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Multiple disease prediction using Machine learning algorithms

Materials Today: Proceedings 2023年 80卷 3682-3685页

作者： Arumugam, K. Naved, Mohd Shinde, Priyanka P. Leiva-Chauca, Orlando Huaman-Osorio, Antonio Gonzales-Yanac, Tatiana Department of Computer Science Karpagam Academy of Higher Education Tamilnadu Coimbatore India Department of Business Analytics Jagannath University Delhi-NCR India Master of Computer Application Department Government College of Engineering Maharashtra Karad415124 India Administration and Tourism Faculty Universidad Nacional Santiago Antúnez de Mayolo Huaraz Peru Economics and Accounting Faculty Universidad Nacional Santiago Antúnez de Mayolo Huaraz Peru

Data mining for healthcare is an interdisciplinary field of study that originated in database statistics and is useful in examining the effectiveness of medical therapies. Machine learning and data visualization Diabetes-related heart disease is a kind of heart disease that affects diabetics. Diabetes is a chronic condition that occurs when the pancreas fails to produce enough insulin or when the body fails to properly use the insulin that is produced. Heart disease, often known as cardiovascular disease, refers to a set of conditions that affect the heart or blood vessels. Despite the fact that various data mining classification algorithms exist for predicting heart disease, there is inadequate data for predicting heart disease in a diabetic individual. Because the decision tree model consistently beat the naive Bayes and support vector machine models, we fine-tuned it for best performance in forecasting the likelihood of heart disease in diabetes individuals. © 2021

关键词： Data mining

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Adaptive Deep Learning Model to Enhance Smart Greenhouse Agriculture

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computers, Materials & Continua 2023年第11期77卷 2545-2564页

作者： Medhat A.Tawfeek Nacim Yanes Leila Jamel Ghadah Aldehim Mahmood A.Mahmood Department of Computer Science College of Computer and Information SciencesJouf UniversityP.O.Box 72314SkakaSaudi Arabia Department of Computer Science Faculty of Computers and InformationMenoufia UniversityP.O.Box 6121890MenoufiaEgypt Department of Software Engineering College of Computer and Information SciencesJouf UniversityP.O.Box 72314SkakaSaudi Arabia RIADI Laboratory La Manouba UniversityP.O.Box2010ManoubaTunisia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems and Technology Faculty of Graduate Studies and ResearchCairo UniversityP.O.Box 3753450GizaEgypt

The trend towards smart greenhouses stems from various factors,including a lack of agricultural land area owing to population concentration and housing construction on agricultural land,as well as water *** study proposes building a full farming adaptation model that depends on current sensor readings and available datasets from different agricultural research *** proposed model uses a one-dimensional convolutional neural network(CNN)deep learning model to control the growth of strategic crops,including cucumber,pepper,tomato,and *** proposed model uses the Internet of Things(IoT)to collect data on agricultural operations and then uses this data to control and monitor these operations in real *** helps to ensure that crops are getting the right amount of fertilizer,water,light,and temperature,which can lead to improved yields and a reduced risk of crop *** dataset is based on data collected from expert farmers,the photovoltaic construction process,agricultural engineers,and research *** experimental results showed that the precision,recall,F1-measures,and accuracy of the one-dimensional CNN for the tested dataset were approximately 97.3%,98.2%,97.25%,and 97.56%,*** new smart greenhouse automation system was also evaluated on four crops with a high turnover *** system has been found to be highly effective in terms of crop productivity,temperature management and water conservation.

关键词： Greenhouse wireless sensor network deep learning Internet of Things strategic crops monitoring smart irrigation

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Wearable sensors for telehealth based on emerging materials and nanoarchitectonics

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npj Flexible Electronics 2023年第1期7卷 293-306页

作者： Jayraj V.Vaghasiya Carmen C.Mayorga-Martinez Martin Pumera Center for Advanced Functional Nanorobots Department of Inorganic ChemistryFaculty of Chemical TechnologyUniversity of Chemistry and Technology PragueTechnická516628PragueCzech Republic Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava17.listopadu 2172/1570800 OstravaCzech Republic

Wearable sensors have made significant progress in sensing physiological and biochemical markers for *** monitoring vital signs like body temperature,arterial oxygen saturation,and breath rate,wearable sensors provide enormous potential for the early detection of *** recent years,significant advancements have been achieved in the development of wearable sensors based on two-dimensional(2D)materials with flexibility,excellent mechanical stability,high sensitivity,and accuracy introducing a new approach to remote and real-time health *** this review,we outline 2D materials-based wearable sensors and biosensors for a remote health monitoring *** review focused on five types of wearable sensors,which were classified according to their sensing mechanism,such as pressure,strain,electrochemical,optoelectronic,and temperature sensors.2D material capabilities and their impact on the performance and operation of the wearable sensor are *** fundamental sensing principles and mechanism of wearable sensors,as well as their applications are *** review concludes by discussing the remaining obstacles and future opportunities for this emerging telehealth *** hope that this report will be useful to individuals who want to design new wearable sensors based on 2D materials and it will generate new ideas.

关键词： wear Wear sensors

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Artificial Intelligent Applications in Smart Cities

Artificial Intelligent Applications in Smart Cities

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Smart Information Systems and Technologies (SIST), IEEE International Conference on

作者： Ruhi Zafer Caglayan Korhan Kayisli Resul Coteli Ruslan Omirgaliyev Nurkhat Zhakiyev Electrical-Electronic Engineering Faculty of Engineering Gazi University Ankara Turkiye Computer Engineering Faculty of Engineering TED University Ankara Turkiye Energy Engireering Faculty of Technology Firat University Elazig Turkiye Department of Computer Engineering Astana IT University Astana Kazakhstan Department of Science and Innovation Astana IT University Astana Kazakhstan

ISBN: (数字)9798350374865

ISBN: (纸本)9798350374872

History shows us that people move towards a more stable lifestyle after undergoing significant changes. During these times, the way communities and buildings were designed started to change too. Different periods in history are known for their standout moments, each bringing its own set of needs and tech breakthroughs. Nowadays, tech keeps evolving. People nowadays aim for a more straightforward life, and technology has been shaping up to meet this need. We live in times highlighted by the rise of smart technology, like artificial intelligence and the Internet of Things, which not only make daily tasks easier but also transform how we think about our cities and personal spaces. This piece focuses on exploring the smart city idea, touching upon various associated subjects. As we delve into these areas, we'll also look at some important research related to them.

关键词： Accuracy Smart cities Reviews Buildings Transforms Internet of Things History

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Office Building Energy Community: A Case Study on Optimal Market Operation and Grid Impact 9

Office Building Energy Community: A Case Study on Optimal Ma...

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9th IEEE International Smart Cities Conference, ISC2 2023

作者： Lepisto, Juhani Forcan, Jovana Forcan, Miodrag Heine, Pirjo Helen Electricity Network Helsinki Finland University of East Sarajevo Faculty of Philosophy Department of Computer Science and Systems East Sarajevo Bosnia and Herzegovina University of East Sarajevo Faculty of Electrical Engineering Department of Electroenergetics East Sarajevo Bosnia and Herzegovina

ISBN: (纸本)9798350397758

In this paper, we study an office building connected to an urban distribution network from an energy community perspective. The office building and its electrical equipment have a real-world counterpart from which we construct synthetic time series based on the historical measurement data. The office building involves three stakeholders and their assets. The central stakeholders are the owner and the tenant of the building. The third stakeholder is the operator of flexible assets. The smartness of the office building is related to the control capabilities of its assets. These controllable assets are the battery energy storage system (BESS) and the electric vehicles (EV) charging system (EVCS) that both can operate in a flexible manner in response to energy price fluctuations and participate in a reserve market. We proposed different cases where either the owner or the tenant is paying the EV charging, and where the flexibility of the EV charging is either active or not. We show that, when the owner pays for the energy usage of the EVCS and the tenant is using it, there is a beneficial case to form a coalition between them and share the benefits from the flexibility, as otherwise there is no incentive for the tenant to be flexible in the first place. We conducted operative cost-benefit calculations where we shared the community benefits according to the Shapley value and simulated the power flows at the building's connection point based on the market operations. We found that harnessing benefits from the two market operations had significant effects on the power flows at the connection point. This was true when operating under the variable energy prices and in the reserve market. © 2023 IEEE.

关键词： Office buildings

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An enhanced mechanism for detection of spam emails by deep learning technique with bio-inspired algorithm

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e-Prime - Advances in Electrical engineering, Electronics and Energy 2024年 8卷

作者： Nicholas, Neomi Nelin Nirmalrani, V. Faculty of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

This study aims to create a highly effective system for classifying email spam, with the key objective of improving performance and accuracy in classification. Rigorous pre-processing techniques, including lemmatization and tokenization, are applied to refine the dataset. The impact of optimal feature selection on deep learning algorithm stability with varying training datasets is investigated and to improve classifier performance, pre-stages such as feature extraction and selection using bag of words are utilized. The hybrid model is further enhanced by combining feature extraction and classification algorithms with optimization. The inclusion of multiple features, such as n-gram features, addresses the 'Curse of Dimensionality,' and optimal feature selection is employed to improve the spam detection process. The proposed system undergoes three main modifications: multiple feature extraction, feature selection, and an enhanced hybrid model, all aimed at improving spam detection accuracy. The e-mail classification phase involves mapping between training and testing sets, with deep learning algorithms utilized for feature extraction and classification. The recently introduced Sand Cat Swarm Optimization algorithm is employed to refine weights during each epoch, enhancing accuracy and minimizing loss. The proposed system extends its capabilities to identify phishing attacks within the network, offering a comprehensive approach to email security. © 2024

关键词： Bag of words Curse of dimensionality Email spam classification Optimization algorithm Sand Cat Swarm Tokenization

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Significance of deep learning for Covid-19: state-of-the-art review

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Research on Biomedical engineering 2022年第1期38卷 243-266页

作者： Nayak, Janmenjoy Naik, Bighnaraj Dinesh, Paidi Vakula, Kanithi Dash, Pandit Byomakesha Pelusi, Danilo K Kotturu AP Tekkali532201 India Department of Computer Application Veer Surendra Sai University of Technology Odisha Burla768018 India Department of Computer Science and Engineering Sri Sivani College of Engineering AP Srikakulam532402 India Faculty of Communication Sciences University of Teramo Coste Sant&#39 Agostino Campus Teramo Italy

Purpose: The appearance of the 2019 novel coronavirus (Covid-19), for which there is no treatment or a vaccine, formed a sense of necessity for new drug discovery advances. The pandemic of NCOV-19 (novel coronavirus-19) has been engaged as a public health disaster of overall distress by the World Health Organization. Different pandemic models for NCOV-19 are being exploited by researchers all over the world to acquire experienced assessments and impose major control measures. Among the standard techniques for NCOV-19 global outbreak prediction, epidemiological and simple statistical techniques have attained more concern by researchers. Insufficiency and deficiency of health tests for identifying a solution became a major difficulty in controlling the spread of NCOV-19. To solve this problem, deep learning has emerged as a novel solution over a dozen of machine learning techniques. Deep learning has attained advanced performance in medical applications. Deep learning has the capacity of recognizing patterns in large complex datasets. They are identified as an appropriate method for analyzing affected patients of NCOV-19. Conversely, these techniques for disease recognition focus entirely on enhancing the accurateness of forecasts or classifications without the ambiguity measure in a decision. Knowing how much assurance present in a computer-based health analysis is necessary for gaining clinicians’ expectations in the technology and progress treatment consequently. Today, NCOV-19 diseases are the main healthcare confront throughout the world. Detecting NCOV-19 in X-ray images is vital for diagnosis, treatment, and evaluation. Still, analytical ambiguity in a report is a difficult yet predictable task for radiologists. Method: In this paper, an in-depth analysis has been performed on the significance of deep learning for Covid-19 and as per the standard search database, this is the first review research work ever made concentrating particularly on Deep Learning for NC

关键词： Coronavirus

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EdgeAuth: An intelligent token-based collaborative authentication scheme

EdgeAuth: An intelligent token-based collaborative authentic...

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作者： Jiang, Xutong Dou, Ruihan He, Qiang Zhang, Xuyun Dou, Wanchun State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Faculty of Mathematics University of Waterloo Waterloo Canada Department of Computer Science and Software Engineering Swinburne University of Technology Melbourne Australia Department of Computing Macquarie University Sydney Australia College of Big Data and Intelligent Engineering Southwest Forestry University China

Edge computing is regarded as an extension of cloud computing that brings computing and storage resources to the network edge. For some Industrial Internet of Things (IIoT) applications such as supply-chain supervision and collaboration, Internet of Vehicles, real-time video analysis and so forth, users should be authenticated before visiting the geographically distributed edge servers. Limited by the considerable latency between the cloud and edge servers, and the limited capacity of edge servers, it is infeasible to copy the authentication method from cloud servers when users are authenticated in edge servers. In view of this challenge, this paper proposes a novel token-based authentication scheme, named EdgeAuth, that enables fast edge user authentication through collaboration among cloud servers and edge servers. Under the EdgeAuth scheme, edge servers can rapidly verify the credentials of users who have been authenticated by the cloud server. EdgeAuth can also protect users from a series of authentication attacks, for example, the replay attack, DoS attack and man-in-the-middle attack. The results of experiments conducted on a simulated edge computing environment validate the usefulness of EdgeAuth through a comparison in latency and throughput against two baseline schemes. © 2023 John Wiley & Sons Ltd.

关键词： Authentication

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Blockchain based Secure System for the Internet of Drones (IoD) 6

Blockchain based Secure System for the Internet of Drones (I...

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6th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2022

作者： Maurya, Sudhanshu Rauthan, M.M.S. Verma, Rohan Ahmad, R. Badlishah Hemvati Nandan Bahuguna Garhwal University Department of Computer Science and Engineering Uttarakhand Srinagar India Universiti Malaysia Perlis Faculty of Electronic Engineering and Technology Perlis Malaysia

ISBN: (纸本)9781665482714

At present, drones are being used at a higher rate than ever. Frequently, they have been often used in military services. Therefore, it is the need of the hour to increase the smartness, privacy, and security of drones as they provide confidentiality and validity in the transmission of data. This research study has suggested and implemented a blockchain-based secure system for the internet of drones. It provides seamless authentication and registration for drones with better data storage security. The proposed method is evaluated on dummy drones to check the security and confidentiality of the system. © 2022 IEEE.

关键词： Drones

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Brain Tumor Segmentation in Multimodal MRI Using U-Net Layered Structure

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computers, Materials & Continua 2023年第3期74卷 5267-5281页

作者： Muhammad Javaid Iqbal Muhammad Waseem Iqbal Muhammad Anwar Muhammad Murad Khan Abd Jabar Nazimi Mohammad Nazir Ahmad Department of Information Technology The Superior UniversityLahorePakistan Department of Software Engineering The Superior UniversityLahorePakistan Department of Information Sciences Division of Science and TechnologyUniversity of EducationLahorePakistan Department of Computer Science Government College UniversityFaisalabadPakistan Deptment of Oral and Maxillofacial Surgery Faculty of DentistryUniversiti Kebangsaan MalaysiaKuala LumpurMalaysia Institute of IR4.0 Universiti Kebangsaan MalaysiaBangiSelangorMalaysia

The brain tumour is the mass where some tissues become old or damaged,but they do not die or not leave their *** brain tumour masses occur due to malignant *** tissues must die so that new tissues are allowed to be born and take their *** segmentation is a complex and time-taking problem due to the tumour’s size,shape,and appearance *** finding such masses in the brain by analyzing Magnetic Resonance Images(MRI)is a crucial task for experts and *** could not work for large volume images simultaneously,and many errors occurred due to overwhelming image *** main objective of this research study is the segmentation of tumors in brain MRI images with the help of digital image processing and deep learning *** research study proposed an automatic model for tumor segmentation in MRI *** proposed model has a few significant steps,which first apply the pre-processing method for the whole dataset to convert Neuroimaging Informatics Technology Initiative(NIFTI)volumes into the 3D NumPy *** the second step,the proposed model adopts U-Net deep learning segmentation algorithm with an improved layered structure and sets the updated *** the third step,the proposed model uses state-of-the-art Medical Image Computing and computer-Assisted Intervention(MICCAI)BRATS 2018 dataset withMRI modalities such as T1,T1Gd,T2,and Fluidattenuated inversion recovery(FLAIR).Tumour types in MRI images are classified according to the tumour *** of these masses carried by state-of-the-art approaches such that the first is enhancing tumour(label 4),edema(label 2),necrotic and non-enhancing tumour core(label 1),and the remaining region is label 0 such that edema(whole tumour),necrosis and *** proposed model is evaluated and gets the Dice Coefficient(DSC)value for High-grade glioma(HGG)volumes for their test set-a,test set-b,and test set-c 0.9795, 0.9855 and 0.9793, respectively.

关键词： Brain tumour segmentation magnetic resonance images modalities dice coefficient low-grade glioma U-Net

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