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EHR based Prediction and Diet Recommender For Diabetes using Blockchain

EHR based Prediction and Diet Recommender For Diabetes using...

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Smart Technologies and systems for Next Generation Computing (ICSTSN), International Conference on

作者： Poonguzhali N N. Amarabalan School of Computer and Systems Sciences Jawaharlal Nehru University New Delhi India Dept. of Electrical and Electronics Engineering Manakula Vinayagar Institute of Technology Puducherry India

ISBN: (数字)9798350391565

ISBN: (纸本)9798350391572

Diabetes is an illness which is triggered due to high blood sugar levels. Ignoring diabetes is undesirable since the condition can have adverse impacts on the heart, kidneys, cholesterol levels, eyes, and almost all other organs. Diabetes will be easier to manage the earlier it is detected. To do this, researchers use a variety of machine learning techniques to more accurately forecast the onset of early diabetes in human bodies or in its patients. By constructing models using datasets that are available, machine learning techniques produce more accurate predictions. In the proposed work, Random Forest Algorithm is used to predict diabetes, as this technique outperforms in terms of accuracy. Individuals with diabetes, maintaining an appropriate diet is crucial. The majority of them aim to assist users in managing their blood sugar levels. A person’s weekly meal preparation habits can be attributed to a myriad of intersecting elements, such as taste, healthfulness, financial constraints, personal preferences, and lifestyle. An individual might experience it difficult to choose what to eat. To assist with the management of diabetes, the proposed system advises using a customized meal planning strategy. The diet plan suggested in the system is based on decision tree technique which is diabetic-friendly that is also socially, economically, and culturally acceptable and that can satisfy all nutritional needs while being mindful of calories. Following the prediction of diabetes and the suggestion of a diet plan, the patient’s medical information is stored in the EHR for convenient access.

关键词： Heart Accuracy Machine learning algorithms Predictive models Prediction algorithms Diabetes Blockchains

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Evaluating the Impact of Data Cleaning for Improving Road Damage Detection Through Vision Transformer

Evaluating the Impact of Data Cleaning for Improving Road Da...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Andy Desman Kahlil Muchtar Maulisa Oktiana Maya Fitria Yuwaldi Away Fardian Fardian Hammam Riza Dept. of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh Indonesia Dept. of Electrical and Computer Engineering Department of Informatics Universitas Syiah Kuala The Master Program in Artificial Intelligence Universitas Syiah Kuala (USK) Banda Aceh Indonesia Department of Informatics BRIN (National Research and Innovation Agency) Indonesia The Master Program in Artificial Intelligence Universitas Syiah Kuala Banda Aceh Indonesia

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

The WHO predicts that by 2030 road accidents will be the 5th leading cause of death. Globally, road accidents account for 1.25 million casualties each year, and road defects cause 34% of these casualties. The road survey process in many countries have several challenges, one of which is detection using cameras that do not have a recognition system. In this study, a model with YOLOS architecture based on Vision Transformer trained on the RDD2022 dataset successfully recognizes road damage well, as indicated by the number of objects detected, bounding box on accurate objects, and the ability to recognize objects with inconsistent shadow and light inference. This research uses assessment parameters such as Average Precision (AP) and Average Recall (AR) to determine the overall performance of the model. The model achieves the highest AP value at Intersection of Union (IoU) 0.5, 0.75, and 0.5-0.95, worth 62.1%, 37.1%, and 36.2% respectively, and the highest AR value in Large, Medium, and Small Areas, worth 42.1%, 60.3%, and 75.4% respectively. The supplementary material can be found through this link: https://***/watch?v=LzkI2e_IORE.

关键词： YOLO Surveys computer vision Road accidents Roads Manuals Transformers Cameras Cleaning Tuning

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Evaluation of Meteorological Factors’ Effect on Daily Heart Rate Variability Over Five Years

Evaluation of Meteorological Factors’ Effect on Daily Heart...

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International Conference on Awareness Science and Technology (iCAST)

作者： Kota Kokubun Tianhui Li Wenxi Chen Dept. of Computer and Information Systems The University of Aizu Aizu-Wakamatsu Japan Division of Information System The University of Aizu Aizu-Wakamatsu Japan Dept. of Computer Science and Engineering The University of Aizu Aizu-Wakamatsu Japan

The aim of this study is to investigate the effect of meteorological factors on heart rate variability (HRV) derived from daily electrocardiogram (ECG) data. Daily meteorological data were obtained from the Japan Meteorological Agency’s database. Daily ECG data were measured while bathing for five years. The subject was a healthy male in his fifties. After collecting ECG data, R-R intervals were calculated to perform HRV analysis in time domain and frequency domain. The evaluation of link between meteorological factors and HRV parameters was realized by utilizing meteorological factors’ importance calculated by Random Forest, and Steel-Dwass significant difference analysis of the values. Additionally, the evaluation was also performed by analyzing the obtained meteorological factors’ importance values using Granger Causality test. In Steel-Dwass test, several significant differences (p<0.05) were found, especially the importance value of air temperature was significantly different in three HRV parameters. I n Granger Causality test, we evaluate the temperature’s importance value for three HRV parameters, and there were significant differences (p<0.05) for HF and LF/HF.

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Velocity-aided Channel Estimation for Spatially Selective mmWave Massive MIMO Communications

Velocity-aided Channel Estimation for Spatially Selective mm...

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European Signal Processing Conference (EUSIPCO)

作者： Evangelos Vlachos Christos Mavrokefalidis Kostas Berberidis Industrial Systems Institute ATHENA Research Center Patras Greece Dept. of Computer Engineering and Informatics University of Patras Patras Greece

This paper focuses on addressing the challenge of estimating multiple-input multiple-output (MIMO) channels for wireless communication between a ground base-station and a moving vehicle. One recently recognised model for time-varying channels incorporates spatial selectivity, which is referred to as beam squint, and is particularly relevant in the millimeter-wave (mmWave) range. In such scenarios, it is essential to account for the beam squint when attempting to recover channel parameters using a training sequence. However, the use of a training sequence alone may be insufficient for this purpose. To overcome this issue, in this work, we propose a channel estimation approach that exploits information provided by the control module of the vehicle, namely its velocity. The estimation problem that is designed, regards the channel both in a parametric and a non-parametric form and the alternating direction method of multipliers is utilised to efficiently solve it. It is demonstrated via simulations that considerable gains can be achieved if information from the control unit of the vehicle can be appropriately introduced and exploited.

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Underwater and In-Air IPT-CPT Wireless Power Transfer Performance Comparison: A Simulation Study

Underwater and In-Air IPT-CPT Wireless Power Transfer Perfor...

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International Conference on Environment and Electrical engineering (EEEIC)

作者： Sadjad Shafiei Seyed Saeid Heidari Yazdi Mostafa Kermani Almaz Saukhimov Arsalan Hekmati Mehdi Bagheri dept. of Electrical and Computer Engineering Nazarbayev University Astana Kazakhstan dept. of Electrical Engineering Sapienza University of Rome Rome Italy dept. of Electrical Systems and Networks Almaty University of Power Engineering and Telecommunications Almaty Kazakhstan Revterra Company Houston Texas USA

In this study, an inductive-capacitive coupling wireless power transmission (IC-UWPT) system in the seawater environment is discussed. It is revealed that the conductive characteristic of seawater medium will change the eddy current flow as compared to the air medium, and in higher frequency ranges, will increase power dissipation and affect the total efficiency and ultimately the system's performance. A new equivalent circuit is provided to precisely model the seawater WPT, where the eddy current losses are referred to as an additional term coil's parameter in the circuit model. Using Ansys Electronics, simulation studies are carried out in both air and water environment. The eddy current and electrostatic solutions are adopted for IPT and CPT system analysis. Moreover, Ansys Simplorer is utilized to couple the proposed IPT-CPT combined system. The simulation results validate the introduced model for the underwater IPT -CPT system and its performance.

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Comparative Evaluation of Machine Learning-Based Controller Area Network Intrusion Detection systems

Comparative Evaluation of Machine Learning-Based Controller ...

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International Conference on Information and Communication systems (ICICS)

作者： Omar Y. Al-Jarrah Omar A. Ramadan Baha Alsaify Suboh Alkhushayni Dept. Network Engineering and Security Faculty of Computer and Information Technology Jordan University of Science and Technology Irbid Jordan Dept. Mechanical Engineering Faculty of Engineering Jordan University of Science and Technology Irbid Jordan Dept. Information Systems Yarmouk University Irbid Jordan

The controller area network (CAN) protocol is widely used in vehicle networks. However, it lacks essential security features like confidentiality and authentication. To enhance vehicle security, researchers have proposed and developed various methods, including intrusion detection systems (IDSs). These IDSs play a key role in early detection of potential cyberattacks, safeguarding vehicles, passengers, and road users. Machine learning (ML) algorithms are increasingly employed in CAN IDSs but they face challenges due to diverse experimental settings, datasets, and metrics used for evaluation, impeding progress and comparison of different approaches in this field. This study aims to benchmark well-recognized ML algorithms, including gaussianNB, k-nearest neighbors (KNN), decision trees (DT), random forest (RF), long short-term memory (LSTM), and convolutional neural networks (CNN), for intrusion detection in CAN. We evaluate the performance of these algorithms using seven evaluation metrics on the real ORNL automotive dynamometer (ROAD) dataset, a state-of-the-art benchmark in this field. Experimental results demonstrate that KNN, DT, RF, LSTM, and CNN can detect various types of attacks (e.g., Fuzzing, targeted ID, and masquerade attacks) with high accuracy. However, the computational and time efficiency of tree-based classifiers, namely DT and RF, makes them a potentially appealing choice for real-time intrusion detection tasks in CAN.

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Unveiling the Unseen: Leveraging Zero-Day Attack Detection Using Unsupervised and Semi-Supervised Learning

Unveiling the Unseen: Leveraging Zero-Day Attack Detection U...

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International Conference on computer Theory and Applications (ICCTA)

作者： Osama A. El Awadia Sameh A. Salem NTRA Research and Technical Solutions Unit Egyptian Computer Emergency Readiness Team (EG-CERT) Egypt Computer & Systems Engineering Dept. Faculty of Engineering Helwan University Cairo Egypt

ISBN: (数字)9798350309584

ISBN: (纸本)9798350309591

In the ever-evolving cybersecurity landscape, detecting unseen, zero-day attacks is both urgent and paramount. These sophisticated attacks often lack precedent, posing a challenge to conventional machine learning techniques that rely on prior knowledge and training data. This paper endeavors to detect zero-day and unseen cyber attacks using zero-shot machine learning technique, which holds the promise of identifying these attacks without any prior exposure. This work explores the effectiveness of unsupervised learning in zero-day attack detection. The experimental results demonstrate that autoencoders can identify anomalies in data, which are typically associated with zero-day attacks. When compared with other unsupervised and semi-supervised learning methods, the proposed autoencoder algorithm outperforms its competitors and achieves an accuracy of 99.9%, shedding light on its relative effectiveness in zero-day attack detection.

关键词： Accuracy computer hacking Autoencoders Zero shot learning Training data Intrusion detection Machine learning Semisupervised learning Unsupervised learning Cyberattack

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Price Optimisation and Management 3

Price Optimisation and Management

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3rd International Conference on Advancements in Computing, ICAC 2021

作者： Shafkhan, M.T.M Jayasundara, P.R.S.S Kariyapperuma, K.A.D.R.L Lakruwan, H.P.S Rupasinghe, Lakmal Sri Lanka Institute of Information Technology Dept. of Computer and Software Eng Malabe Sri Lanka Sri Lanka Institute of Information Technology Dept. of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9781665408622

One of the most crucial decisions a company makes is its pricing strategy. When it comes to pricing, a company must consider the present, as well as the future and the past pricing. It enables a company to make sound judgments. In the process of marketing products, price is the only factor that creates income;everything else is a cost. Guessing at product pricing is a little like throwing darts blindfolded;some will hit something, but it probably will not be the dartboard. Large-scale enterprises throughout the world still depend on Excel sheets with numerous manpower or expensive pricing solutions. Expensive pricing systems are difficult to implement for Medium and Large Sized Enterprises in countries like Sri Lanka. Our goal in this research is to propose an affordable, efficient, easy-To-use and secure solution which can be implemented in Medium and Large Sized Enterprises in Sri Lanka. Manufacturing cost, shipping cost, competitor analysis, customer behaviour are taken as the root factors when deciding the price. The proposed solution includes Machine Learning components which is fed with historical data of these four factors to predict the manufacturing cost, shipping cost, competitor price and customer behavioural factors on a given date and as well as an optimisation component which enables the opportunities to minimise the cost and maximise the profit. The four Machine Learning components are implemented using LSTM, ARIMA, Facebook Prophet and a clustering model. The optimisation model is implemented using linear programming optimise these four components. A user-friendly web application is implemented using MEAN stack with micro service architecture to access this. © 2021 IEEE.

关键词： Cost benefit analysis

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Coordinate Attention-enabled Ship Object Detection with Electro-optical Image 22

Coordinate Attention-enabled Ship Object Detection with Elec...

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Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition

作者： Hongbin Xu Xiantao Jiang Tao Yin Qi Cen Zhijian Zhang Tian Song F.Richard Yu Dept.Information Engineering Shanghai Maritime University China Ningbo Special Equipment Inpection and Research Institute China Dept. Electrical and Electronics Engineering Tokushima University Japan Dept. Systems and Computer Engineering Carleton University Canada

ISBN: (纸本)9781450397056

Shipping safety is one of the factors restricting the development of navigation. In particular, the route near the shore is prone to unknown risks due to the existence of multiple types of ships, the density of ships, the shielding between ships, and other reasons. This paper presents a method for detecting medium-range ships, which can improve security for ships. This method is based on the You Only Look Once Version 5 network (YOLOv5). To improve the accuracy, the coordinate attention model is integrated into the detection network. The main research content and experimental work of this paper are as follows. Firstly, the YOLOv5 network and spatial attention mechanism are analyzed. Then, detection experiments were carried out based on YOLOv5 and Singapore Maritime Data Set (SMD). Then, the coordinate attention model was used to improve the network. Finally, by adjusting training parameters and improving attention, the mAP of test results of the object detection network reaches 73%, and the feasibility of object detection of the YOLOv5 algorithm with coordinate attention is confirmed.

关键词： Ship Object Detection Coordinate Attention Mechanism You Only Look Once Version 5 (YOLOv5)

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Direct Data-Driven State-Feedback Control of General Nonlinear systems

Direct Data-Driven State-Feedback Control of General Nonline...

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IEEE Conference on Decision and Control

作者： Chris Verhoek Patrick J. W. Koelewijn Sofie Haesaert Roland Tóth Dept. of Electrical Engineering Control Systems group Eindhoven University of Technology The Netherlands Institute for Computer Science and Control Budapest Hungary

Through the use of the Fundamental Lemma for linear systems, a direct data-driven state-feedback control synthesis method is presented for a rather general class of nonlinear (NL) systems. The core idea is to develop a data-driven representation of the so-called velocity-form, i.e., the time-difference dynamics, of the NL system, which is shown to admit a direct linear parameter-varying (LPV) representation. By applying the LPV extension of the Fundamental Lemma in this velocity domain, a state-feedback controller is directly synthesized to provide asymptotic stability and dissipativity of the velocity-form. By using realization theory, the synthesized controller is realized as a NL state-feedback law for the original unknown NL system with guarantees of universal shifted stability and dissipativity, i.e., stability and dissipativity w.r.t. any (forced) equilibrium point, of the closed-loop behavior. This is achieved by the use of a single sequence of data from the system and a predefined basis function set to span the scheduling map. The applicability of the results is demonstrated on a simulation example of an unbalanced disc.

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