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GPRS Based Health Monitoring System 1

2nd International Conference on Advances in Information and Communication Technology, ICTA 2023

作者： Deepa, A.R. Hoang, Quang-Trung Bui, Van-Tung Tran, Duc-Tan Ramaswamy, V. Sheela, C. J. Jeba Chowdhury, S. Tran, Duc-Nghia Department of Computer Science and Engineering Koneru Lakshmaih Educational Guntur Foundation India Faculty of Electrical and Electronic Engineering Phenikaa University Hanoi12116 Viet Nam Thai Nguyen University of Information and Communication Technology Thai Nguyen2400 Viet Nam Department of PG Computer Science Nesamony Memorial Christian College Marthandam India Chittoor AP Murukambattu India Institute of Information Technology Vietnam Academy of Science and Technology Hanoi Viet Nam

ISBN: (数字)9783031508189

ISBN: (纸本)9783031508172

The idea and prototype of an Internet of Things (IoT) based health monitoring gadget are introduced in this paper. We concentrate on leveraging the cloud and IoT to monitor temperature an coronary heart rate in real-time. This design can be utilized for the early diagnosis of heart illnesses given the rising number of cardiac patients throughout the world. Contrary to the majority of the current monitoring methods and systems, a variety of wireless technologies are available, but their range is constrained. IoT is being used in this session to monitor. There is no distance restriction with IoT. Through the internet, we can test a patient's coronary heart rate and temperature from anywhere. Our strategy involves using the cloud to track the temperature and heart rate in real time. Through the use of IoT technology, the recorded temperature and heart rate may be saved and instantly evaluated for prognosis and diagnosis. An integrated GPRS microcontroller on a single chip makes up the suggested hardware architecture for connecting to the internet. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Internet of things

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Impact Analysis of Insufficient Destination Resources on VoIP Service Quality: Case Study

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Procedia computer science 2024年 245卷 247-254页

作者： Mhd Indra Iskandar Alexander Agung Santoso Gunawan Professional Engineer Engineering Faculty Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480

This research investigates the disruption caused by insufficient destination resources in Voice over Internet Protocol (VoIP) networks. The study is based on a case study of service disruptions at a Public Service Company (PSC) where the public experienced dropped calls. The research employs Ribbon Analytics, a big data analytics platform, to measure the concurrent session connection on the Session Initiation Protocol (SIP). The observation of the VoIP network followed a specific topology with 22 Session Border Controller (SBC) nodes, 2 Call Detail Record (CDR) servers, and 2 Ribbon Analytics (RA) devices. The results indicate that the call failure occurred because the number of sessions had reached the maximum limit. The solution proposed is to increase the number of sessions; however, this cannot be done immediately due to contractual constraints between the telecom operators and the PSC. An alternative solution is to provide a call answering machine managed by AI. Future work could also use text message in the form of chat prompts from AI. The study highlights the importance of big data analysis in identifying and resolving service disruptions in VoIP networks.

关键词： insufficient destination resources text mining, call details recorder session border controller call termination

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Intelligent Optimization-Based Clustering with Encryption Technique for Internet of Drones Environment

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computers, Materials & Continua 2022年第12期73卷 6617-6634页

作者： Dalia H.Elkamchouchi Jaber S.Alzahrani Hany Mahgoub Amal S.Mehanna Anwer Mustafa Hilal Abdelwahed Motwakel Abu Sarwar Zamani Ishfaq Yaseen Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Faculty of Computers and Information Computer Science DepartmentMenoufia UniversityEgypt Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo 11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The recent technological developments have revolutionized the functioning of Wireless Sensor Network(WSN)-based industries with the development of Internet of Things(IoT).Internet of Drones(IoD)is a division under IoT and is utilized for communication amongst *** drones are naturally mobile,it undergoes frequent topological *** alterations in the topology cause route election,stability,and scalability problems in *** is considered as an effective method to transmit the images in IoD *** current study introduces an Atom Search Optimization basedClusteringwith Encryption Technique for Secure Internet of Drones(ASOCE-SIoD)*** key objective of the presented ASOCE-SIoD technique is to group the drones into clusters and encrypt the images captured by *** presented ASOCE-SIoD technique follows ASO-based Cluster Head(CH)and cluster construction *** addition,signcryption technique is also applied to effectually encrypt the images captured by drones in IoD *** process enables the secure transmission of images to the ground *** order to validate the efficiency of the proposed ASOCE-SIoD technique,several experimental analyses were conducted and the outcomes were inspected under different *** comprehensive comparative analysis results established the superiority of the proposed ASOCE-SIoD model over recent approaches.

关键词： Internet of drones atom search algorithm clustering encryption signcryption

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Assessing Parkinson's Disease Severity Using Channel Attention and Graph Attention Networks on Kinect Skeleton Images 19

Assessing Parkinson's Disease Severity Using Channel Attenti...

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19th Iranian Conference on Intelligent Systems, ICIS 2024

作者： Fadaie, Fatemeh Aghapanah, Hamed Sedighifard, Alireza Shirvan, Hadi Niknam AI and Robotics University of Isfahan Department of Computer Engineering Isfahan Iran School of Advanced Technologies in Medicine Isfahan University of Medical Sciences Department of Bioelectrics and Biomedical Engineering Isfahan Iran Iran University of Science and Technology Department of Artificial Intelligence Isfahan Iran University of Tehran Faculty of New Sciences and Technologies Isfahan Iran

ISBN: (纸本)9798331507565

Parkinson's Disease (PD), a prevalent global neurological disorder, presents with motor symptoms like rigidity and tremors. Accurate severity assessment is vital for intervention evaluation. This study uses computer vision, specifically Kinect cameras and deep learning, to develop a system for assessing the severity of Parkinson's disease. Leveraging Kinect cameras' Precision in tracking key motion patterns, the system utilizes convolutional neural networks, namely Spatial-Temporal Graph Convolutional Network (STGCN) and Two-Stream Adaptive Graph Convolutional Network (2SAGCN), to detect movement anomalies and provide quantitative severity measures. A novel model is proposed, representing an enhanced version of 2SAGCN featuring a channel and graph attention module. Parkinson's severity classification comprises 11 classes based on 9 motion categories, totaling 99 classes. Three-dimensional transfer learning and data augmentation improve model Accuracy. This research advances non-invasive, quantitative Parkinson's assessment using innovative technology and advanced neural network models. The program is hosted on GitHub for implementation and result observation. The 10-fold cross-validation results of the best model, incorporating 2SAGCN structure, Graph, and Channel Attention, yield Accuracy, Precision, Recall, and F1-Score of Confidence Intervals 94.12± 0.14, 99.6 ± 0.20, 98.9 ± 0.10,99.2 ± 0.80 respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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Intelligent Control of Air Combat Maneuvers Using Fuzzy Logic

Intelligent Control of Air Combat Maneuvers Using Fuzzy Logi...

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Mechatronics and Robotics engineering (ICMRE), International Conference on

作者： Mohanad A. Deif Mohamed A. Hafez Hani Attar S.F. Rezeka T. Awad E.M. Attia M.B. Badawi Research Institute of Sciences and Engineering University of Sharjah United Arab Emirates Faculty of Engineering FEQS INTI-IU Universi Nilai Malaysia Department of Energy Engineering Zarqua University Zarqa Jordan College of Engineering University of Business and Technology Jeddah Saudi Arabia Mechanical Engineering Department Faculty of Engineering Alexandria University Alexandria Egypt Department of Mechanical Engineering Faculty of Engineering Alexandria University Alexandria Egypt Mechanical Engineering Department Faculty of Engineering Alexandria University El-Chatby Alexandria Egypt Department of Computer Science Faculty of Computer Science and Engineering King Salman International University (KSIU) Egypt

ISBN: (数字)9798331509293

ISBN: (纸本)9798331509309

With the expanding capabilities of fighter aircraft in gauging their targets with great accuracy, comes the necessity that fighter pilots possess higher talents in making the correct decision. Because human error is frequently a barrier to making precise and timely decisions, a control mechanism is required to regulate the trajectory of aircraft during combat maneuvers. In this paper, a combat-maneuver fuzzy program is developed and applied to the inverse kinematics of the F-16 aircraft. The program produces a new path for the plane to achieve the maneuver such that the minimum distance between the resulting path and the object aircraft path is larger than a specified distance (critical distance). Also, spatial defensive maneuvers against spatial offensive maneuvers of bandits were realized, accordingly, this research emphasizes the importance of process innovation in the design of adaptive air combat control systems, enabling more effective response strategies.

关键词： Fuzzy logic Technological innovation Process control Turning Reliability engineering Prediction algorithms Trajectory Aircraft Robots Aerospace control

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A non-Restraining Sheep Activity Detection and Surveillance using Deep Machine Learning 16

A non-Restraining Sheep Activity Detection and Surveillance ...

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16th International Conference on Developments in eSystems engineering, DeSE 2023

作者： Ayub, Muhammad Yaseen Hussain, Abir Hassan, Muhammad Furqan Ul Khan, Bilal Khan, Farman Ali Al-Jumeily, Dhiya Khan, Wasiq Comsats University Islamabad Department of Computer Science Attock Campus Pakistan Sharjah University College of Engineering United Arab Emirates California State University School of Computer Science & Eng San BernardinoCA United States Liverpool John Moores University Faculty of Engineering & Technology Liverpool United Kingdom

ISBN: (纸本)9798350381344

The number of livestock farms and their sizes (particularly the sheep farms) are on the rise, in response to the growing demands of food supply chain for increasing population. The detection and monitoring of sheep activities particularly in huge farms is tedious and challenging task. Therefore, a reliable and cost-effective sheep activity detection system which can be utilized for the virtual fencing, is of high demand. Existing data-driven approaches use accelerometer data for sheep monitoring and activity detection however, there are several limitations with these methods such as generating high volume of data with noise, relatively expensive, and not very reliable. This study presents a non-invasive computer vision-based approach along with deep transfer learning for sheep detection and determining whether the corresponding state is 'active' or 'inactive'. We complied a primary dataset comprising sheep in diverse poses and activities in a realistic outdoor environment. A custom YOLOV5s model is trained over new dataset and validated on purely unseen sheep instances for the model evaluation. The statistical outcomes demonstrate the robustness of proposed approach for various sheep activity detection. Our method has diverse implications and uses in the development of reliable and economical systems for monitoring sheep, particularly in extensive farms and virtual fencing applications. © 2023 IEEE.

关键词： Deep learning

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Automatic Detection of Tuberculosis with the Help of Image Processing and Machine Learning Classifier 7

Automatic Detection of Tuberculosis with the Help of Image P...

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7th International Conference on Computing in engineering and Technology, ICCET 2022

作者： Sharma, Anshu Malhotra, Rahul Sharma, Anurag Department of Computer Science & Engineering CT University Ludhiana India Department of Electronics & Communication Engineering CT Group of Institutions Jalandhar India Faculty of Engineering Design and Automation GNA University Phagwara India

ISBN: (纸本)9781839537042

Tuberculosis is the substantial irresistible disease present in human that principally influences lungs, this tuberculosis is one the main ten noticeable reasons for death for human. It has very typical symptoms so without testing it isn't sure that patient has infected with TB. In this paper, an automated framework dependent on image handling is proposed to help specialists and radiologists in deciphering Chest X-beams (CXR) for early identification of lung Tuberculosis (TB). CXR can show the abnormalities in lungs as well as TB which helps to precisely and rapidly distinguish TB on the grounds that early treatment will provide to patients and disease will stop spreading in body. The proposed system comprised of preprocessing, segmentation, features extraction, and classification. In this paper proposed a strategy of image segmentation and AI to conclusion and characterize the tuberculosis (TB). These tool and procedures are utilized to earlier uncover of tuberculosis (TB) illness. Additionally arrange the sort of TB. The framework recognizes the tuberculosis with more prominent precision. © 2022 The Institution of engineering and Technology.

关键词： Diseases

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Diagnosis of COVID-19 Infection Using Three-Dimensional Semantic Segmentation and Classification of Computed Tomography Images

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computers, Materials & Continua 2021年第8期68卷 2451-2467页

作者： Javaria Amin Muhammad Sharif Muhammad Almas Anjum Yunyoung Nam Seifedine Kadry David Taniar Department of Computer Science University of Wah47040Pakistan Department of Computer Science Comsats University IslamabadWah Campus47040Pakistan College of Electrical and Mechanical Engineering National University of Sciences&Technology(NUST)Islamabad44000Pakistan Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea Department of Mathematics and Computer Science Faculty of ScienceBeirut Arab University115020Lebanon Faculty of Information Technology Monash UniversityClaytonVictoria3800Australia

Coronavirus 19(COVID-19)can cause severe pneumonia that may be *** diagnosis is *** tomography(CT)usefully detects symptoms of COVID-19 *** this retrospective study,we present an improved framework for detection of COVID-19 infection on CT images;the steps include pre-processing,segmentation,feature extraction/fusion/selection,and *** the pre-processing phase,a Gabor wavelet filter is applied to enhance image intensities.A marker-based,watershed controlled approach with thresholding is used to isolate the lung *** the segmentation phase,COVID-19 lesions are segmented using an encoder-/decoder-based deep learning model in which deepLabv3 serves as the bottleneck and mobilenetv2 as the classification ***3 is an effective decoder that helps to refine segmentation of lesion *** model was trained using fine-tuned hyperparameters selected after extensive ***,the Gray Level Co-occurrence Matrix(GLCM)features and statistical features including circularity,area,and perimeters were computed for each segmented *** computed features were serially fused and the best features(those that were optimally discriminatory)selected using a Genetic Algorithm(GA)for *** performance of the method was evaluated using two benchmark datasets:The COVID-19 Segmentation and the POF Hospital *** results were better than those of existing methods.

关键词： Deeplabv3 fusion genetic algorithm gabor watershed

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Preface of the Symposium “Qualitative Properties of Solutions of Differential Equations”

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AIP Conference Proceedings 2022年第1期2425卷 1-4页

作者： Ruggieri, Marianna Scapellato, Andrea Faculty of Engineering and Architecture Kore University of Enna Enna Italy Department of Mathematics and Computer Science University of Catania Catania Italy

Marianna Ruggieri, Andrea Scapellato; Preface of the Symposium “Qualitative Properties of Solutions of Differential Equations”, AIP Conference Proceedings, Volu

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Optimized Ensemble Algorithm for Predicting Metamaterial Antenna Parameters

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computers, Materials & Continua 2022年第6期71卷 4989-5003页

作者： El-Sayed M.El-kenawy Abdelhameed Ibrahim Seyedali Mirjalili Yu-Dong Zhang Shaima Elnazer Rokaia M.Zaki Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Centre for Artificial Intelligence Research and Optimization Torrens University AustraliaFortitude ValleyQLD 4006Australia Yonsei Frontier Lab Yonsei UniversitySeoul03722Korea School of Computing and Mathematical Sciences University of LeicesterLeicesterLE17RHUK Nile Higher Institute for Engineering and Technology MansouraEgypt Computer and Information Technology College Taif UniversityTaifSaudi Arabia Higher Institute of Engineering and Technology Kafrelsheikh Department of Electrical Engineering Shoubra Faculty of EngineeringBenha UniversityEgypt

Metamaterial Antenna is a subclass of antennas that makes use of metamaterial to improve *** antennas can overcome the bandwidth constraint associated with tiny *** learning is receiving a lot of interest in optimizing solutions in a variety of *** learning methods are already a significant component of ongoing research and are anticipated to play a critical role in today’s *** accuracy of the forecast is mostly determined by the model *** purpose of this article is to provide an optimal ensemble model for predicting the bandwidth and gain of the Metamaterial *** Vector Machines(SVM),Random Forest,K-Neighbors Regressor,and Decision Tree Regressor were utilized as the basic *** Adaptive Dynamic Polar Rose Guided Whale Optimization method,named AD-PRS-Guided WOA,was used to pick the optimal features from the *** suggested model is compared to models based on five variables and to the average ensemble *** findings indicate that the presented model using Random Forest results in a Root Mean Squared Error(RMSE)of(0.0102)for bandwidth and RMSE of(0.0891)for *** is superior to other models and can accurately predict antenna bandwidth and gain.

关键词： Metamaterial antenna machine learning ensemble model feature selection guided whale optimization support vector machines

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