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Data science and engineering in Healthcare, Medicine and Biology, IEEE EMBS Special Topic Conference on

作者： Shereen Afifi Andrew Emad Radwa Taha Mirza Baig Assistant Professor in Computer science and Engineering German University in Cairo Egypt Senior student in Computer science and Engineering German University in Cairo Egypt Master Candidate in Computer science and Engineering German University in Cairo Egypt Senior Solutions Specialist in Orion Health Auckland New Zealand

Internet of Things (IoT) is gaining much popularity nowadays, especially in the field of healthcare, where different wearables, sensors and data analysis-based systems are connected together. This research work proposes an IoT-based system/application dedicated for continuous monitoring of the patient’s health parameters and vital signs. The designed application can be used by both the patient and the doctor as the doctor can track the patient’s parameters remotely in real-time to facilitate the diagnosis and to early detect patient’s health deterioration. It also enables patients to spend more time in the comfort of their own homes and communicate with their care facilities, aiming to improve the patient’s independent living and support activities of daily life. The biosensors in the smart watch or the mobile phone are utilized to measure the health parameters of the patient, which are then used by the devloped application for further processing, analysis and efficient visualization using charts and diagrams. A notification is sent to the monitoring doctor to facilitate rapid response once any of the patient’s vital signs exceeds the normal range. The developed application could be integrated within an existing hospital system or could be made available to individuals/patients via smartphones for providing support at *** Relevance: Doctors are always in need to have exact health information for their patients to receive care and therapy in a timely manner. The task of ongoing monitoring of patients’ vital signs is carried out by nurses in the interim, but the availability of a skilled and sizable team of nurses is a major concern. Thus, we propose an efficient continuous monitoring system that automates and facilitates the process of remote and continuous monitoring of patients, targeting early detection of patient deterioration while supporting independent living.

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A novel hybrid machine learning model for shopping trip estimation: A case study of Tehran, Iran

Transportation Engineering

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Transportation engineering 2023年 14卷

作者： Dasoomi, MohammadHanif Naderan, Ali Allahviranloo, Tofigh PhD. Candidate Department of Civil Engineering Science and Research Branch Islamic Azad University Tehran14778-93855 Iran Assistant Professor Department of Civil Engineering Science and Research Branch Islamic Azad University Tehran14778-93855 Iran Professor Department of Mathematical Sciences Science and Research Branch Islamic Azad University Tehran14778-93855 Iran Professor Faculty of Engineering and Natural Sciences Istinye University Istanbul Turkey

Online and offline shopping trips have a profound impact on various facets of urban life, including e-commerce, transportation systems, and sustainability. To assess the factors shaping consumers' decisions, we introduce a novel hybrid machine learning model that integrates the Gray Wolf Optimization (GWO) algorithm with a deep Convolutional Neural Network (CNN). This model is applied to predict shopping behavior based on a survey of 1000 active e-commerce users residing in areas 2 and 5 of Tehran. These individuals have made successful purchases through both online and offline services during the final 20 days of 2021. The GWO algorithm plays a pivotal role in selecting optimal features and hyperparameters for the deep Convolutional Neural Network, which is a powerful deep learning model for image recognition and classification. Notably, our model achieves an impressive accuracy of 97.81% while maintaining a MSE of 0.325, having identified seven out of ten key features as the most influential. To gage the effectiveness of our approach, we conduct a comparative analysis with alternative methods. The results unequivocally showcase the superiority of our proposed algorithm, which attains an accuracy of 97.81%. In contrast, other models such as CNN, LSTM, MLP, DT, and KNN yield accuracies of 95.63%, 94.04%, 90.12%, 86.49%, and 80.16%, respectively. This study offers valuable insights for transportation planners, e-commerce managers, and policymakers. Its primary objective is to assist them in formulating effective strategies aimed at reducing transportation costs, curbing pollutant emissions, mitigating urban traffic congestion, and enhancing user satisfaction all while fostering sustainable development. © 2023 The Author(s)

关键词： Neural network models

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Assessment of Risk Associated with Closure and Rehabilitation of Dump Sites

Assessment of Risk Associated with Closure and Rehabilitatio...

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Smart Cities 4.0, International Conference on

作者： Nouran Aly Gad Karim El-Dash Tarek Mahmoud Attia Shady Ahmed Dokhan Dept. construction engineering and management (Lecturer Assistant) Faculty of engineering Misr University for Science and Technology (MUST) Giza Egypt Dept. Civil engineering (Professor of Construction Management) Faculty of Engineering benha University Giza Egypt Dept. Construction engineering and management (Professor of Construction Management) Housing & Building National Research Center Giza Egypt Dept. Civil engineering (Assistant Professor of Construction Management) Faculty of Engineering benha University Giza Egypt

ISBN: (数字)9781665474306

ISBN: (纸本)9781665474313

Egypt's government and civil society are addressing waste management issues, with an estimated 810,000 tons of solid waste collected annually. The government has issued warnings and initiated recycling initiatives for both legal and illegal dumpsites. This paper focuses on recent initiatives for the closure of dumpsites in megacities in Egypt. The research methodology has been developed by reviewing the most valuable international research for identification and assessment of risks that contribute to the closure and rehabilitation of open dumpsites in Egypt, as well as questionnaires and interviews with the key stakeholders involved in the closure activities. The closure phases are sorted into six classified sources of risk: site technical risks (STR), social risks (SR), environmental and human health factors (EHHR), site rehabilitation risks (SRR), economic and financial risks (EFR), and strategic and construction partnering risks (SCPR), and 56 factors of risk. The research has found that each source classification has its own critical risks that should be considered as examples of which STR, SRR, EFR, and SCPR categories have the most critical risks. As presented in the case of Shabramnat, the Giza Dumpsite Checklist. The research encourages the authorities and key stakeholders to use the risk assessment list for the closure and rehabilitation of dumpsites in Egypt.

关键词： Waste management Economics Waste materials Smart cities Law Government Recycling

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Optimization of machining parameters in EDM using Taguchi based grey relational analysis

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Materials Today: Proceedings 2022年 82卷 43-46页

作者： Ganapathy S. Palanivendhan M. Susitra K. Balasubramanian P. Department of Mechanical Engineering Sri Sai Ram Engineering College Chennai 600044 India Centre for Automotive Materials Assistant Professor Department of Automobile Engineering SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu 603203 India Department of Computer Science and Engineering Sri Sai Ram Engineering College Chennai 600044 India Department of Mechanical Engineering A.V.C College of Engineering Mayiladuthurai 609305 India

The present investigation optimizes process parameters such tool size, peak current, pulse on time, and dielectric pressure in the EDM process. The material removal rate affects the machining operation's quality. The goal of the present work is to maximize material removal rate using Taguchi technique-based optimization of machining parameters. On an EDM, the experimental study for machining operations was carried out. For EN-31, machining operations are carried out using an orthogonal array with L9. The ANOVA technique was used to evaluate the experimental results. Using a technique called as grey relational analysis, the optimum conditions for material removal rate are determined. The optimum approach for a set of linked parameters is evaluated using the grey relation grade. © 2023 Elsevier Ltd. All rights reserved.

关键词： Anova EDM EN-31 GRA MRR

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Message Communication Challenges in Heterogeneous Architecture of Internet of Vehicles: A Review

Message Communication Challenges in Heterogeneous Architectu...

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International Conference on System Modeling & Advancement in Research Trends (SMART)

作者： Jitendra Kumar Y.D.S. Arya Akash Sanghi Inverters University Bareilly & Assistant Professor Teerthanker Mahaveer University Moradabad UP India Invertis University Bareilly UP India Department of Computer Science & Engineering Invertis University Bareilly UP India

ISBN: (数字)9798350380583

ISBN: (纸本)9798350380590

Inclusion of Internet of vehicles (IoV) an emerging technology in smart city for vehicular communication is covering the Internet of things (IoT) technology into vehicular networks for benefiting from omnipresent Internet connectivity. Smart city as a demand of new way of living of current and coming generation by gathering the information through the various electronic devices. The one of the key point in smart city is making traffic planning more secure and accurate by using the better message communication between vehicles. A huge number of researchers have been worked on vehicular networks for message communication but researchers are still having to do work on to achieve the higher data rate, faultless network connectivity, message scalability, message security and improved quality of services for message communication for making IoV more effective. IoV is more capable for comprehensively information processing that is collected from the vehicles, roads and surroundings and helps the drivers for driving the vehicles in safer zone. The Internet of Vehicles (IoV) model integrates vehicular intelligence in addition to leveraging ubiquitous vehicle connection for a multitude of applications. This research technology used in network communication including the smart city requirements, traffic control for increasing the life security.

关键词： Wireless communication Accuracy Smart cities Scalability Roads Traffic control Security Standards Internet of Vehicles Systematic literature review

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Developing a New MapReduce Framework with SVM-based Software Solution for Lyme Lesion Impact on Alzheimer's Disease Prevention

Developing a New MapReduce Framework with SVM-based Software...

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International Conference on Trends in Electronics and Informatics (ICEI)

作者： Saravanan. M.S G. Sreehitha Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences and Assistant Professor Sri Padmavati Mahila Visvavidyalayam Tirupathi Andhra Pradesh India

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

Alzheimer's disease has many symptoms to occur, one of the latest research studies identified that Lyme disease, caused by “Borrelia burgdorferi” also one of the reasons indirectly affect the neuroinflammation. The dataset used for this research study is supplied by many web sources, that are involved in feature extraction to apply on machine learning techniques for extracting meaningful knowledge. This work proposing a new MapReduce framework-based Support Vector Machine (SVM) model for better prediction of impact of Lyme on Alzheimer's disease. The MapReduce based framework using SVM model creates more impact on prediction of Alzheimer's disease at the earliest. The clinical data with images are collected from distributed environments and applied on preprocessing and feature extraction stages then applied to proposed MapReduce based SVM model for better accuracy and optimal classification for complex patters on Lyme image dataset. From these results the Alzheimer's risk factors are predicted for early treatment process. The results obtained by this study will give early decision making for highly available sources of treatment procedures and gives more insight on preventive care on each and every patient involved in diagnosis of Lyme disease prediction, lead to identify the Alzheimer's disease prediction.

关键词： Support vector machines Accuracy Machine learning algorithms Software algorithms Machine learning Predictive models Feature extraction Software Classification algorithms Alzheimer's disease

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IoT based Power and Water Theft Detection in Urban Cities

IoT based Power and Water Theft Detection in Urban Cities

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： Praful V. Nandankar G.N.R. Prasad M. Lakshminarayana Mr. Doddabasappa N G. Satish Ramya Maranan Assistant Professor Department of Electrical Engineering Government College of Nagpur Maharashtra India Sr. Asst. Professor Department of Master of Computer Application Chaitanya Bharathi Institute of Technology (A) Hyderabad India Assistant Professor Department of Medical Electronics Engineering M S Ramaiah Institute of Technology Karnataka India Assistant Professor School of Electrical and Electronic Engineering REVA University Bengaluru Karnataka India Associate Professor Department of Electrical and Electronics Engineering Sree Dattha Institute of Engineering and Science Sheriguda Ibrahimpatnam Hyderabad Telangana India Assistant Professor Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai India

Many people in metropolitan areas have experienced problems with energy and water shortages. Some people used modern technologies and advancements to theft water and power illegally. Because the planet absolutely needs both water and power. Currently, the Internet of Things (IoT), smart meters, smart pipelines, and smart homes are being heavily integrated to replace traditional systems for detecting theft activities. This can be digitizing both the meter readings and the data that has been gathered. Wireless technology may be used to collect and analyze the data, which helps to reduce the need for physical labor. In this project, we suggested creating an IoT-based system for water and power theft detection. The proposed project aims to design a system to monitor the amount of power and water used by the people and indicates if water and electricity theft activities happen. This research study offers a user interface system to help people to monitor whether anyone is stealing their water or power. This study employs a hardware like voltage sensors, current sensors, flow sensors, and additionally, the Arduino IoT cloud to enable remote control and monitor the water and power usage.

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A Robust Statistical CNNCTC-Based AI Model for Tracking and Monitoring COVID-19 1st

A Robust Statistical CNNCTC-Based AI Model for Tracking and ...

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1st International Virtual Conference on Industry 4.0, IVCI4.0 2020

作者： Chelliah, Balika J. Arunkumar, S. Prabavathi, R. Aarthi, B. Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Campus Chennai India Assistant Professor Sri Sairam Institute of Technology West Tambaram Chennai India

ISBN: (纸本)9789811612435

The world today battles the new coronavirus (COVID-19) spread that can lead to a major endemic or pandemic outbreak with devastating outcomes which has overturned all our usual calculus seemingly. Because of the unavailability of a vaccine and the serious extent of infectiousness, social distancing is perhaps the only way left to fight the infection. The tracking system is still a challenge for monitoring the COVID-19 affected person and the community spread. This leads to an epidemic situation, where the number of secondary infections keeps going higher and higher which intern leads to the difficulty of tracking and treating each infected person. An AI-based mathematical modelling is proposed for tracking and monitoring the pandemic outbreak. A GPS tracking system, geolocation using Fit bit and Wi-Fi tracking modules are developed to locate the secondary infections and the carriers. Calculation of real-time coordinates and the conditional probabilities with respect to time is done followed by the classification based on risk using CNN connectionist temporal classification (CTC) model, a type of neural network which is used to train the recurrent process. Monitoring the affected person using sensors like thermal sensors and a tracking chip inbuilt in a wristband for real-time updates. A comparative study is done on various classification models. Therefore, the system provides a robust model with 96.5% accuracy to combat this pandemic situation. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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Autonomous Vehicles for All?

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ACM Journal on Autonomous Transportation Systems 2024年第1期1卷 1-8页

作者： Sakib Mahmud Khan M Sabbir Salek Vareva Harris Gurcan Comert Eric A. Morris Mashrur Chowdhury Intelligent Transportation Systems Lead MITRE Corporation McLean VA Ph.D. Candidate Glenn Department of Civil Engineering Clemson University Clemson SC Vice President for Corporate Engagement O Analytics Incorporated Fairmont WV Associate Professor Computer Science Physics and Engineering Department Benedict College Columbia SC USA Professor Nieri Department of Construction Development and Planning Clemson University Clemson SC USA Professor Glenn Department of Civil Engineering Clemson University Clemson SC USA

The traditional build-and-expand approach is not a viable solution to keep roadway traffic rolling safely, so technological solutions, such as autonomous vehicles (AVs), are favored. AVs have considerable potential to increase the carrying capacity of roads, ameliorate the chore of driving, improve safety, provide mobility for those who cannot drive, and help the environment. However, they also raise concerns over whether they are socially responsible, taking into account issues such as fairness, equity, and transparency. Regulatory bodies have focused on AV safety, cybersecurity, privacy, and legal liability issues, but have failed to adequately address social responsibility. Thus, existing AV developers do not have to embed social responsibility factors in their proprietary technology. Adverse bias may therefore occur in the development and deployment of AV technology. For instance, an artificial intelligence-based pedestrian detection application used in an AV may, in limited lighting conditions, be biased to detect pedestrians who belong to a particular racial demographic more efficiently compared to pedestrians from other racial demographics. Also, AV technologies tend to be costly, with a unique hardware and software setup which may be beyond the reach of lower-income people. In addition, data generated by AVs about their users may be misused by third parties such as corporations, criminals, or even foreign governments. AVs promise to dramatically impact labor markets, as many jobs that involve driving will be made redundant. We argue that the academic institutions, industry firms, and government agencies overseeing AV development and deployment must act proactively to ensure that AVs serve all and do not increase the digital divide in our society.

关键词： Autonomous vehicles Social responsibility Fairness Equity Transparency

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Secure Localization Based Authentication (SLA) Strategy for Data Integrity in WNS

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computers, Materials & Continua 2021年第6期67卷 4005-4018页

作者： V.Manikandan M.Sivaram Amin Salih Mohammed V.Porkodi K.Shankar Assistant Professor Research Research CenterLebanese French UniversityErbil44001Iraq Vice President Lebanese French UniversityErbil44001Iraq Department of Information Technology College of Engineering and Computer ScienceLebanese French UniversityErbil44001Iraq Department of Computer Applications Alagappa UniversityKaraikudi630003India

Wireless Sensor Networks(WSN)has been extensively utilized as a communication model in Internet of Things(IoT).As well,to offer service,numerous IoT based applications need effective transmission over unstable *** ensure reliability,prevailing investigations exploit multiple candidate forwarders over geographic opportunistic routing in ***,these models are affected by crucial denial of service(DoS)attacks,where huge amount of invalid data are delivered intentionally to the receivers to disturb the functionality of ***,secure localization based authentication(SLA)is presented to fight against DoS attack,and to fulfil the need of reliability and *** examining state information,SLA projects a trust model to enhance efficacy of data ***,of the prevailing opportunistic protocols,SLA guarantees data integrity by modelling a trust based authentication,providing protection against DoS attackers and diminishing computational ***,this model acts as a verification strategy to accelerate?attackers and to handle *** strategy helps SLA in eliminating duplicate transmission and by continuous verification that results from conventional opportunistic *** is performed in a MATLAB environment that offers authentic and reliable delivery by consuming approximately 50%of the cost in contrast to other *** anticipated model shows better trade off in comparison to the prevailing ones.

关键词： Wireless sensor networks opportunistic routing secure localization based authentication denial of service computational cost

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