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Detection of Antibiotic Constituent in Aspergillus flavus Using Quantum Convolutional Neural Network 基于量子卷积神经网络的黄曲霉抗生素成分检测
Int. J. E Health Medical Commun. Pub Date : 2023-04-14 DOI: 10.4018/ijehmc.321150
S. SannidhanM., J. Martis, Ramesh Sunder Nayak, Sunil Kumar Aithal, B. SudeepaK.
{"title":"Detection of Antibiotic Constituent in Aspergillus flavus Using Quantum Convolutional Neural Network","authors":"S. SannidhanM., J. Martis, Ramesh Sunder Nayak, Sunil Kumar Aithal, B. SudeepaK.","doi":"10.4018/ijehmc.321150","DOIUrl":"https://doi.org/10.4018/ijehmc.321150","url":null,"abstract":"Treatment of influenza and its complications is a major challenge for healthcare systems. Pyrazine is one drug used in treating influenza. Aspergillic acid is major antibiotic constituent in pyrazine compounds mined from Aspergillus flavus' final stage. This stage of flavus is detected through color change forming a pale-yellow crystal structure. Detection of the same is complex and demands an experienced fraternity to continuously monitor the growth of fungus and identify its color change. However, researches proved that the task needs to be perfect and a tiny human error leads to a catastrophe in antibiotic creation. To avoid these flaws, druggists make a huge investment on costly equipment for accurate detection. To overcome these drawbacks, this article proposes a hybrid quantum convolutional neural network that predicts various stages of the fungus from the microscope's sample. To train the network, about 47,000 samples were poised under typical lab settings. The proposed system was tested in usual conditions and positively isolated the mature samples with 96% efficiency.","PeriodicalId":375617,"journal":{"name":"Int. J. E Health Medical Commun.","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126499736","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
Computational Framework of Inverted Fuzzy C-Means and Quantum Convolutional Neural Network Towards Accurate Detection of Ovarian Tumors 基于倒模糊c均值和量子卷积神经网络的卵巢肿瘤精确检测计算框架
Int. J. E Health Medical Commun. Pub Date : 2023-04-13 DOI: 10.4018/ijehmc.321149
Ashwini Kodipalli, S. Fernandes, Santosh K. Dasar, Taha Ismail
{"title":"Computational Framework of Inverted Fuzzy C-Means and Quantum Convolutional Neural Network Towards Accurate Detection of Ovarian Tumors","authors":"Ashwini Kodipalli, S. Fernandes, Santosh K. Dasar, Taha Ismail","doi":"10.4018/ijehmc.321149","DOIUrl":"https://doi.org/10.4018/ijehmc.321149","url":null,"abstract":"Due to the advancements in the lifestyle, stress builds enormously among individuals. A few recent studies have indicated that stress is a major contributor for infertility and subsequent ovarian cancer among women of reproductive age. In view of this, the present study proposes a two-stage computational methodology to identify and segment the ovarian tumour and classify it as benign or malignant. Using computerized tomography images, the first stage involves image segmentation using inverted fuzzy c-Means clustering, and second stage consists of deep quantum convolutional neural network in order to detect the tumours. The efficacy of the proposed method is demonstrated using in-house clinically collected dataset by comparing the results with the state-of-the-art methods. The experimental results confirm that the proposed approach outperforms the existing fuzzy C means algorithm by achieving the average Jaccard score of (0.65, 0.84, 0.79) (min, max, avg) and Dice score of (0.70, 0.83, 0.77) (min, max, avg), classification result of 78% for benign and 70.03% for malignant tumours. The classification results using the variant of convolutional neural network (CNN) model ResNet16 are compared with the quantum convolutional neural networks (QCNN) and obtained the classification performance of 87.02% for benign and 79.4% for malignant tumours and 84.4% for benign and 77.03% for malignant tumours respectively.","PeriodicalId":375617,"journal":{"name":"Int. J. E Health Medical Commun.","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129973518","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: 7
A Comprehensive Survey on Quantum Machine Learning and Possible Applications 量子机器学习及其应用综述
Int. J. E Health Medical Commun. Pub Date : 2022-10-31 DOI: 10.4018/ijehmc.315730
Muhammad Umer, Muhammad Imran Sharif
{"title":"A Comprehensive Survey on Quantum Machine Learning and Possible Applications","authors":"Muhammad Umer, Muhammad Imran Sharif","doi":"10.4018/ijehmc.315730","DOIUrl":"https://doi.org/10.4018/ijehmc.315730","url":null,"abstract":"Machine learning is a branch of artificial intelligence that is being used at a large scale to solve science, engineering, and medical tasks. Quantum computing is an emerging technology that has a very high computational ability to solve complex problems. Classical machine learning with traditional systems has some limitations for problem-solving due to a large amount of data availability. Quantum machine learning has high performance and computational ability that can effectively be used to solve computation tasks. This study reviews the latest articles in quantum computing and quantum machine learning. Building blocks of quantum computing and different flavors of quantum algorithms are also discussed. The latest work in quantum neural networks is also presented. In the end, different possible applications of quantum computing are also discussed.","PeriodicalId":375617,"journal":{"name":"Int. J. E Health Medical Commun.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117204203","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
EEG Forecasting With Univariate and Multivariate Time Series Using Windowing and Baseline Method 基于窗和基线法的单变量和多变量时间序列脑电预测
Int. J. E Health Medical Commun. Pub Date : 2022-10-31 DOI: 10.4018/ijehmc.315731
K. TharaD., B. Premasudha, T. V. Murthy, Syed Ahmad Chan Bukhari
{"title":"EEG Forecasting With Univariate and Multivariate Time Series Using Windowing and Baseline Method","authors":"K. TharaD., B. Premasudha, T. V. Murthy, Syed Ahmad Chan Bukhari","doi":"10.4018/ijehmc.315731","DOIUrl":"https://doi.org/10.4018/ijehmc.315731","url":null,"abstract":"People suffering from epilepsy disorder are very much in need for precautionary measures. The only way to provide precaution to such people is to find some methods which help them to know in advance the occurrence of seizures. Using Electroencephalogram, the authors have worked on developing a forecasting method using simple LSTM with windowing technique. The window length was set to five time steps; step by step the length was increased by 1 time step. The number of correct predictions increased with the window length. When the length reached to 20 time steps, the model gave impressive results in predicting the future EEG value. Past 20 time steps are learnt by the neural network to forecast the future EEG in two stages; in univariate method, only one attribute is used as the basis to predict the future value. In multivariate method, 42 features were used to predict the future EEG. Multivariate is more powerful and provides the prediction which is almost equal to the actual target value. In case of univariate the accuracy achieved was about 70%, whereas in case of multivariate method it was 90%.","PeriodicalId":375617,"journal":{"name":"Int. J. E Health Medical Commun.","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122792643","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
Epidemic Healthcare Kiosk: A Social Economic Remote Solution Using IoT 流行病医疗亭:使用物联网的社会经济远程解决方案
Int. J. E Health Medical Commun. Pub Date : 2022-10-31 DOI: 10.4018/ijehmc.313912
D. J. Jagannath, D. J. Dolly, J. Peter
{"title":"Epidemic Healthcare Kiosk: A Social Economic Remote Solution Using IoT","authors":"D. J. Jagannath, D. J. Dolly, J. Peter","doi":"10.4018/ijehmc.313912","DOIUrl":"https://doi.org/10.4018/ijehmc.313912","url":null,"abstract":"One of the most difficult tasks for the physicians is to analyze, manage, and plan suitable diagnoses and treatments for society, especially in an epidemic situation like Corona virus disease (COVID-19). Hence, the mortality rate shoots up. The ultimate reason for such pathetic situation is due to large mass of people being infected, lack of physicians and testing staff, and the threat of physicians themselves being infected while testing patients. This article proposes a solution to tackle this major issue worldwide. This article portrays the methodology of an IoT-interfaced epidemic healthcare kiosk (EHK)-intelligent monitoring system to plan and manage epidemics. The EHK is a non-invasive data acquisition system that consists of several sensors that can record the physiological measurements of the EHK user. The measured parameters are computed using quantum machine learning techniques. The proposed ideology can reduce the mortality rate, control the epidemic, and moreover, provide safety to citizens and physicians.","PeriodicalId":375617,"journal":{"name":"Int. J. E Health Medical Commun.","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129538339","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
Categorical Data Clustering Using Harmony Search Algorithm for Healthcare Datasets 基于和谐搜索算法的医疗数据集分类数据聚类
Int. J. E Health Medical Commun. Pub Date : 2022-08-11 DOI: 10.4018/ijehmc.309440
Abha Sharma, Pushpendra Kumar, K. S. Babulal, Ahmed J. Obaid, Harshita Patel
{"title":"Categorical Data Clustering Using Harmony Search Algorithm for Healthcare Datasets","authors":"Abha Sharma, Pushpendra Kumar, K. S. Babulal, Ahmed J. Obaid, Harshita Patel","doi":"10.4018/ijehmc.309440","DOIUrl":"https://doi.org/10.4018/ijehmc.309440","url":null,"abstract":"Healthcare analytics provide many benefits in healthcare dashboard systems. Healthcare datasets majorly contains categorical attributes. This paper proposed an optimized clustering for healthcare dataset named harmony search based categorical clustering (HSCC). The existing k-modes clustering algorithm is one of the well-known categorical data-clustering algorithm. Since the k-modes algorithm produces local optimal clusters. Generally, researchers use genetic algorithm (GA) based clustering algorithms to converge locally optimal solutions to global optimal solutions. GA has some deficiencies such as premature convergence with low speed. In this paper, harmony search (HS) optimization algorithm used to optimize clustering results. The result shows the proposed HSCC algorithm produced global optimized solution, unbiased and matured results. HSCC produces 98% accuracy for dental and 71% for lung cancer dataset. While GACC produces 95% and 65% accuracy for dental dataset and lung cancer dataset.","PeriodicalId":375617,"journal":{"name":"Int. J. E Health Medical Commun.","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128960958","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
The Terahertz Channel Modeling in Internet of Multimedia Design In-Body Antenna 多媒体网络环境下的太赫兹信道建模
Int. J. E Health Medical Commun. Pub Date : 2022-08-11 DOI: 10.4018/ijehmc.309437
Bokang Francis Maphathe, P. Thakur, G. Singh, Hashimu E. Iddi
{"title":"The Terahertz Channel Modeling in Internet of Multimedia Design In-Body Antenna","authors":"Bokang Francis Maphathe, P. Thakur, G. Singh, Hashimu E. Iddi","doi":"10.4018/ijehmc.309437","DOIUrl":"https://doi.org/10.4018/ijehmc.309437","url":null,"abstract":"In this paper, the authors have emphasized on the perspectives of the Terahertz channel modeling in Internet of multimedia nano things (IoMNT) networks. A modulation technique targeting body-centric network is discussed. An analogy of a real Terahertz antenna is developed within a terahertz multi-layer modelling channel for a human skin tissue. As a result, the investigation of how signals at THz frequency band interact and transmit within the skin biomaterial. The human skin model used to collect data was selected to have four layers: epidermis, dermis, blood, and hypodermis, with the depth of the layers varying between normal human body values. It is revealed from the literature that the frequency and content have a substantial impact on path failure. The estimated path loss could thus differ considerably, but for a human skin model with depths of 0.21 mm, 1.23 mm, 1.38 mm, and 3.76 mm, the frequencies of 0.5-1.5 THz at the end distance resulted in a path loss estimated about 250-350 dB.","PeriodicalId":375617,"journal":{"name":"Int. J. E Health Medical Commun.","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130130539","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
Simulation of the Lightweight Blockchain Technique Based on Privacy and Security for Healthcare Data for the Cloud System 基于隐私和安全的轻量级区块链技术在云系统医疗数据中的模拟
Int. J. E Health Medical Commun. Pub Date : 2022-08-11 DOI: 10.4018/ijehmc.309436
P. Rani, Sonia Verma, S. Yadav, Bipin Kumar Rai, Mahaveer Singh Naruka, Devendra Kumar
{"title":"Simulation of the Lightweight Blockchain Technique Based on Privacy and Security for Healthcare Data for the Cloud System","authors":"P. Rani, Sonia Verma, S. Yadav, Bipin Kumar Rai, Mahaveer Singh Naruka, Devendra Kumar","doi":"10.4018/ijehmc.309436","DOIUrl":"https://doi.org/10.4018/ijehmc.309436","url":null,"abstract":"Information about healthcare is derived from healthcare data. Healthcare data sharing helps make healthcare systems more efficient as well as improving healthcare quality. Patients should own and control healthcare information, one of their most valuable assets, instead of letting data be spread out among health care providers differ. This protects data from being shared between healthcare systems and privacy. Public ledger accompanied by a decentralized network of peer's compromises patient has been demonstrated to be able to achieve trusted, auditable computing by blockchain. The use of access control and cryptographic primitives are insufficient in addressing modern cyber threats all privacy and security concerns associated with a cloud-based environment. In this paper, the authors proposed a lightweight blockchain technique based on privacy and security for healthcare data for the cloud system. The cost-effectiveness of our system's smart contracts is evaluated, as well as the procedures used for data processing in order to encrypt and pseudonymize patient data.","PeriodicalId":375617,"journal":{"name":"Int. J. E Health Medical Commun.","volume":"159 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134444913","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: 27
Patient-Centric Multichain Healthcare Record 以患者为中心的多链医疗记录
Int. J. E Health Medical Commun. Pub Date : 2022-08-11 DOI: 10.4018/ijehmc.309439
Bipin Kumar Rai, Sumrah Fatima, Kumar Satyarth
{"title":"Patient-Centric Multichain Healthcare Record","authors":"Bipin Kumar Rai, Sumrah Fatima, Kumar Satyarth","doi":"10.4018/ijehmc.309439","DOIUrl":"https://doi.org/10.4018/ijehmc.309439","url":null,"abstract":"In this paper, the authors have amplified the concept that EHRs need to be patient-centric and patient-driven, that is the patient should be the real owner as well as the manager of his medical records. The authors propose patient-centric multichain healthcare record (PCMHR) that implements health records using smart contracts on ethereum blockchain and also utilizes the multichain framework - Polygon. PCMHR can concurrently implement blockchain functionality while addressing the concerns of interoperability among authorized hospitals and patient health information confidentiality that damages our healthcare system. The authors propose a solution to fully decentralize the current medical healthcare system by storing PCMHR on IPFS (InterPlanetary File System) to resolve the limitation of blockchain-based applications in scalability and high cost. The authors have depicted the cost and time analysis of transactions on the polygon framework to give a clear view of this multichain framework and its advantages over the ethereum blockchain.","PeriodicalId":375617,"journal":{"name":"Int. J. E Health Medical Commun.","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117273212","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
HuDA_COVID Human Disposition Analysis During COVID-19 Using Machine Learning 使用机器学习分析COVID-19期间的人类倾向
Int. J. E Health Medical Commun. Pub Date : 2022-07-01 DOI: 10.4018/IJEHMC.20220701.oa1
Charu Gupta, D. Gaur, Prateek Agrawal, Deepali Virmani
{"title":"HuDA_COVID Human Disposition Analysis During COVID-19 Using Machine Learning","authors":"Charu Gupta, D. Gaur, Prateek Agrawal, Deepali Virmani","doi":"10.4018/IJEHMC.20220701.oa1","DOIUrl":"https://doi.org/10.4018/IJEHMC.20220701.oa1","url":null,"abstract":"Coronavirus has greatly impacted various aspects of human life, including human psychology & human disposition. In this paper, we attempted to analyze the impact of the COVID-19 pandemic on human health. We propose Human Disposition Analysis during COVID-19 using machine learning (HuDA_COVID), where factors such as age, employment, addiction, stress level are studied for human disposition analysis. A mass survey is conducted on individuals of various age groups, regions & professions, and the methodology achieved varied accuracy ranges of 87.5% to 98%. The study shows people are worried about lockdown, work & relationships. Furthermore, 23% of the respondents have not had any effect. 45% and 32% have had positive and negative effects, respectively. It is a novel study in human disposition analysis in COVID-19 where a novel weighted assignment indicating the health status is also proposed. HuDA_COVID clearly indicates a need for a methodical approach towards the human psychological needs to help the social organizations formulating holistic interventions for affected individuals.","PeriodicalId":375617,"journal":{"name":"Int. J. E Health Medical Commun.","volume":"279 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116050893","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
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