2022 IEEE 1st International Conference on Data, Decision and Systems (ICDDS)最新文献

Implementation and Performance Assessment of Wavelet Prefiltered Platform Tilt Computation Using Low-cost MEMS IMU 基于低成本MEMS IMU的小波预滤波平台倾斜计算实现及性能评估

2022 IEEE 1st International Conference on Data, Decision and Systems (ICDDS) Pub Date : 2022-12-02 DOI: 10.1109/ICDDS56399.2022.10037452

Arya S J, V. R. Jisha, Ponmalar M, K. Usha, Haridas T R

{"title":"Implementation and Performance Assessment of Wavelet Prefiltered Platform Tilt Computation Using Low-cost MEMS IMU","authors":"Arya S J, V. R. Jisha, Ponmalar M, K. Usha, Haridas T R","doi":"10.1109/ICDDS56399.2022.10037452","DOIUrl":"https://doi.org/10.1109/ICDDS56399.2022.10037452","url":null,"abstract":"MEMS sensors are becoming ubiquitous. Their applications range from navigation solutions in automated driving and personal health monitoring as wearable health devices to biomedical applications. However, their inherent high noise levels limit their use in high-accuracy applications. The scientific community explores many methods to overcome this limitation. Often a trade-off between noise levels, complexity, and bandwidth is encountered. In this paper, MEMS IMU data is denoised using wavelet-based prefiltering. This method retains the high dynamics content of the signal as well. The paper presents the algorithm of Wavelet transform and threshold parameters. We also perform the comparison with traditional methods by defining the key performance indicators (KPIs). Comparison carried out on a composite simulated signal that mimics the real-world signal and original MEMS IMU signal. Further, an actual use case is presented, viz., platform tilt computation, as part of the static alignment process in Inertial Navigation. Through extensive simulations, we establish the effectiveness of denoising sensor data using wavelet packet transform.","PeriodicalId":344311,"journal":{"name":"2022 IEEE 1st International Conference on Data, Decision and Systems (ICDDS)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131927338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

General Transit Feed Specification Assisted Effective Traffic Congestion Prediction Using Decision Trees and Recurrent Neural Networks 基于决策树和递归神经网络的交通拥塞预测

2022 IEEE 1st International Conference on Data, Decision and Systems (ICDDS) Pub Date : 2022-12-02 DOI: 10.1109/ICDDS56399.2022.10037408

C. Bannur, C. Bhat, G. Goutham, H. Mamatha

{"title":"General Transit Feed Specification Assisted Effective Traffic Congestion Prediction Using Decision Trees and Recurrent Neural Networks","authors":"C. Bannur, C. Bhat, G. Goutham, H. Mamatha","doi":"10.1109/ICDDS56399.2022.10037408","DOIUrl":"https://doi.org/10.1109/ICDDS56399.2022.10037408","url":null,"abstract":"Traffic congestion prediction is an open-ended critical problem highlighted by the rapid growth in intra-city transit mobility in recent years fuelling the necessity for an intelligent traffic management system in metropolitan areas. The majority of this research continues to rely on data from electronic devices and mobile signals, which can sometimes be manipulated to mislead the public. Modern state-of-the-art models of predictive analysis of Graph Neural Networks (GNNs), AutoRegressive Integrated Moving Average (ARIMA) and other Hybrid Deep Neural Networks have yielded positive results. However, determining which artificial intelligence model would be able to best address the issue of traffic congestion in densely populated areas. Based on this premise, we focus on using GTFS(General Transit Feed Specification) data and have constructed a meticulous and reflective dataset. We also postulate a study of multifarious models in comparison as well as a novel approach that maps traffic congestion as a classification problem rather than a regression-prediction problem to address the shortcomings of the issue. The highest accuracy metric for the optimised models was using the Decision Tree Classifier which yielded an accuracy of 81%. In this research article, we offer an overview of predicting traffic congestion whilst focusing on the G TFS dataset.","PeriodicalId":344311,"journal":{"name":"2022 IEEE 1st International Conference on Data, Decision and Systems (ICDDS)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127065755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ARMS: An Analysis Framework for Mixed Criticality Systems ARMS:混合临界系统的分析框架

2022 IEEE 1st International Conference on Data, Decision and Systems (ICDDS) Pub Date : 2022-12-02 DOI: 10.1109/ICDDS56399.2022.10037556

L. Colaco, Arun S. Nair, Anurag Madnawat, B. Raveendran

{"title":"ARMS: An Analysis Framework for Mixed Criticality Systems","authors":"L. Colaco, Arun S. Nair, Anurag Madnawat, B. Raveendran","doi":"10.1109/ICDDS56399.2022.10037556","DOIUrl":"https://doi.org/10.1109/ICDDS56399.2022.10037556","url":null,"abstract":"The current era of ubiquitous computing and infor-mation overload prompts for a collaborative knowledge manage-ment and decision support system to pursue genuine scientific re-search. Diverse results by various research groups in the real-time mixed criticality community mandates an online decision support system to disseminate information. The prevalence of real-time mixed criticality systems in a large number of application domains has given birth to several task models in literature. Rigid certification requirements and accurate schedulability analysis in the mixed criticality domain require appropriate and well-defined task models, tools and techniques. This paper presents our efforts in the design and development of a knowledge management and decision support system ARMS - a cloud-based analysis tool for mixed criticality systems. ARMS is a unique and novel platform that brings synthesized knowledge on contemporary research in mixed criticality systems together and provides a platform to collaborate with like-minded academicians and engineers. The harmonized research results disseminated by ARMS serves both as an exploratory platform as well as a decision support system for assisting industrial deployment. ARMS is hosted on Amazon Amplify and the user interface is implemented using ReactJS. ARMS serves as a ready-made analyzer for researchers to validate their designs and acts as a quintessential reference aid for academicians and engineers in the mixed criticality domain.","PeriodicalId":344311,"journal":{"name":"2022 IEEE 1st International Conference on Data, Decision and Systems (ICDDS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131042705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AI Based Deepfake Detection 基于人工智能的深度伪造检测

2022 IEEE 1st International Conference on Data, Decision and Systems (ICDDS) Pub Date : 2022-12-02 DOI: 10.1109/ICDDS56399.2022.10037286

Aditi Garde, Shraddha Suratkar, F. Kazi

{"title":"AI Based Deepfake Detection","authors":"Aditi Garde, Shraddha Suratkar, F. Kazi","doi":"10.1109/ICDDS56399.2022.10037286","DOIUrl":"https://doi.org/10.1109/ICDDS56399.2022.10037286","url":null,"abstract":"Advances in machine learning, especially following the 2014 release of Generative Adversarial Networks, have allowed techniques and methods to be used for nefarious ends. Generative Adversarial Networks can even create fake images and videos which appears to be real to human eyes. Generative Adversarial Networks can swap the faces of two different people. For film producers or graphic designers, this tool is quite useful. Face swapping has been done in movies to replace the real person's face with that of the actor. A computer-generated versions of actors Carrie Fisher and Peter Cushing have been featured in a movie named “Star Wars: The Rise of Skywalker” from the “Star wars” film series just like they appeared in the 1977 original, while other Marvel films have “de-aged” actors such as Michael Douglas and Robert Downey Jr. However, this has the potential to be misused. By producing ultra-realistic Deep Fake videos using various trailblazing machine learning techniques, felon are trying to harass, blackmail the innocents. It can also be used to induce political instability by disseminating erroneous information, which can cause communal, diplomatic, and violent outbreak with disastrous consequences. This gives rise to a significant menaces to security of the person as well as national defence, necessitating the development of automated methods for detecting deep fake videos. The eye blinking pattern in deepfaked videos is not formed as naturally as it should be due to the incapability of Generative Adversarial Networks. This will come in handy when constructing a deepfake detecting algorithm. The project uses an object's eye blinking pattern to determine whether or not a video is deepfaked.","PeriodicalId":344311,"journal":{"name":"2022 IEEE 1st International Conference on Data, Decision and Systems (ICDDS)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133525582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A machine learning based frame work for classification of neuromuscular disorders 基于机器学习的神经肌肉疾病分类框架

2022 IEEE 1st International Conference on Data, Decision and Systems (ICDDS) Pub Date : 2022-12-02 DOI: 10.1109/ICDDS56399.2022.10037513

G. Murthy, G. Phawahan Saii, T. Pavani, J. Lalith Mohan

引用次数: 1

Smart Sensor Network Based Rover Design for Surveillance 基于智能传感器网络的监控漫游车设计

2022 IEEE 1st International Conference on Data, Decision and Systems (ICDDS) Pub Date : 2022-12-02 DOI: 10.1109/ICDDS56399.2022.10037295

Manoj Kumar, M. Sabarimuthu, N. Telagam, Bellam Naveen Kumar, Hemanth Sai Upputuri, Dola Sainath Reddy

引用次数: 1

Two Step Recognition of Raags in Hindustani Classical Music Using Supervised Deep Learning 利用监督深度学习两步识别印度斯坦古典音乐中的破布

2022 IEEE 1st International Conference on Data, Decision and Systems (ICDDS) Pub Date : 2022-12-02 DOI: 10.1109/ICDDS56399.2022.10037397

Shobhan Banerjee, Geetanjali Hota, Ribhu Sanyal, M. Rath

引用次数: 1

Fake news detection: A RNN-LSTM, Bi-LSTM based deep learning approach 假新闻检测:基于RNN-LSTM、Bi-LSTM的深度学习方法

2022 IEEE 1st International Conference on Data, Decision and Systems (ICDDS) Pub Date : 2022-12-02 DOI: 10.1109/ICDDS56399.2022.10037403

Govind Singh Mahara, Sharad Gangele

{"title":"Fake news detection: A RNN-LSTM, Bi-LSTM based deep learning approach","authors":"Govind Singh Mahara, Sharad Gangele","doi":"10.1109/ICDDS56399.2022.10037403","DOIUrl":"https://doi.org/10.1109/ICDDS56399.2022.10037403","url":null,"abstract":"Fake news is a new phenomenon that promotes misleading information and fraud via internet social media or traditional news sources. Fake news is readily manufactured and transmitted across numerous social media platforms nowadays, and it has a significant influence on the real world. It is vital to create effective algorithms and tools for detecting misleading information on social media platforms. Most modern research approaches for identifying fraudulent information are based on machine learning, deep learning, feature engineering, graph mining, image and video analysis, and newly built datasets and online services. There is a pressing need to develop a viable approach for readily detecting misleading information. The deep learning LSTM and Bi-LSTM model was proposed as a method for detecting fake news, In this work. First, the NLTK toolkit was used to remove stop words, punctuation, and special characters from the text. The same toolset is used to tokenize and preprocess the text. Since then, GLOVE word embeddings have incorporated higher-level characteristics of the input text extracted from long-term relationships between word sequences captured by the RNN-LSTM, Bi-LSTM model to the preprocessed text. The proposed model additionally employs dropout technology with Dense layers to improve the model's efficiency. The proposed RNN Bi-LSTM-based technique obtains the best accuracy of 94%, and 93% using the Adam optimizer and the Binary cross-entropy loss function with Dropout (0.1,0.2), Once the Dropout range increases it decreases the accuracy of the model as it goes 92% once Dropout (0.3).","PeriodicalId":344311,"journal":{"name":"2022 IEEE 1st International Conference on Data, Decision and Systems (ICDDS)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129317362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

“Smart Home Automation Device” Using Raspberry Pie and Arduino Uno 使用树莓派和Arduino Uno的“智能家居自动化设备”

2022 IEEE 1st International Conference on Data, Decision and Systems (ICDDS) Pub Date : 2022-12-02 DOI: 10.1109/ICDDS56399.2022.10037544

Harsha Vardhan Tomar, Anagha Anand, H. Harsha, Anubhav Deshwal, Badari Nath K

引用次数: 0

People Analyser 人分析器

2022 IEEE 1st International Conference on Data, Decision and Systems (ICDDS) Pub Date : 2022-12-02 DOI: 10.1109/ICDDS56399.2022.10037307

Ayushi Parikh, Hridya K Prasanth, Nilima Kulkarni, Satyam Bhalerao, Shivam Koul

引用次数: 0