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检索条件"任意字段=2023 International Conference on Computer Engineering and Distance Learning, CEDL 2023"

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Differentiate Music Genre from an Audio File Using CNN

Differentiate Music Genre from an Audio File Using CNN

引用

2023 international conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Raja Rajeswari, S. Dhanalakshmi, R. School of Computer Science and Engineering Vellore Institute of Technology Chennai600127 India Ctr. for Cyber Phys. Syst. Sch. of Comp. Science and Engineering Vellore Institute of Technology Chennai600127 India

ISBN: (纸本)9798350315127

The complexity of music production has gradually decreased recently, causing a big number of individuals to produce music as a result and submitting it to streaming media. Because there are so many music streaming services, people are putting in a lot of effort looking for certain songs. The result says, it's critical to be able to swiftly classify musical genres in today's culture. As deep learning and machine learning technologies progress, Convolutional Neural Networks (CNN) are indeed being applied in a number of disciplines, and new CNN-based versions have appeared one after the other. Manually extracting features from time-series data is required for traditional music genre classification. Applying deep learning to time-series data has proven to be useful and efficient. To save time for the user when looking for different styles of genres. Librosa is used to convert into a corresponding Mel spectrum. Converted Mel send to a CNN model for training. © 2023 IEEE.

关键词： Convolutional neural networks

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Implementation of Science learning Application Two Seasons Weather Change Material Based of multimedia For Deaf Students

Implementation of Science Learning Application Two Seasons W...

引用

2023 international conference on Informatics engineering, Science and Technology, INCITEST 2023

作者： Effendi, Diana Dinasti, Sri Ela Noviansyah, Beri Cancerina, Delviola Universitas Komputer Indonesia Department of Informatics Management Bandung Indonesia Universitas Komputer Indonesia Department of Information System Bandung Indonesia Politeknik Negeri Bandung Department of Informatics Engineering Bandung Indonesia

ISBN: (纸本)9798350317862

This research is a continuation of previous research, namely the Design of Multimedia Based Two Season Weather Change Science learning Application For Deaf Students Part B for Deaf. This study will discuss how to implement programs that have been made into applications that can be used by user with prototype approach. In addition, a measurement of user satisfaction with the application was carried out. To measure user satisfaction with the application, descriptive and quantitative methods were used, using a questionnaire distributed to seven respondents, namely six students of grade VI and one science teacher at SLBN Cicendo Bandung, with seven questions. From the results of the questionnaire distribution, the satisfaction level of the respondents was 95%, which means that the user is satisfied with the multimedia application, where this application can help in the process of teaching science, especially the material of two seasons weather change. The results of this research contribute to how the learning model is more interesting for deaf students and makes it easier for teachers to explain the material compared to conventional teaching methods. © 2023 IEEE.

关键词： Students

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MOVING FORWARD BY MOVING BACKWARD: EMBEDDING ACTION IMPACT OVER ACTION SEMANTICS 11

MOVING FORWARD BY MOVING BACKWARD: EMBEDDING ACTION IMPACT O...

引用

11th international conference on learning Representations, ICLR 2023

作者： Zeng, Kuo-Hao Weihs, Luca Mottaghi, Roozbeh Farhadi, Ali Paul G. Allen School of Computer Science & Engineering University of Washington United States PRIOR @ Allen Institute for AI United States

A common assumption when training embodied agents is that the impact of taking an action is stable;for instance, executing the "move ahead" action will always move the agent forward by a fixed distance, perhaps with some small amount of actuator-induced noise. This assumption is limiting;an agent may encounter settings that dramatically alter the impact of actions: a move ahead action on a wet floor may send the agent twice as far as it expects and using the same action with a broken wheel might transform the expected translation into a rotation. Instead of relying that the impact of an action stably reflects its pre-defined semantic meaning, we propose to model the impact of actions on-the-fly using latent embeddings. By combining these latent action embeddings with a novel, transformer-based, policy head, we design an Action Adaptive Policy (AAP). We evaluate our AAP on two challenging visual navigation tasks in the AI2-THOR and Habitat environments and show that our AAP is highly performant even when faced, at inference-time with missing actions and, previously unseen, perturbed action space. Moreover, we observe significant improvement in robustness against these actions when evaluating in real-world scenarios. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

关键词： Embeddings

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A Systematic Review for Cardiovascular Disease Detection using Machine learning Methods

A Systematic Review for Cardiovascular Disease Detection usi...

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2023 international conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2023

作者： Rao, Gorapalli Srinivasa Muneeswari, G. VIT-AP University School of Computer Science and Engineering Andhra Pradesh Amaravathi India

ISBN: (纸本)9798350344387

The most important part of providing health care is diagnosing diseases. If a disease is diagnosed earlier than usual or anticipated, it may save a person's life. Methods based on machine learning can assist the medical field by providing accurate and immediate disease diagnoses. The difficulty of diagnosing heart disease makes it one of the most hazardous diseases in the world today. In healthcare contexts, ML is a powerful technology that can swiftly and correctly predict outcomes. After a brief explanation of machine learning, we present synopses of the very common methods for HD diagnosis. This study provides a comprehensive overview of heart disease, the risk factors associated with it, and a brief discussion of the numerous techniques employed to automatically detect heart disease. The detailed information on certain machine learning-based methodologies and investigates the methods for heart disease diagnosis that have been mentioned in the literature throughout the previous five years (2018-2023). There is also mention of a comparison chart. However, among the possibilities mentioned, the classification of cardiac disease has recently drawn increasing attention as a useful and significant alternative. © 2023 IEEE.

关键词： Classification Heart Disease Machine learning Optimizers XG-Boost

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A Qualitative Study on Developing IT Strategies for The Effective Remote Work Using IT Balanced Scorecard

A Qualitative Study on Developing IT Strategies for The Effe...

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2023 international conference on Networking, Electrical engineering, computer Science, and Technology, IConNECT 2023

作者： Chudra, Glenny Yohannis, Alfa Pradita University Departerment of Informatics Tangerang Indonesia University of York Daparterment of Computer Science York United Kingdom

ISBN: (纸本)9798350331172

This Remote work has become increasingly prevalent among many companies, even as the COVID-19 pandemic gradually recedes. However, effective planning is crucial for ensuring the successful implementation of remote work in the long run. One practical approach to remote work planning involves using an IT Balanced Scorecard. The IT Balanced Scorecard serves as a framework that enables organizations to align their overall business objectives and actions with their IT strategies, focusing on four distinct perspectives: customer, financial, internal processes, and learning and growth. In this research conducted a qualitative study and identified the advantages and disadvantages of working from the office and remote work. We also gathered insights on the technological tools, essential qualities required for effective remote work, and etiquette guidelines to foster healthy workplace interactions - an important consideration for organizations. Based on our findings and leveraging the IT Balanced Scorecard, we have developed a set of objectives and activities to mitigate the disadvantages associated with remote working. By employing an IT Strategy developed through the four perspectives of the Balanced Scorecard - namely customer, finance, internal processes, and learning & growth. it is anticipated that this IT Strategy can assist organizations in effectively initiating and optimizing remote work implementation which organizations could refer to when seeking to optimize their remote work arrangements. © 2023 IEEE.

关键词： IT Balanced Scorecard IT Strategy remote working work-from-office

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Malware Prediction and Classification for Android Applications Using Machine learning Techniques

Malware Prediction and Classification for Android Applicatio...

引用

2023 international conference on computer Communication and Informatics, ICCCI 2023

作者： Kalpana, T. Kongu Engineering College Department of Computer Applications Erode India

ISBN: (纸本)9798350348217

Numerous paid and open-source applications have been produced as a result of the increased use of Mobile phones. These programs enable users to perform a wide range of tasks, including as communicating, playing games, and carrying out financial and educational tasks. Malicious app growth has become a significant problem that is holding back the development of a healthy mobile ecosystem. According to recent studies, new dangerous Android apps are released every 10 seconds. Users of the Android operating system can download and install software from unreliable websites including file-sharing websites and third-party app stores. The issue of malware infiltration has become so serious and significant that, according to recent statistics, 98% of mobile malware targeted Android smart phones. In this project we using SVM, KNN and CNN algorithms. © 2023 IEEE.

关键词： Android (operating system)

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Deep learning Approach to Enhance IDS Accuracy using Hybrid LSTM Algorithm 14

Deep Learning Approach to Enhance IDS Accuracy using Hybrid ...

引用

14th international conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Sehrawat, Sahil Singh, Dinesh Dcrust Murthal Department of Computer Science and Engineering Sonipat India

ISBN: (纸本)9798350335095

In this research, the focus is on safeguarding networks and sensitive data from cyber threats. The study utilizes the UNSW-NB15 dataset to test the effectiveness of machine learning models, specifically BiLSTM models, in creating a reliable intrusion detection system. The dataset provides a more up-to-date and accurate representation of modern cyber attacks. The BiLSTM model, which considers both forward and backward dependencies in sequential data, is employed for detecting and classifying intrusions. The experimental results indicate a promising accuracy of 96.7%, as evaluated using accuracy, recall, and F1-score assessment criteria. This study contributes to the field of intrusion detection by highlighting the potential of deep learning techniques, particularly BiLSTM models, in achieving high accuracy and reliability, thereby enhancing network security and protecting sensitive information. © 2023 IEEE.

关键词： Statistical tests

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A Deep Reinforcement learning Perspective on Adaptive Federated Dropout 20

A Deep Reinforcement Learning Perspective on Adaptive Federa...

引用

20th IEEE international conference on Mobile Ad Hoc and Smart Systems, MASS 2023

作者： Sun, Tonghui Huang, Yan Xie, Zhenzhen Pang, Junjie Chen, Xingyun Cai, Zhipeng Qingdao University College of Computer Science and Technology Qingdao266000 China Kennesaw State University College of Computing and Software Engineering AtlantaGA United States Shandong University School of Computer Science and Technology Qingdao266237 China Georgia State University Department of Computer Science AtlantaGA United States

ISBN: (纸本)9798350324334

Federated learning is a distributed learning solution that achieves high-quality machine learning models while ensuring privacy and collaboration among various end devices. However, different kinds of end devices can lead to an unstable training process due to limited and dynamic communication and computation resources. Federated Dropout is an important technique for mitigating such resource bottlenecks by randomly removing a fixed percentage of activations of model components. However, most of the existing FL dropout methods cannot effectively utilize the distinguished characteristics of different clients or fail to automatically adapt to end device heterogeneity. In this paper, we thus propose Adaptive Federated Dropout with Reinforcement learning (AFD-RL). AFD-RL employs reinforcement learning technology to adapt to the clients' unbalanced computation and communication resources and provide a personalized dropout strategy for each client. By automatically selecting suitable model components for each client in each communication round, AFD-RL achieves better accuracy with fewer communication rounds. Extensive experiments validate the effectiveness of the proposed approach in improving training efficiency and inference accuracy. © 2023 IEEE.

关键词： Reinforcement learning

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Harnessing CNNs for Accurate Skin Disease Identification 3

Harnessing CNNs for Accurate Skin Disease Identification

引用

3rd international conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Dhoundiyal, Parveen Sharma, Vikrant Vats, Satvik Singh, Karan Graphic Era Hill University Department of Computer Science & Engineering Uttarakhand Dehradun India School of Computer and System Sciences Jnu New Delhi New Delhi India

ISBN: (纸本)9798350342338

Skin disorders are common and can be brought on by several things, including viruses, bacteria, allergies, or fungi. The speed and precision of detecting skin diseases have increased because of developments in laser and photonics-based medical technologies. The expense of such a diagnostic is still considerable, though, which restricts accessibility. An automated early-stage dermatological screening system has been developed using image processing techniques to solve this issue. Effective skin disease classification requires the extraction of key information from pictures, and many detection algorithms rely on computer vision. DenseNet169 and NASNetMobile are some of the methods that were employed in the study. Graphs were used to display the results as the researchers compared the algorithms' performance based on training accuracy and confusion matrix. Keywords: Skin Disease, Medical Diagnosis, Deep learning, Transfer learning, CNNs © 2023 IEEE.

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Forecasting Crime Rate Using Artificial Intelligence Applications

Forecasting Crime Rate Using Artificial Intelligence Applica...

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2023 international conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Srivastava, Rijul Kumar Gupta, Anuj Sharma, Gaurav Chandigarh University Department of Mechatronics Engineering Mohali India Chandigarh University Department of Electronics and Communication Engineering Mohali India

ISBN: (纸本)9798350313987

Crime is a ubiquitous and serious issue in modern society, with a plethora of crimes perpetrated on a daily basis, producing widespread unhappiness. Prevention of Crime is a major critical duty nowadays. Artificial intelligence has recently been an important role that is being noticed practically in every industry, Crime Prediction is also included in that. Comprehensive database has been kept for prior crimes, as this information can be used for future reference. Predicting the chances of future crimes can assist law enforcement organizations in preventing them before they cause the major happening by giving strategic and crucial information based on variables such as time, location and distance. However, the growth rate is considered on the basis of crimes are committed, precisely anticipating crime is a difficult undertaking. As a result, examining good crime rate prediction and analyzing or studying methodologies is critical in detecting and reducing the future crime rate. Multiple academics have introduced studies to predict crimes using various machine learning algorithms and input parameters such as random forest and decision trees. The ultimate goal is to demonstrate the value, need and efficacy of machine learning in predicting violent crimes in a particular region, so that police can use it to lower crime rates and increase societal well-being. © 2023 IEEE.

关键词： Crime Forecasting Machine learning Random Forest Classification

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