2022 4th International Conference on Artificial Intelligence and Speech Technology (AIST)最新文献_第5页

Investigation on Medicated Drugs in ECG of Healthy Subjects 健康受试者心电图用药情况调查

2022 4th International Conference on Artificial Intelligence and Speech Technology (AIST) Pub Date : 2022-12-09 DOI: 10.1109/AIST55798.2022.10064840

V. Vijean, Abdul Ghapar Ahmad, Syakirah Afiza Mohammed, Razi Ahmad, Wan Amiza Amneera Wan Ahmad, R. Santiagoo, L. C. Chin

{"title":"Investigation on Medicated Drugs in ECG of Healthy Subjects","authors":"V. Vijean, Abdul Ghapar Ahmad, Syakirah Afiza Mohammed, Razi Ahmad, Wan Amiza Amneera Wan Ahmad, R. Santiagoo, L. C. Chin","doi":"10.1109/AIST55798.2022.10064840","DOIUrl":"https://doi.org/10.1109/AIST55798.2022.10064840","url":null,"abstract":"Heart diseases are now the leading cause of death worldwide, it is estimated that around 7 million patients who are living in developed countries, lost their lives due to diseases related to their cardiovascular system. In Malaysia, cardiovascular diseases represents one fifth of total deaths in the country in the past three decades. Currently patients need some sort of drugs that help them to stabilize and restore the regular patterns of their heart beat because if the patients cannot manage to restore the normal heart beat pattern, the undesired heart condition could lead life threatening situations. Advancement of biotechnology has enabled the creation of new medicated drugs to provide better treatment options. However, when this treatment option fails and there is a need to provide emergency intervention to the patients in hospitals, the medical experts often need to know about the patients’ intake of any medications prior to hospital admittance for providing suitable treatments. Sometimes, this would be a difficult task as the patient might be admitted in semi-conscious or unconscious state. Therefore, this study focusses on identification of different medicated drugs usage through analysis of ECG data of the users. The data for the experiment was obtained from physionet library, which provides ECG data of subjects administered with a combination of Dofetilide, Mexiletine, lidocaine, Moxifloxacin and Diltiazem medicated drugs. The use of morphological and non-linear features derived from the ECG signals were able to provide prediction accuracy of 77.26% using SVM classifier.","PeriodicalId":360351,"journal":{"name":"2022 4th International Conference on Artificial Intelligence and Speech Technology (AIST)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126352256","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

Map-Reduce Based Parallel Firefly Algorithm For Fast Recommendations 基于Map-Reduce的快速推荐并行萤火虫算法

2022 4th International Conference on Artificial Intelligence and Speech Technology (AIST) Pub Date : 2022-12-09 DOI: 10.1109/AIST55798.2022.10064743

Bharti Sharma, Saksham Kumar Sharma, Poonam Bansal, N. Sushma, Sangam Sangam

{"title":"Map-Reduce Based Parallel Firefly Algorithm For Fast Recommendations","authors":"Bharti Sharma, Saksham Kumar Sharma, Poonam Bansal, N. Sushma, Sangam Sangam","doi":"10.1109/AIST55798.2022.10064743","DOIUrl":"https://doi.org/10.1109/AIST55798.2022.10064743","url":null,"abstract":"The Recommendation System is a strong tool that aids the decision-making process across a variety of situations. Using the aid of aspects such as prior experiences of the user, their ratings, comparable interests, etc., we can acquire the most relevant results upon the application of various optimization techniques. A movie recommendation system is a useful tool/software that aids users in rapidly obtaining optimum results with comparable interests. Using the Firefly clustering technique, this study focuses on a movie recommendation system whose major goal is to propose movies of comparable interest to the active user. Although much study has been done in the topic of recommendation systems, there are still several issues with producing suitable results. To address these issues, we suggested a strategy that uses a meta-heuristic approach to get optimal outcomes. Instead of utilising K-means, fuzzy C-means, and other algorithms, we present the Firefly clustering method in this research to provide the best optimum outcomes in recommendation systems. For performance analysis, many measurements such as t-value, RMSE, SD, and MAE are utilised.","PeriodicalId":360351,"journal":{"name":"2022 4th International Conference on Artificial Intelligence and Speech Technology (AIST)","volume":"194 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115582948","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

An Efficient Method to Recognize and Separate Patient’s Audio from Recorded Data 一种有效的患者语音识别与分离方法

2022 4th International Conference on Artificial Intelligence and Speech Technology (AIST) Pub Date : 2022-12-09 DOI: 10.1109/AIST55798.2022.10065116

Arjita Choubey, M. Pandey, Ashwani Kumar Dubey

{"title":"An Efficient Method to Recognize and Separate Patient’s Audio from Recorded Data","authors":"Arjita Choubey, M. Pandey, Ashwani Kumar Dubey","doi":"10.1109/AIST55798.2022.10065116","DOIUrl":"https://doi.org/10.1109/AIST55798.2022.10065116","url":null,"abstract":"Separation of two voices along with silences and noise is one of the important parts of audio data pre-processing. This pre-processing increases the accuracy of any function. Removal of silence and unwanted voice is especially important in case of health care where doctors’ voice is not required. The proposed Patient’s Audio Recognition and Segmentation Model (PARSM) elaborates an end-to-end methodology for removing silence as well as voice of the virtual interviewer from DIAC-WOZ dataset. This model not only ensures creation of new audio file but also checks for eligibility of audio for being segmentable on the basis of close proximity of voices. In the dataset the volume levels of voice of interviewer and a patient is distinguishable. This fact is utilized in the model as it uses Short Time Energy as a feature. The binary classification is done using Support Vector Machine (SVM). After the calculation of STE, the signal is classified as either low energy or high energy signals. High energy signals, which depict voice of the patient, are then concatenated together to get desired output audio signal. Also, the weight factor can also be varied for each audio manually depending upon the requirement of strictness of segmentation for each audio.","PeriodicalId":360351,"journal":{"name":"2022 4th International Conference on Artificial Intelligence and Speech Technology (AIST)","volume":"657 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132093299","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 Novel Classification Technique for Safety Measures on Covid-19 Using Featured-Based Sentimental Analysis 基于特征的情感分析的新型Covid-19安全措施分类技术

2022 4th International Conference on Artificial Intelligence and Speech Technology (AIST) Pub Date : 2022-12-09 DOI: 10.1109/AIST55798.2022.10064878

V. P, Dorababu Sudarsa, Purushotham E, Sreeraman Y, Siva Kumar Pathuri, C. Prasad

{"title":"A Novel Classification Technique for Safety Measures on Covid-19 Using Featured-Based Sentimental Analysis","authors":"V. P, Dorababu Sudarsa, Purushotham E, Sreeraman Y, Siva Kumar Pathuri, C. Prasad","doi":"10.1109/AIST55798.2022.10064878","DOIUrl":"https://doi.org/10.1109/AIST55798.2022.10064878","url":null,"abstract":"Covid-19 is a term that has frightened the globe because it has broken beyond socioeconomic barriers in which people literally forgot the word social help because of this deadliest virus.The main goal of this study is to create a model that forecasts Covid-19 reviews based on coronavirus ratings from Kaggle repository. The World Health Organization(WHO) declared a pandemic of the coronavirus infection when it first appeared in 2019. People are worrying and concerned about their health as the number of instances rises throughout the world. People’s physical and emotional health is inversely proportional to the pandemic scenario. As a result, in this case, a categorization model based on numerous metrics is required to rescue nations by analyzing facts and information about the outbreak. In this article to organise the reviews or opinions provided by people worldwide, we performed emotional or opinion classification using a Novel classifier. Then, the accuracy of the proposed model is compared with existing base classifiers like NB(Naive-Bayes) and Support Vector Machine(SVM), where Novel classifier gave the best accuracy compared to the other two classifiers, i.e., 95","PeriodicalId":360351,"journal":{"name":"2022 4th International Conference on Artificial Intelligence and Speech Technology (AIST)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128450857","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

Classification of Flower Dataset using Machine Learning Models 基于机器学习模型的花卉数据集分类

2022 4th International Conference on Artificial Intelligence and Speech Technology (AIST) Pub Date : 2022-12-09 DOI: 10.1109/AIST55798.2022.10065178

Tina Gupta, Puja Arora, Ritu Rani, Garima Jaiswal, Poonam Bansal, A. Dev

{"title":"Classification of Flower Dataset using Machine Learning Models","authors":"Tina Gupta, Puja Arora, Ritu Rani, Garima Jaiswal, Poonam Bansal, A. Dev","doi":"10.1109/AIST55798.2022.10065178","DOIUrl":"https://doi.org/10.1109/AIST55798.2022.10065178","url":null,"abstract":"Modern day machine learning aims to categorize data based on developed models and predict future outcomes according to these models. Today Machine Learning finds its application in various fields such as facial recognition, speech recognition, medical diagnosis for example predicting potential heart failure, sentiment analysis, product recommendations etc. This paper proposes 3 classification models to efficiently predict the Iris flower species. The proposed model uses Exploratory Data Analysis (EDA) to analyse and pre-process the dataset and the prediction is performed by the three classification models namely- \"Logistic Regression, Support Vector Machine (SVM) and K-Nearest Neighbours (KNN)\". All the proposed models are tested on Iris dataset and achieved maximum accuracy of 96.43, 98.21 and 94.64 percent respectively. This paper provides a thorough analysis of the various supervised machine learning models that are suitable for predicting the species of Iris flower based on the various attributes like sepal width, sepal length, petal width and petal length.","PeriodicalId":360351,"journal":{"name":"2022 4th International Conference on Artificial Intelligence and Speech Technology (AIST)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121607505","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: 4

Detecting Keratoconus using Machine Learning Models 使用机器学习模型检测圆锥角膜

2022 4th International Conference on Artificial Intelligence and Speech Technology (AIST) Pub Date : 2022-12-09 DOI: 10.1109/AIST55798.2022.10065321

Radhika Goyal, Priyankar Maity, Madhulika Bhatia, Ashish Grover

{"title":"Detecting Keratoconus using Machine Learning Models","authors":"Radhika Goyal, Priyankar Maity, Madhulika Bhatia, Ashish Grover","doi":"10.1109/AIST55798.2022.10065321","DOIUrl":"https://doi.org/10.1109/AIST55798.2022.10065321","url":null,"abstract":"One of the major progressing sectors due to the introduction of technology has been healthcare. Diagnosis of patients has improved by manifolds. Keratoconus is a rare disease where it affects the patient’s cornea. There are ongoing researches around the world to find a solution that is accessible and practical. Our objective is to detect whether a person is suffering from Keratoconus or not. This huge volume of important data cannot be handled manually, hence use of concepts like machine learning, data analysis, data mining, etc. play an important role. To evaluate accuracy of Machine learning models like Inception V3, VGG16, MobileNet V2, ResNet 50 using color coded corneal maps. The authors have implemented these models and compared their performances amongst each other and thus select the best fit model. The training set contains of 1050 images and comprising of 1051 Normal eyes and 862 Suspect eyes. The models were implemented in python language on the Google Colab Platform. These models are providing a range of 75-95% accuracies depending on the different models. The highest accuracy was obtained by Inception V3 which was 95%. The dataset were corneal maps recorded using Scheimpflug imaging system. Based on the classification of the parameters of the corneal maps, the input data was sorted on the basis of severity and also predicting how likely the patient is to suffer from keratoconus","PeriodicalId":360351,"journal":{"name":"2022 4th International Conference on Artificial Intelligence and Speech Technology (AIST)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123737335","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

An Approach on BCI based Silent Speech Interface for Automatic Speech Recognition 基于脑机接口的无声语音自动识别方法

2022 4th International Conference on Artificial Intelligence and Speech Technology (AIST) Pub Date : 2022-12-09 DOI: 10.1109/AIST55798.2022.10064895

N. Ramkumar, D. Renuka, L. Ashok kumar

{"title":"An Approach on BCI based Silent Speech Interface for Automatic Speech Recognition","authors":"N. Ramkumar, D. Renuka, L. Ashok kumar","doi":"10.1109/AIST55798.2022.10064895","DOIUrl":"https://doi.org/10.1109/AIST55798.2022.10064895","url":null,"abstract":"Paralysis is the loss of muscle function in a part of the body. It occurs when the transmission of messages between brain and muscles is disrupted. Approximately 5.4 million people have some form of paralysis. . Depending on the site of the damage, paralysis may be accompanied by a lack of sensation. The majority of paralysis is caused by strokes or spinal cord injury. The Brain computer interface (BCI) is an interface between the Brain and the computer. It is an emerging field wherein it’s a fast-growing emerging technology, in which researchers acquire data in the form for signals by using various sensors. It uses artificially produced electrical signals to stimulate the brain, transfer sensory information to the brain, or restore sensory function. Motor speech dysfunction paralyzes people and prevents them from speaking. Since paralyzed people are unable to speak or move, it is very difficult to fulfill the basic requirements. One way to approach the problem of voice recognition using brain waves is to quantify the brain signals from an individual human being. By examining the EEG signals from an individual person, and to investigating the feasibility of detecting syllable level units.","PeriodicalId":360351,"journal":{"name":"2022 4th International Conference on Artificial Intelligence and Speech Technology (AIST)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124709559","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

Detection and Localization of Breast Lesion with VGG19 Optimized Vision Transformer VGG19优化视觉变压器对乳腺病变的检测与定位

2022 4th International Conference on Artificial Intelligence and Speech Technology (AIST) Pub Date : 2022-12-09 DOI: 10.1109/AIST55798.2022.10065355

Kamakshi Rautela, Dinesh Kumar, Vijay Kumar

引用次数: 0

Brain Tumour Segmentation Techniques from MR Images using Machine Learning: An Analysis 使用机器学习的MR图像的脑肿瘤分割技术:分析

2022 4th International Conference on Artificial Intelligence and Speech Technology (AIST) Pub Date : 2022-12-09 DOI: 10.1109/AIST55798.2022.10064733

Shivangi Sinha, Amar Saraswat, Shweta A. Bansal, S. Sharan

{"title":"Brain Tumour Segmentation Techniques from MR Images using Machine Learning: An Analysis","authors":"Shivangi Sinha, Amar Saraswat, Shweta A. Bansal, S. Sharan","doi":"10.1109/AIST55798.2022.10064733","DOIUrl":"https://doi.org/10.1109/AIST55798.2022.10064733","url":null,"abstract":"One disease kind that targets the brain in the form of clots is a brain tumour. An MRI image is needed in order to see a brain tumour in detail. Because of their similar colours, brain tumours and normal tissue might be hard to tell apart. Accurate research must be done on brain tumours. Segmentation is the answer to analysing a brain tumour. To get around this problem, brain tumour segmentation is used to split the brain tumour made up of various tissues, such as fat, edema, cerebrospinal fluid and normal brain tissue. The MRI image must first the kept at the margin of the image using median filtering. Then the threshold method is needed for the tumour segmentation procedure, which is iterated to take the greatest area. Nowadays, automated disease diagnosis using Magnetic Resonance Images, mammography, and further sources commonly makes use of these CBIR techniques. As a part of the objective of innovation for sustained development, this gap could be closed with the help of our innovative edge detection technique and deep learning feature extraction algorithm, accuracy is now considerably closer to that of manual evaluation by a human.","PeriodicalId":360351,"journal":{"name":"2022 4th International Conference on Artificial Intelligence and Speech Technology (AIST)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125393907","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

Chatbots in Healthcare: Challenges, Technologies and Applications 医疗保健中的聊天机器人:挑战、技术和应用

2022 4th International Conference on Artificial Intelligence and Speech Technology (AIST) Pub Date : 2022-12-09 DOI: 10.1109/AIST55798.2022.10065328

Deepali Sharma, S. Kaushal, H. Kumar, S. Gainder

{"title":"Chatbots in Healthcare: Challenges, Technologies and Applications","authors":"Deepali Sharma, S. Kaushal, H. Kumar, S. Gainder","doi":"10.1109/AIST55798.2022.10065328","DOIUrl":"https://doi.org/10.1109/AIST55798.2022.10065328","url":null,"abstract":"Artificial intelligence (AI) technologies have been around for more than fifty years. However, current improvements in processing power, the accessibility of huge amounts of data, and improved algorithms have led to significant advancements in AI. A chatbot is a software program with AI that simulates user interactions. Healthcare chatbots gradually eliminate hospital wait times, appointments, and consultation meetups, thus instantly assisting patients in connecting with the right doctor. Chatbots reduce the workload of healthcare providers by decreasing the number of hospital visits and unnecessary treatments, providing suggestions and alerts. However, the introduction of such chatbots in the healthcare domain exposes users to a plethora of challenges. This paper presents a systematic survey of recent developments by researchers in the field of healthcare chatbots. This research brings to light general information regarding the application type, various technologies and evaluation methods that have been used to evaluate the effectiveness of healthcare chatbots and aims to serve as a research guideline which may be valuable for the development of chatbots in various fields.","PeriodicalId":360351,"journal":{"name":"2022 4th International Conference on Artificial Intelligence and Speech Technology (AIST)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120978219","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