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Smart Health Monitoring System With Iot 物联网智能健康监测系统
2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR) Pub Date : 2022-03-10 DOI: 10.1109/ICAITPR51569.2022.9844201
A. S. Prasad, M. Jayaram
{"title":"Smart Health Monitoring System With Iot","authors":"A. S. Prasad, M. Jayaram","doi":"10.1109/ICAITPR51569.2022.9844201","DOIUrl":"https://doi.org/10.1109/ICAITPR51569.2022.9844201","url":null,"abstract":"During a pandemic, healthcare automation ensures patient assistance and eliminates the need for a caretaker near the patient. As a result, a Raspberry Pi-based IoT smart system was built to meet the needs of the circumstance. This paper presents automation strategies for measuring data such as temperature, ECG, heartbeat, saline bottle level, and body fall in the healthcare setting. Instead of using an Ag/AgCl electrode, the ECG signal is acquired using a Graphene NanoRibbon (GNR) electrode. These automation gadgets use sensors and mobile communication devices to monitor patient health remotely. The suggested system employs a heart rate sensor that detects a person’s heart rate and sends the heart rate and R-R interval measurements via the Internet. The results can be recorded and shown on an LCD screen using a Raspberry Pi. The main aim of work is to provide an extensive research in capturing the sensor data, analyzing the data and providing a feedback to patients based on different health parameters. During an emergency, an alert message is delivered to the concerned person’s mobile phone and email address after collecting data from webpage. Data was sent via GSM module for visualization. Doctors can access patient records through a website.","PeriodicalId":262409,"journal":{"name":"2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR)","volume":"19 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116694724","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
Variable Length Digit Recognition for Gujarati Language 古吉拉特语的可变长度数字识别
2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR) Pub Date : 2022-03-10 DOI: 10.1109/ICAITPR51569.2022.9844182
Shrey Malvi, Nirmal Patel, Pratikkumar Prajapati
{"title":"Variable Length Digit Recognition for Gujarati Language","authors":"Shrey Malvi, Nirmal Patel, Pratikkumar Prajapati","doi":"10.1109/ICAITPR51569.2022.9844182","DOIUrl":"https://doi.org/10.1109/ICAITPR51569.2022.9844182","url":null,"abstract":"In this paper, we describe a method to perform handwritten digit recognition for Gujarati - a regional Indian language. Our method can handle variable-length inputs, meaning that there are no limitations around the digit length for the input image. To our knowledge, this is the first attempt to do variable length digit classification for the Gujarati language numerals. We outline a two-step method to classify handwritten Gujarati numerals. The first step identifies connected components of the input image and predicts the numeric length of each connected component. The second step predicts the actual number that is contained within each connected component. The final result is a concatenation of individual predictions. Our Convolutional Neural Networks (CNN) architecture for this task has a low number of output classes (e.g. 30 classes for 3 digit classifier). Our method achieves 83.8% test set accuracy for 1 to 4 digit Gujarati numerals. On the NIST19 dataset, our method achieves 96.1% test set accuracy for 2 to 6 digit English numerals.","PeriodicalId":262409,"journal":{"name":"2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130445271","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: 1
Evaluating audio features for speech/non-speech discrimination 评估语音/非语音区分的音频特征
2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR) Pub Date : 2022-03-10 DOI: 10.1109/ICAITPR51569.2022.9844226
H. Redelinghuys, Zenghui Wang
{"title":"Evaluating audio features for speech/non-speech discrimination","authors":"H. Redelinghuys, Zenghui Wang","doi":"10.1109/ICAITPR51569.2022.9844226","DOIUrl":"https://doi.org/10.1109/ICAITPR51569.2022.9844226","url":null,"abstract":"In this paper, the suitability of audio features for application in speech-music discrimination was evaluated to select a feature set that produces high mean accuracy in the classification algorithm, while also reducing the total feature space. The first four standardized moments of twelve audio features were evaluated namely the mean, variance, skewness and kurtosis of the Root Mean Square value, Short Time Energy Ratio, Zero Crossing Rate, Spectral Rolloff, Spectral Flux, Spectral Centroid, Energy Entropy, Spectral Entropy, the first 13 Mel Frequency Cepstral Coefficients (MFCC), Percentage Low Energy Frames, Modified Low Energy Ratio and 4 Hz Modulation Energy. The 4 Hz modulation Energy feature was computed by two different methods, firstly as a by-product of the MFCC feature and secondly using the Hilbert transform for envelope detection. This resulted in an 88-dimensional feature space. It was demonstrated that with a thorough feature selection process a higher mean accuracy and 50% reduction in dimensionality was achieved.","PeriodicalId":262409,"journal":{"name":"2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125154253","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
Ranking Companies Based On VADER Sentiment Analysis 基于维德情绪分析的公司排名
2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR) Pub Date : 2022-03-10 DOI: 10.1109/ICAITPR51569.2022.9844215
Pothapragada Sri Krishna Chaitanya, Kaushik Kasoju, Sunil Bhutada, Bellamkonda Naga Udaya Chandrika
{"title":"Ranking Companies Based On VADER Sentiment Analysis","authors":"Pothapragada Sri Krishna Chaitanya, Kaushik Kasoju, Sunil Bhutada, Bellamkonda Naga Udaya Chandrika","doi":"10.1109/ICAITPR51569.2022.9844215","DOIUrl":"https://doi.org/10.1109/ICAITPR51569.2022.9844215","url":null,"abstract":"We live in the world where the advancement of technology has been taking place tremendously. On everything being digitalized people began to use electronic gadgets immensely. Most importantly communication has got advanced. Everyone are being able to express their views, opinion and comments readily through social media and other websites. These factors has increased the significance and more implementation of sentiment analysis. Sentiment analysis is a process where the polarities or emotions behind the given text is analyzed. Using sentiment analysis now we can easily classify the comment or views made by the people and give the quicker analysis of data. We came up with idea of using this concept this in ranking of the companies, which is a lot productive for the youth in decision making.","PeriodicalId":262409,"journal":{"name":"2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134631272","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
Feature Selection Based Approach for Handling Cold Start Problem in Collaborative Recommender Systems 基于特征选择的协同推荐系统冷启动问题处理方法
2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR) Pub Date : 2022-03-10 DOI: 10.1109/ICAITPR51569.2022.9844218
Madhusree Kuanr, Puspanjali Mohapatra, Mannava Yesubabu
{"title":"Feature Selection Based Approach for Handling Cold Start Problem in Collaborative Recommender Systems","authors":"Madhusree Kuanr, Puspanjali Mohapatra, Mannava Yesubabu","doi":"10.1109/ICAITPR51569.2022.9844218","DOIUrl":"https://doi.org/10.1109/ICAITPR51569.2022.9844218","url":null,"abstract":"Due to the widespread usage of Information and communication technology (ICT), nowadays people are getting a large number of options to choose a particular item or a service. So, in this scenario, the recommender system (RS) plays a very vital role to optimize their decisions. But cold start problem is one of the major challenges in RS for new users and items. In this paper, a novel method using feature selection and prediction has been proposed to address the cold start problem in Collaborative RS. The proposed approach has been validated using two data sets i.e Laptop Dataset and Red wine Quality dataset taking Mean Absolute Error (MAE) and Precision as the evaluation metrics.","PeriodicalId":262409,"journal":{"name":"2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR)","volume":"298 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127071469","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
Singer Identification – Analysis with SVM and GMM Classifier 歌手识别-用支持向量机和GMM分类器分析
2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR) Pub Date : 2022-03-10 DOI: 10.1109/ICAITPR51569.2022.9844214
D. Y. Loni, S. Subbaraman
{"title":"Singer Identification – Analysis with SVM and GMM Classifier","authors":"D. Y. Loni, S. Subbaraman","doi":"10.1109/ICAITPR51569.2022.9844214","DOIUrl":"https://doi.org/10.1109/ICAITPR51569.2022.9844214","url":null,"abstract":"Singer Identification (SID) plays a vital role in the music information retrieval (MIR) system, as music and singing are inter-bounded entities and partial without one another. This paper presents the singer identification system that identifies the singer by extracting the acoustic features that completely describe the vocal characteristics of the singing voice using a self-developed cappella database. The paper first discusses the performance of the individual acoustic features and then signifies the impact of its combination on the SID accuracy. The SID was investigated with two classifiers – Support Vector Machine (SVM) and Gaussian Mixture Model (GMM). It was found that for all the combinations of the acoustic features, SVM outperformed GMM. Moreover, the experimental work also revealed that the rbf kernel performed better than the polynomial kernel both in terms of performance and computation cost.","PeriodicalId":262409,"journal":{"name":"2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121844428","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: 1
Web application reconnaissance scan detection using LSTM network based deep learning Web应用侦察扫描检测采用基于LSTM网络的深度学习
2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR) Pub Date : 2022-03-10 DOI: 10.1109/ICAITPR51569.2022.9844219
Bronjon Gogoi, Rahul Deka, Suchitra Pyarelal
{"title":"Web application reconnaissance scan detection using LSTM network based deep learning","authors":"Bronjon Gogoi, Rahul Deka, Suchitra Pyarelal","doi":"10.1109/ICAITPR51569.2022.9844219","DOIUrl":"https://doi.org/10.1109/ICAITPR51569.2022.9844219","url":null,"abstract":"Web applications are frequent targets of attack due to their widespread use and round the clock availability. Malicious users can exploit vulnerabilities in web applications to steal sensitive information, modify and destroy data as well as deface web applications. The process of exploiting web applications is a multi-step process and the first step in an attack is reconnaissance, in which the attacker tries to gather information about the target web application. In this step, the attacker uses highly efficient automated scanning tools to scan web applications. Following reconnaissance, the attacker proceeds to vulnerability scanning and subsequently attempts to exploit the vulnerabilities discovered to compromise the web application. Detection of reconnaissance scans by malicious users can be combined with other traditional intrusion detection and prevention systems to improve the security of web applications. In this paper, a method for detecting reconnaissance scans through analysis of web server access logs is proposed. The proposed approach uses an LSTM network based deep learning approach for detecting reconnaissance scans. Experiments conducted show that the proposed approach achieves a mean precision, recall and f1-score of 0.99 over three data sets and precision, recall and f1-score of 0.97, 0.96 and 0.96 over the combined dataset.","PeriodicalId":262409,"journal":{"name":"2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122603094","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
Effects and Comparison of different Data pre-processing techniques and ML and deep learning models for sentiment analysis: SVM, KNN, PCA with SVM and CNN 不同数据预处理技术以及ML和深度学习模型在情感分析中的效果和比较:SVM、KNN、PCA with SVM和CNN
2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR) Pub Date : 2022-03-10 DOI: 10.1109/ICAITPR51569.2022.9844192
Shoaib Hafeez, Nikhila Kathirisetty
{"title":"Effects and Comparison of different Data pre-processing techniques and ML and deep learning models for sentiment analysis: SVM, KNN, PCA with SVM and CNN","authors":"Shoaib Hafeez, Nikhila Kathirisetty","doi":"10.1109/ICAITPR51569.2022.9844192","DOIUrl":"https://doi.org/10.1109/ICAITPR51569.2022.9844192","url":null,"abstract":"In this paper, we have discussed different data pre-processing techniques and different machine learning and deep learning models which are used for sentiment analysis. The dataset used was “Restaurant Reviews” We have compared the results of different results of SVM, KNN, PCA with SVM and CNN models. Each of the different pre-processed datasets was passed to different machine learning and deep learning models and the results were compared to find the most useful data pre-processing technique for a particular model, so we can save resources (time and money) by concentrating our resources on that particular data pre-processing technique for that model.","PeriodicalId":262409,"journal":{"name":"2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124893625","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: 2
Recognition and Classification of Smiles using Computer Vision 基于计算机视觉的微笑识别与分类
2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR) Pub Date : 2022-03-10 DOI: 10.1109/ICAITPR51569.2022.9844198
Ramya Rao, Veena N Hedge
{"title":"Recognition and Classification of Smiles using Computer Vision","authors":"Ramya Rao, Veena N Hedge","doi":"10.1109/ICAITPR51569.2022.9844198","DOIUrl":"https://doi.org/10.1109/ICAITPR51569.2022.9844198","url":null,"abstract":"A simple method for the recognition and classification of varied types of smiles using the basics of machine learning is proposed in this paper. Machine-human interaction has seen exponential growth in the last decade. Key features of this interaction include emotion detection. A smiling face is often considered a sign of euphoria and excitement. The analysis is performed on real-time video sequence. The algorithm used for detection is a 68-point facial landmark recognition with aspect ratio calculation of facial features such as mouth and eyes.","PeriodicalId":262409,"journal":{"name":"2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132599623","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
MARS: A Hybrid Deep CNN-based Multi-Accent Recognition System for English Language MARS:一个基于cnn的混合深度英语多口音识别系统
2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR) Pub Date : 2022-03-10 DOI: 10.1109/ICAITPR51569.2022.9844177
S. Darshana, H. Theivaprakasham, G. Jyothish Lal, B. Premjith, V. Sowmya, Kp Soman
{"title":"MARS: A Hybrid Deep CNN-based Multi-Accent Recognition System for English Language","authors":"S. Darshana, H. Theivaprakasham, G. Jyothish Lal, B. Premjith, V. Sowmya, Kp Soman","doi":"10.1109/ICAITPR51569.2022.9844177","DOIUrl":"https://doi.org/10.1109/ICAITPR51569.2022.9844177","url":null,"abstract":"Classifying the speech of non-native English speakers is challenging due to various features that distinguish accents. Accents vary by sex, age, formality, social status, geographical area, mother tongue, quality of the voice, phoneme, and prosody. This paper proposes a novel, well-structured database of non-native Indian English speaker accents, referred to as IndicAccentDB. IndicAccentDB contains speech samples from 6 different states to address the unbalanced dataset (gender-bias) and speaker mismatch problems observed in the past. The proposed work also discusses the requirements for creating the IndicAccentDB database and pre-processing tasks performed on the dataset. Furthermore, we experimented with accent classification models, namely 1D-CNN, Support Vector Machines, Random forest, Decision tree, ResNet18, ResNet50, and xResNet18, using MFCC and Mel-Spectrogram features to build the robust Multi-Accent Recognition System (MARS). At last, we evaluated the performance of proposed models on the novel database and compared the results using evaluation metrics like precision, accuracy, F1-score, and recall. Based on our findings, xResNet18 was able to identify the accent classes with significant accuracy.","PeriodicalId":262409,"journal":{"name":"2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128306455","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: 2
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