BOHR International Journal of Internet of things, Artificial Intelligence and Machine Learning最新文献

Emotion Recognition Based on Speech Signals by Combining Empirical Mode Decomposition and Deep Neural Network 基于经验模态分解和深度神经网络的语音信号情感识别

BOHR International Journal of Internet of things, Artificial Intelligence and Machine Learning Pub Date : 1900-01-01 DOI: 10.54646/bijiam.014

Shing Tai Pan, Ching Fa Chen, Chuan Cheng Hong

引用次数: 0

Deep Learning Analysis for Estimating Sleep Syndrome Detection Utilizing the Twin Convolutional Model FTC2 利用双卷积模型FTC2估计睡眠综合征检测的深度学习分析

BOHR International Journal of Internet of things, Artificial Intelligence and Machine Learning Pub Date : 1900-01-01 DOI: 10.54646/bijiam.003

Cvetko Tim, Robek Tinkara

{"title":"Deep Learning Analysis for Estimating Sleep Syndrome Detection Utilizing the Twin Convolutional Model FTC2","authors":"Cvetko Tim, Robek Tinkara","doi":"10.54646/bijiam.003","DOIUrl":"https://doi.org/10.54646/bijiam.003","url":null,"abstract":"Manual sleep stage scoring is frequently performed by sleep specialists by visually evaluating the patient's neurophysiological signals acquired in sleep laboratories. This is a difficult, time-consuming, laborious process. Because of the limits of human sleep stage scoring, there is a greater need for creating Automatic Sleep Stage Classification (ASSC) systems. Sleep stage categorization is the process of distinguishing the distinct stages of sleep is an important step in assisting physicians in the diagnosis treatment of associated sleep disorders. In this research, we offer a unique method a practical strategy to predicting early onsets of sleep disorders, such as restless leg syndrome insomnia, using the Twin Convolutional Model FTC2, based on an algorithm composed of two modules. To provide localised time-frequency information, 30 second long epochs of EEG recordings are subjected to a Fast Fourier Transform, a deep convolutional LSTM neural network is trained for sleep stage categorization. Automating sleep stages detection from EEG data offers a great potential to tackling sleep irregularities on a daily basis. Thereby, a novel approach for sleep stage classification is pro- posed which combines the best of signal processing statistics. In this study, we used the PhysioNet Sleep European Data Format (EDF) Database. The code evaluation showed impressive results, reaching accuracy of 90.43, precision of 77.76, recall of 93,32, F1-score of 89.12 with the final mean false error loss 0.09. All the source code is availlable at https://github.com/timothy102/eeg.","PeriodicalId":231453,"journal":{"name":"BOHR International Journal of Internet of things, Artificial Intelligence and Machine Learning","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129767834","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

Enhanced 3D brain tumor segmentation using assortedprecision training 使用分类精确训练增强3D脑肿瘤分割

BOHR International Journal of Internet of things, Artificial Intelligence and Machine Learning Pub Date : 1900-01-01 DOI: 10.54646/bijiam.2022.10

Pandya Pandya, O. Oguine, Harita Bhargava, S. Zade

引用次数: 0

Drug recommendation using recurrent neural networksaugmented with cellular automata 基于元胞自动机增强的递归神经网络的药物推荐

BOHR International Journal of Internet of things, Artificial Intelligence and Machine Learning Pub Date : 1900-01-01 DOI: 10.54646/bijiam.2023.13

S. Gousiya Begum, Pokkuluri Kiran Sree

{"title":"Drug recommendation using recurrent neural networksaugmented with cellular automata","authors":"S. Gousiya Begum, Pokkuluri Kiran Sree","doi":"10.54646/bijiam.2023.13","DOIUrl":"https://doi.org/10.54646/bijiam.2023.13","url":null,"abstract":"Drug recommendation systems are systems that have the capability to recommend drugs. On a daily basis, a hugeamount of data is being generated by the patients. All this valuable data can be properly utilized to create a reliabledrug recommendation system. In this paper, we recommend a system for drug recommendations. The main scopeof our system is to predict the correct medication based on reviews and ratings. Our proposed system uses naturallanguage processing techniques (NLP), recurrent neural networks (RNN), and cellular automata (CA). We alsoconsidered various metrics like precision, recall, accuracy, F1 score, and ROC curve as measures of our system’sperformance. NLP techniques are being used for gathering useful information from patient data, and RNN is amachine learning methodology that works really well in analyzing textual data. The system considers various patientdata attributes like age, gender, dosage, medical history, and symptoms in order to make appropriate predictions.The proposed system has the potential to help medical professionals make informed drug recommendations.","PeriodicalId":231453,"journal":{"name":"BOHR International Journal of Internet of things, Artificial Intelligence and Machine Learning","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125539392","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 internet of things enabled smart firefighting system 物联网智能消防系统

BOHR International Journal of Internet of things, Artificial Intelligence and Machine Learning Pub Date : 1900-01-01 DOI: 10.54646/bijiam.2022.07

R. Subhaa, V. Kandavel, V. Nitheshkumar,, P. Naveen Kumar, N. Parthasarathi, T. Ponsankar

{"title":"An internet of things enabled smart firefighting system","authors":"R. Subhaa, V. Kandavel, V. Nitheshkumar,, P. Naveen Kumar, N. Parthasarathi, T. Ponsankar","doi":"10.54646/bijiam.2022.07","DOIUrl":"https://doi.org/10.54646/bijiam.2022.07","url":null,"abstract":"A fire accident is a mishap that could be either man-made or natural. Fire accidents occur frequently and can becontrolled but may, at times, result in severe loss of life and property. Many a time, firefighters struggle to sort outthe exact source of the fire as it is continuously flammable and spreads all over the area. For this concern, we havedesigned an Internet of things based device which sorts out the exact source of fire through software and hardwaredevices, and also allows the complete detail of an area to be visualized by firefighters, which is pre-installed in thesoftware itself. Because of our system’s intelligence in decision-making during firefighting, the proposed systemis claimed as a “smart firefighting system.” The implementation of the smart firefighting system can make thefirefighters analyze the current situation immediately and make the decision more quickly in an effective manner.Because of this system, fire losses in a building can be greatly reduced and many lives can be rescued immediately.Furthermore, fire spread can also be restricted","PeriodicalId":231453,"journal":{"name":"BOHR International Journal of Internet of things, Artificial Intelligence and Machine Learning","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132359926","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

Robotic Mushroom Harvesting by Employing Probabilistic Road Map and Inverse Kinematics 基于概率路线图和逆运动学的蘑菇采摘机器人

BOHR International Journal of Internet of things, Artificial Intelligence and Machine Learning Pub Date : 1900-01-01 DOI: 10.54646/bijiam.001

Mohanan M. G., Salgaonkar Ambuja

引用次数: 1

An Internet – of – Things enabled Smart Fire Fighting System 物联网智能消防系统

BOHR International Journal of Internet of things, Artificial Intelligence and Machine Learning Pub Date : 1900-01-01 DOI: 10.54646/bijiam.007

S. R, K. V., Nitheshkumar V., N. P., Parthasarathi N., Ponsankar T.

引用次数: 0

Transforming African Education Systems through the Application of IOT 通过物联网应用改变非洲教育体系

BOHR International Journal of Internet of things, Artificial Intelligence and Machine Learning Pub Date : 1900-01-01 DOI: 10.54646/bijiam.004

Angula Nikodemus

引用次数: 0

Role of Artificial Intelligence in Supply Chain Management 人工智能在供应链管理中的作用

BOHR International Journal of Internet of things, Artificial Intelligence and Machine Learning Pub Date : 1900-01-01 DOI: 10.54646/bijiam.010

Chaudhari Niraj C.

引用次数: 2

Transforming African education systems throughthe application of IoT 通过物联网应用改变非洲教育体系

BOHR International Journal of Internet of things, Artificial Intelligence and Machine Learning Pub Date : 1900-01-01 DOI: 10.54646/bijiam.2022.04

Nikodemus Angula

引用次数: 0