVM alias Support Vector machine. Sensitivity, Accuracy, F1-score, AUC, Kappa, and Specificity are the performance metrics used in the evaluation of ML models. Based on these classifiers, osteosarcoma can be classified as three categories which are explained in this work. the overall accuracy of the RF and SVM are 94.70% and 93.2% respectively. RF algorithm outperforms SVM algorithm with specificity 98.5%, F1 score 93.60%, sensitivity 92.30%, overall accuracy 94.70%, AUC 0.99 and Kappa 0.92. © 2024 IEEE.

关键词： Support vector machines

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proceedings - 2023 4th international conference on Information Systems and Software Technologies, ICI2ST 2023

Proceedings - 2023 4th International Conference on Informati...

引用

4th international conference on Information Systems and Software Technologies, ICI2ST 2023

ISBN: (纸本)9798350373219

the proceedings contain 17 papers. the topics discussed include: artificial neural networks in non-small cell lung cancer detection using computed tomography medical imaging;recommendations for the development of video games to reduce stress in young people;neural network architectures comparison for atrial fibrillation detection;real-time web monitoring of shoulder rehabilitation exercises using artificial vision techniques;HELP! It's up to YOU to DONATE - a structured literature review on conversational agents and promoting donation behavior;GigMaleBPMN: generation of graphical components from the BPMN model using machine learning;machine learning and balanced techniques for diabetes prediction;and a topic modeling approach to analyze teaching innovation projects.

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machine learning Methods for the Timely Identification of Autism Spectrum Disorder in Toddlers 4

Machine Learning Methods for the Timely Identification of Au...

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4th international conference on Emerging Systems and Intelligent Computing, ESIC 2024

作者： Jain, Sweta Tripathy, Hrudaya Kumar Shri Ramdeobaba College of Engineering and Management Department of Computer Science and Engineering Nagpur India Kalinga Institute of Industrial Technology Deemed to Be University School of Computer Engineering Odisha Bhubaneswar751024 India

ISBN: (纸本)9798350349856

Autism Spectrum Disorder (ASD) is an evolving ailment that upsets the nervous system, by means of a widespread range of symptoms and severity levels. Early ASD identification is challenging and there is no treatment, early detection and intervention are vital for improving outcomes in individuals with ASD. this research, explored the application of machine learning (ML) techniques, specifically Support Vector machines (SVM), Random Forest (RF), and Gradient Boosting (GB), for detection of ASD using a dataset focused on screening toddlers. the dataset, obtained from Kaggle in the 2018 release, encompasses demographic information, medical history, and screening test results for toddlers. the presented system employs leading edge algorithms to analyze the dataset and build predictive models for ASD detection. SVM is preferred due to its capability to lever multifaceted associations in high-dimensional data, while RF excels in handling diverse feature sets and capturing ***, known for its ensemble learning capabilities, is also employed to boost predictive performance. the assessment of these models is accomplished via bench mark estimation metrics in ML. this approach aims at an effective model for ASD detection in children based on the characteristics of the dataset. Additionally, feature importance analysis is conducted to gain insights into the factors contributing to ASD prediction. the implementation results reveals that SVM algorithm outperforms in the proposed ASD diagnosis. Early identification using ML techniques could assist healthcare professionals and caretakers in timely intervention, ultimately improving the prolonged effects for persons with ASD. © 2024 IEEE.

关键词： Support vector machines

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