Pub Date : 2022-12-02DOI: 10.1109/ICDDS56399.2022.10037452
Arya S J, V. R. Jisha, Ponmalar M, K. Usha, Haridas T R
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.
{"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}
Pub Date : 2022-12-02DOI: 10.1109/ICDDS56399.2022.10037408
C. Bannur, C. Bhat, G. Goutham, H. Mamatha
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.
{"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}
Pub Date : 2022-12-02DOI: 10.1109/ICDDS56399.2022.10037556
L. Colaco, Arun S. Nair, Anurag Madnawat, B. Raveendran
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.
{"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}
Pub Date : 2022-12-02DOI: 10.1109/ICDDS56399.2022.10037286
Aditi Garde, Shraddha Suratkar, F. Kazi
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.
{"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}
Pub Date : 2022-12-02DOI: 10.1109/ICDDS56399.2022.10037513
G. Murthy, G. Phawahan Saii, T. Pavani, J. Lalith Mohan
Neuromuscular disorders, primarily due to either random mutation of genes or problems in the human immune system, cause muscular atrophy, weakness or balancing problems. With an estimated diabetic population of 578 million by 2030, the subsequent risk of being affected by diabetic neuropathy is also more. In particular Amyotrophic lateral sclerosis (ALS) is the non-curable disease caused by death or loss of neurons. Current work proposes a machine learning based frame work to demarcate between normal and myopathic subjects. Electromyography (EMG) signals taken from the biceps brachii muscle located on the upper arm are considered for the purpose.
{"title":"A machine learning based frame work for classification of neuromuscular disorders","authors":"G. Murthy, G. Phawahan Saii, T. Pavani, J. Lalith Mohan","doi":"10.1109/ICDDS56399.2022.10037513","DOIUrl":"https://doi.org/10.1109/ICDDS56399.2022.10037513","url":null,"abstract":"Neuromuscular disorders, primarily due to either random mutation of genes or problems in the human immune system, cause muscular atrophy, weakness or balancing problems. With an estimated diabetic population of 578 million by 2030, the subsequent risk of being affected by diabetic neuropathy is also more. In particular Amyotrophic lateral sclerosis (ALS) is the non-curable disease caused by death or loss of neurons. Current work proposes a machine learning based frame work to demarcate between normal and myopathic subjects. Electromyography (EMG) signals taken from the biceps brachii muscle located on the upper arm are considered for the purpose.","PeriodicalId":344311,"journal":{"name":"2022 IEEE 1st International Conference on Data, Decision and Systems (ICDDS)","volume":"19 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":"122273166","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}
Pub Date : 2022-12-02DOI: 10.1109/ICDDS56399.2022.10037295
Manoj Kumar, M. Sabarimuthu, N. Telagam, Bellam Naveen Kumar, Hemanth Sai Upputuri, Dola Sainath Reddy
Robotics is quick-growing and exciting in today's generation. Globally, robots with inbilt software has sufficient intelligence to control the surrounding environment. Developing a robot to keep away from limitations is taken into consideration as a critical step in setting up personal cars. These motors are utilized in numerous operations, which include transportation, surveillance, and rescue operations. The proposed prototype has an integrated ultrasonic sensor. The Arduino is equipped with a Wi-Fi camera and live video streaming, which may be viewed via various terminals, such as smartphones, tablets, and PCs. The robotic car employs ultrasonic distance sensors to identify obstacles as it runs on the Arduino UNO board. Robot capable of complete independence, preventing collisions while navigating an uncharted area.
{"title":"Smart Sensor Network Based Rover Design for Surveillance","authors":"Manoj Kumar, M. Sabarimuthu, N. Telagam, Bellam Naveen Kumar, Hemanth Sai Upputuri, Dola Sainath Reddy","doi":"10.1109/ICDDS56399.2022.10037295","DOIUrl":"https://doi.org/10.1109/ICDDS56399.2022.10037295","url":null,"abstract":"Robotics is quick-growing and exciting in today's generation. Globally, robots with inbilt software has sufficient intelligence to control the surrounding environment. Developing a robot to keep away from limitations is taken into consideration as a critical step in setting up personal cars. These motors are utilized in numerous operations, which include transportation, surveillance, and rescue operations. The proposed prototype has an integrated ultrasonic sensor. The Arduino is equipped with a Wi-Fi camera and live video streaming, which may be viewed via various terminals, such as smartphones, tablets, and PCs. The robotic car employs ultrasonic distance sensors to identify obstacles as it runs on the Arduino UNO board. Robot capable of complete independence, preventing collisions while navigating an uncharted area.","PeriodicalId":344311,"journal":{"name":"2022 IEEE 1st International Conference on Data, Decision and Systems (ICDDS)","volume":"39 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":"126561756","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}
Pub Date : 2022-12-02DOI: 10.1109/ICDDS56399.2022.10037397
Shobhan Banerjee, Geetanjali Hota, Ribhu Sanyal, M. Rath
In the Indian Classical system of music, we have various raags which differ from one another based on the notes being used in them, their frequency bandwidth, the subtle intricacies of the progression of notes, etc. In [1], we have worked upon the classification of a sample of music into a thaat which is an upper-level classification simply based on the frequencies present in it. In this paper, we take the work one step ahead to recognize the raag after successful classification of the thaat. In this way, it forms a two-step process where we first identify the thaat under which the music falls, followed by which we recognize the raag which corresponds to the music in consideration.
{"title":"Two Step Recognition of Raags in Hindustani Classical Music Using Supervised Deep Learning","authors":"Shobhan Banerjee, Geetanjali Hota, Ribhu Sanyal, M. Rath","doi":"10.1109/ICDDS56399.2022.10037397","DOIUrl":"https://doi.org/10.1109/ICDDS56399.2022.10037397","url":null,"abstract":"In the Indian Classical system of music, we have various raags which differ from one another based on the notes being used in them, their frequency bandwidth, the subtle intricacies of the progression of notes, etc. In [1], we have worked upon the classification of a sample of music into a thaat which is an upper-level classification simply based on the frequencies present in it. In this paper, we take the work one step ahead to recognize the raag after successful classification of the thaat. In this way, it forms a two-step process where we first identify the thaat under which the music falls, followed by which we recognize the raag which corresponds to the music in consideration.","PeriodicalId":344311,"journal":{"name":"2022 IEEE 1st International Conference on Data, Decision and Systems (ICDDS)","volume":"1 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":"130782460","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}
Pub Date : 2022-12-02DOI: 10.1109/ICDDS56399.2022.10037403
Govind Singh Mahara, Sharad Gangele
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).
{"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}
Pub Date : 2022-12-02DOI: 10.1109/ICDDS56399.2022.10037544
Harsha Vardhan Tomar, Anagha Anand, H. Harsha, Anubhav Deshwal, Badari Nath K
Over the past decade, there has been a significant improvement in home security systems and technology in general. This in addition with the leverage of control offered by smart phones has made integration of smart home systems a lot easier and faster. From advanced home networks and remote access to features like security systems and light control, one can take advantage of technologies like Internet of Things and Artificial Intelligence and develop a smart home system. This paper presents a highly featured home automation system by integrating Raspberry Pi, Arduino UNO, Camera Module, a 7-inch LCD panel and various other sensors. The main features encompassed in this model include Gas Leakage Detection system, Intrusion Detection system, Lights control, Real-time weather reports, Music player, Image Viewer. In case of gas leakage or intrusion, alerts and notifications are sent to the Telegram App. These wide range of features and functionalities make the model potent and efficacious.
{"title":"“Smart Home Automation Device” Using Raspberry Pie and Arduino Uno","authors":"Harsha Vardhan Tomar, Anagha Anand, H. Harsha, Anubhav Deshwal, Badari Nath K","doi":"10.1109/ICDDS56399.2022.10037544","DOIUrl":"https://doi.org/10.1109/ICDDS56399.2022.10037544","url":null,"abstract":"Over the past decade, there has been a significant improvement in home security systems and technology in general. This in addition with the leverage of control offered by smart phones has made integration of smart home systems a lot easier and faster. From advanced home networks and remote access to features like security systems and light control, one can take advantage of technologies like Internet of Things and Artificial Intelligence and develop a smart home system. This paper presents a highly featured home automation system by integrating Raspberry Pi, Arduino UNO, Camera Module, a 7-inch LCD panel and various other sensors. The main features encompassed in this model include Gas Leakage Detection system, Intrusion Detection system, Lights control, Real-time weather reports, Music player, Image Viewer. In case of gas leakage or intrusion, alerts and notifications are sent to the Telegram App. These wide range of features and functionalities make the model potent and efficacious.","PeriodicalId":344311,"journal":{"name":"2022 IEEE 1st International Conference on Data, Decision and Systems (ICDDS)","volume":"189 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":"115505323","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}
People Analyzer is a platform that uses machine learning algorithm to predict personality of the user and Unity3D to illustrate scenarios where the user can interact with characters of different personalities. It is based on various platforms like Android Studio, Google colab, Unity3D etc. This platform proves to be useful for any individual who wishes to understand their own personalities as well as interact with people in different environments and also institutes and companies who want to assess their candidates. A questionnaire similar to MBTI test which classifies OCEAN personalities, that are Openness, Conscientiousness, Extraversion, Agreeableness, Neuroticism, acts as an input to a machine learning model where libraries like KMeans helps in predicting the personality. Further in our proposed work we have developed an environment where characters portraying each personality, is present for users to interact with them and understand how their reactions and actions are.
People Analyzer是一个使用机器学习算法来预测用户性格的平台，使用Unity3D来演示用户可以与不同性格的人物互动的场景。它基于Android Studio, Google colab, Unity3D等各种平台。事实证明，这个平台对任何想要了解自己个性的人、与不同环境中的人互动的人、以及想要评估候选人的机构和公司都很有用。一份类似于MBTI测试的调查问卷，对OCEAN性格进行分类，即开放性、严谨性、外向性、宜人性和神经质，作为机器学习模型的输入，KMeans等库可以帮助预测性格。在我们提出的工作中，我们进一步开发了一个环境，在这个环境中，角色描绘了每个人的个性，让用户与他们互动，并了解他们的反应和行动。
{"title":"People Analyser","authors":"Ayushi Parikh, Hridya K Prasanth, Nilima Kulkarni, Satyam Bhalerao, Shivam Koul","doi":"10.1109/ICDDS56399.2022.10037307","DOIUrl":"https://doi.org/10.1109/ICDDS56399.2022.10037307","url":null,"abstract":"People Analyzer is a platform that uses machine learning algorithm to predict personality of the user and Unity3D to illustrate scenarios where the user can interact with characters of different personalities. It is based on various platforms like Android Studio, Google colab, Unity3D etc. This platform proves to be useful for any individual who wishes to understand their own personalities as well as interact with people in different environments and also institutes and companies who want to assess their candidates. A questionnaire similar to MBTI test which classifies OCEAN personalities, that are Openness, Conscientiousness, Extraversion, Agreeableness, Neuroticism, acts as an input to a machine learning model where libraries like KMeans helps in predicting the personality. Further in our proposed work we have developed an environment where characters portraying each personality, is present for users to interact with them and understand how their reactions and actions are.","PeriodicalId":344311,"journal":{"name":"2022 IEEE 1st International Conference on Data, Decision and Systems (ICDDS)","volume":"37 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":"125025579","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：