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15th IEEE Annual Computing and Communication Workshop and Conference, CCWC 2025

作者： Said, Huwida E. Mahmoud, Qusay H. Goyal, Mandeep Hashim, Faiza College of Technological Innovation Zayed University Department of Computing and Applied Technolgy Dubai United Arab Emirates Ontario Tech University Department of Electrical Computer and Software Engineering OshawaONL1G 0C6 Canada College of Information Technology Uae University Department of Computer Science and Software Engineering AI-Ain United Arab Emirates

ISBN: (纸本)9798331507695

Differential privacy offers a promising solution to balance data utility and user privacy. This paper compares two prominent differential privacy tools-PyDP and IBM's diffprivlib-that are applied to a synthetic dataset with medical attributes. We evaluate these tools based on their effectiveness in maintaining data privacy while preserving the statistical integrity of the data. Our results reveal that PyDP provides synthetic data that closely matches real-world data, making it appropriate for tasks requiring accuracy while striking a better balance between data utility and privacy. However, IBM's diffprivlib is more suitable for privacy-critical applications due to its stronger privacy guarantees, but at the cost of increased noise and decreased data utility. This paper contributes to the practical understanding of implementing differential privacy in machine learning and software applications and enhances the tools available for developers in sensitive data environments. © 2025 IEEE.

关键词： Data integrity

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Smart Home Automation with IoT Sensor Systems for Energy Conservation using Zigbee and Wifi 5

Smart Home Automation with IoT Sensor Systems for Energy Con...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Jagannathan, Srinivasan Deepa, V. Sakthi Karthi Durai, B. Rajan, M. Sundar Celin, J. Jeya A. Dhamodaran, Sasikala Madanapalle Institute of Technology & Science Department of Computer Applications Madanapalle India Tiruppur Kumaran College for Women Pg and Research Department of Computer Science Tamil Nadu Tiruppur India Kalasalingam Academy of Research and Education Computer Science and Engineering India Arbaminch Institute of Technology Arbaminch University Faculty of Electrical and Computer Engineering Ethiopia Kalasalingam Academy of Research and Education Department of Information Technology Viruthunagar Krishnankoil626126 India Rajasthan Jaipur India

ISBN: (纸本)9798350379945

This study presents a comprehensive study on smart home automation systems utilizing Internet of Things (IoT) sensor technology to achieve efficient energy conservation. The proposed system integrates ZigBee and WiFi communication protocols for effective and reliable connectivity across various smart devices within a home. Through the deployment of interconnected sensors and intelligent controllers, the system allows real-time monitoring and management of household energy usage, optimizing energy consumption and reducing waste. The combination of ZigBee for low-power, long-range sensor networks and WiFi for high-speed data transmission enables seamless interaction between smart appliances, lighting, heating, and cooling systems. Proposesd approach offers a scalable, cost-effective solution for enhancing home automation with an emphasis on energy conservation and sustainability. Also discussed the implementation, challenges, and future potential of IoT-based smart home systems for improved energy efficiency. © 2024 IEEE.

关键词： Smart homes

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Predicting Resource Availability in Local Mobile Crowd Computing Using Convolutional GRU

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computers, Materials & Continua 2022年第3期70卷 5199-5212页

作者： Pijush Kanti Dutta Pramanik Nilanjan Sinhababu Anand Nayyar Mehedi Masud Prasenjit Choudhury Department of Computer Science&Engineering National Institute of TechnologyDurgapurIndia Reliability Engineering Centre Indian Institute of Technology KharagpurIndia Graduate School Duy Tan UniversityDa Nang550000Vietnam Faculty of Information Technology Duy Tan UniversityDa Nang550000Vietnam Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

In mobile crowd computing(MCC),people’s smart mobile devices(SMDs)are utilized as computing *** the ever-growing computing capabilities of today’s SMDs,a collection of them can offer significantly high-performance computing *** a localMCC,the SMDs are typically connected to a local Wi-Fi *** and institutions can leverage the SMDs available within the campus to form local MCCs to cater to their computing needs without any financial and operational *** it offers an economical and sustainable computing solution,users’mobility poses a serious issue in the QoS of *** address this,before submitting a job to an SMD,we suggest estimating that particular SMD’s availability in the network until the job is *** this,we propose a convolutional GRU-based prediction model to assess how long an SMD is likely to be available in the network from any given point of *** experimental purposes,we collected real users’mobility data(in-time and outtime)with respect to a Wi-Fi access *** build the prediction model,we presented a novel feature extraction method to be applied to the time-series *** experimental results prove that the proposed convolutional GRU model outperforms the conventional GRU model.

关键词： Resource selection resource availability mobile grid mobile cloud ad-hoc cloud crowd computing deep learning GRU CNN RNN

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Artificial Intelligence for Improved Maternal Healthcare: A Systematic Literature Review 16

Artificial Intelligence for Improved Maternal Healthcare: A ...

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16th IEEE AFRICON, AFRICON 2023

作者： Chemisto, Musa Gutu, Tar J.L. Kalinaki, Kassim Mwebesa Bosco, Darlius Egau, Percival Fred, Kirya Tim Oloya, Ivan Rashid, Kisitu Department of Electronics and Computer Engineering Soroti Uganda Department of Computer Science Mbale Uganda Isbat University Faculty of Information and Communications Technology Kampala Uganda Soroti Uganda Soroti Uganda

ISBN: (纸本)9798350336214

The integration of artificial intelligence (AI) in maternal health is a promising avenue for improving pregnancy, early childhood, and postnatal care. This systematic review analyzed 31 articles retrieved from Web of science, PubMed, and Scopus, which were classified using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method and Mendeley referencing tool. Our interpretive study found that AI applications in maternal health can predict 48% of maternal complications using electronic medical records (EMR), 29% using medical images, 19% using genetic markers, and 4% using other medical features such as fetal heart rates and sensors. The accuracy of prematurity prediction using AI was 95.7%, while the XGBoost technique predicted neonatal mortality with 99.7% accuracy. The study underscores the potential benefits of AI in maternal healthcare and highlights the need for further research to improve maternal and child health outcomes, especially in resource-constrained sub-Saharan African regions where maternal mortality rates are significantly high. © 2023 IEEE.

关键词： Artificial intelligence

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A Neuro-Fuzzy Approach to Road Traffic Congestion Prediction

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computers, Materials & Continua 2022年第10期73卷 295-310页

作者： Mohammed Gollapalli Atta-ur-Rahman Dhiaa Musleh Nehad Ibrahim Muhammad Adnan Khan Sagheer Abbas Ayesha Atta Muhammad Aftab Khan Mehwash Farooqui Tahir Iqbal Mohammed Salih Ahmed Mohammed Imran BAhmed Dakheel Almoqbil Majd Nabeel Abdullah Omer Department of Computer Information System(CIS) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Department of Computer Science(CS) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Pattern Recognition and Machine Learning Lab Department of Software EngineeringGachon UniversityInchonKorea Department of Computer Science National College of Business Administration and EconomicsLahorePunjab54000Pakistan Department of Computer Science Government College University(GCU)LahorePunjab54000Pakistan Department of Computer Engineering(CE) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia College of Business Administration Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Department of Networks and Communications(NC) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia

The fast-paced growth of artificial intelligence applications provides unparalleled opportunities to improve the efficiency of various *** as the transportation sector faces many obstacles following the implementation and integration of different vehicular and environmental aspects *** congestion is among the major issues in this regard which demands serious attention due to the rapid growth in the number of vehicles on the *** address this overwhelming problem,in this article,a cloudbased intelligent road traffic congestion prediction model is proposed that is empowered with a hybrid Neuro-Fuzzy *** aim of the study is to reduce the delay in the queues,the vehicles experience at different road junctions across the *** proposed model also intended to help the automated traffic control systems by minimizing the congestion particularly in a smart city environment where observational data is obtained from various implanted Internet of Things(IoT)sensors across the *** due preprocessing over the cloud server,the proposed approach makes use of this data by incorporating the neuro-fuzzy ***,it possesses a high level of accuracy by means of intelligent decision making with minimum error *** results reveal the accuracy of the proposed model as 98.72%during the validation phase in contrast to the highest accuracies achieved by state-of-the-art techniques in the literature such as 90.6%,95.84%,97.56%and 98.03%,*** far as the training phase analysis is concerned,the proposed scheme exhibits 99.214% accuracy. The proposed prediction modelis a potential contribution towards smart cities environment.

关键词： Neuro-fuzzy machine learning congestion prediction AI cloud computing smart cities

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An empirical IoT and cloud-based customizable healthcare surveillance system

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International Journal of information technology (Singapore) 2024年第8期16卷 5317-5323页

作者： Meti, Subhash Razauddin, S. Nallakumar, R. Mansingh, P. M. Benson Sameen, Aws Zuhair Pandey, Sakshi Bhatt, Sanjeev Kumar Jayabalan, Bhuvana Department of Electronics and Communication Engineering SKSVMACET Lakshmeshwar Karnataka Gadag India College of Computer Science and Engineering University of Hail Hail Saudi Arabia Department of Artificial Intelligence and Data Science Karpagam Institute of Technology Coimbatore India Department of Advanced Computer Science and Engineering Vignan’s Foundation for Science Technology and Research Andhra Pradesh Guntur India College of Medical Techniques Al-Farahidi University Baghdad Iraq Centre of Interdisciplinary Research in Business and Technology Chitkara Institute of Engineering and Technology Chitkara University Punjab Rajpura India Software Architect and Research Scholar Digital Innovation College PDM University Bahadurgarh India Department of Computer Science and Information Technology JAIN (Deemed-to-Be University) Karnataka Bengaluru India

In certain emergencies, patients must be continuously monitored and cared for. However, visiting the hospital to do such activities is difficult because of time constraints. To modernize the healthcare sector, the study in question presents an empirical Internet of Things and cloud-based, customizable healthcare monitoring system. Initially, a cloud-based algorithm called particle swarm intelligence (PGI) is used in the proposed healthcare monitoring system to group recorded data, hence increasing the prediction rate. Subsequently, features are selected and extracted, and back propagation neural networks (BPNN) are employed to classify healthcare for humans. In the end, the effectiveness of the suggested system is evaluated using a healthcare dataset and contrasted with current deep learning/machine learning classifiers. The suggested surveillance system performs better than the methods employed in the previous research, according to the findings. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： BPNN Cloud computing Healthcare surveillance IoT Particle swarm intelligence (PSI)

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Studying Cardiac Electrophysiology via EPSSIM 20

Studying Cardiac Electrophysiology via EPSSIM

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20th International Joint Conference on computer science and software engineering, JCSSE 2023

作者： Katekarn, Sujitra Sisuk, Noppadon Nuamchit, Teonchit Jittiporn, Kanjana Payakpate, Janjira Naresuan University Faculty of Allied Health Sciences Department of Cardio-thoracic Technology Phitsanulok Thailand Phibunsongkhram Rajabhat University Program in Computer Engineering Faculty of Industrial Technology Phitsanulok Thailand Naresuan University Faculty of Sciences Department of Computer Science and Information Technology Phitsanulok Thailand

ISBN: (纸本)9798350300505

For Cardio-thoracic technology education program, the focus is on teaching pathophysiology, as well as the interpretation of electrocardiograms (ECG), through case studies. The curriculum includes instruction on treatment options and the use of electrophysiology studies (EP studies) for tachycardia and cardiac ablation. A portion of e-learning resources for ECGs and EP studies are delivered via web-based platforms. However, to date, there have been no e-learning platforms that combine instruction on pathological ECGs, EP study, cardiac ablation, or simulations of EP which would provide a more comprehensive learning experience for students. Therefore, the purpose of this study was to create the electrophysiology study simulator program (EPSSIM), which serves as an e-learning platform for EP studies and ECG. The development started with gathering information about ECG from the medical records of patients at a public hospital. MATLAB was used to generate synthetic heart signals. Microsoft Visual Studio 2019 was used to construct the EPSSIM program. A total of 47 cardio-thoracic technology students who used the program completed a pre-test and post-test and questionnaires. The effectiveness and satisfaction of the EPSSIM program were evaluated. The results showed that participants' scores improved, but the improvement was restricted due to the short time that the students were able to use the system. The participants who completed the satisfaction survey generally indicated satisfaction with the effectiveness of EPSSIM. Overall, the use of the application was beneficial and the application could be successfully used in future to enhance users knowledge. © 2023 IEEE.

关键词： Electrocardiograms

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Predicting the friction angle of clays using a multi-layer perceptron neural network enhanced by yeo-johnson transformation and coral reefs optimization

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Journal of Rock Mechanics and Geotechnical engineering 2024年第10期16卷 3982-4003页

作者： Libing Yang Trung Nguyen-Thoi Trung-Tin Tran College of Civil Engineering and Architecture Hunan Institute of Science and TechnologyYueyang414006China Laboratory for Applied and Industrial Mathematics Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh CityViet Nam Faculty of Mechanical-Electrical and Computer Engineering School of TechnologyVan Lang UniversityHo Chi Minh CityViet Nam Department of Information Technology Greenwich VietnamFPT UniversityDa Nang550000Viet Nam

The accurate prediction of the friction angle of clays is crucial for assessing slope stability in engineering *** study addresses the importance of estimating the friction angle and presents the development of four soft computing models:YJ-FPA-MLPnet,YJ-CRO-MLPnet,YJ-ACOC-MLPnet,and *** of all,the Yeo-Johnson(YJ)transformation technique was used to stabilize the variance of data and make it more suitable for parametric statistical models that assume normality and equal *** technique is expected to improve the accuracy of friction angle prediction *** friction angle prediction models then utilized multi-layer perceptron neural networks(MLPnet)and metaheuristic optimization algorithms to further enhance performance,including flower pollination algorithm(FPA),coral reefs optimization(CRO),ant colony optimization continuous(ACOC),and cuckoo search algorithm(CSA).The prediction models without the YJ technique,***-MLPnet,CRO-MLPnet,ACOC-MLPnet,and CSA-MLPnet,were then compared to those with the YJ technique,***-FPA-MLPnet,YJ-CRO-MLPnet,YJ-ACOC-MLPnet,and *** these,the YJ-CRO-MLPnet model demonstrated superior reliability,achieving an accuracy of up to 83%in predicting the friction angle of clay in practical engineering *** improvement is significant,as it represents an increase from 1.3%to approximately 20%compared to the models that did not utilize the YJ transformation technique.

关键词： Natural hazards Slope stability Friction angle Clay Soft computing models Geotechnical engineering

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MLPDR: High Performance ML Algorithms for the Prediction of Diabetes Retinopathy

MLPDR: High Performance ML Algorithms for the Prediction of ...

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2023 IEEE International Conference on Network, Multimedia and information technology, NMITCON 2023

作者： Madhuri G, Sindhu Chokkanathan, K. Mahesh, T.R. Musthafa, M.Mohamed Vanitha, K. Vivek, V. Manipal Institute of Technology Department of Information Technology Bengaluru India Manipal Academy of Higher Education Manipal India Madanapalle Institute of Technology & Science Department of Ai Madanapalle India Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India Tamil Nadu Erode India Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350300826

In today's world, millions of imaging analyses are performed every week, and medical imaging plays a significant role in that. It is a process of creating visual representation based on the functioning of human organs or tissues to identify abnormalities or study diseases. Diabetes has been regarded as one of the most prevalent diseases by many researchers. Blindness or other retinal alterations based on the eye's vision, such as difficulty reading or seeing distant things, may occur, which has an impact on diabetes in humans. A new computer aided diagnosis system based on image processing of retinal images can be analyzed in order to detect diabetic patients in advance. In this study, the eye retina based on image processing techniques is analyzed and machine learning techniques have been used to extract features of eye and several computational intelligence Machine Learning techniques like Support Vector Machines (SVM) and Principal Component Analysis (PCA). These Computational Intelligence techniques have been used to detect the diabetic in advance. Several performance metrics have been considered such as Root Mean Square Error (RMSE), Peak Signal Noise Ratio (PSNR), Entropy (E), and Accuracy (A) in obtaining the better accuracy results for detection of diabetic retinopathy in early stages. The experimental results show that, SVM outperforms with an accuracy of 95%. © 2023 IEEE.

关键词： Principal component analysis

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Type II Fuzzy Logic Based Cluster Head Selection for Wireless Sensor Network

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computers, Materials & Continua 2022年第1期70卷 801-816页

作者： J.Jean Justus M.Thirunavukkarasan K.Dhayalini G.Visalaxi Adel Khelifi Mohamed Elhoseny Department of Computer Science&Engineering St.Joseph’s College of EngineeringChennai600119India School of Computing Science and Engineering Galgotias UniversityUttar Pradesh203201India Department of Electrical and Electronics Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Computer Science&Engineering Bharath Institute of Higher Education and ResearchChennai600073India Department of Computer Science and Information Technology Abu Dhabi UniversityAbu Dhabi59911United Arab Emirates College of Computer Information Technology American University in the EmiratesDubaiUnited Arab Emirates Faculty of Computers and Information Mansoura UniversityEgypt

Wireless Sensor Network(WSN)forms an essential part of *** is embedded in the target environment to observe the physical parameters based on the type of *** nodes inWSN are constrained by different features such as memory,bandwidth,energy,and its processing *** WSN,data transmission process consumes the maximum amount of energy than sensing and processing of the ***,diverse clustering and data aggregation techniques are designed to achieve excellent energy efficiency in *** this view,the current research article presents a novel Type II Fuzzy Logic-based Cluster Head selection with Low Complexity Data Aggregation(T2FLCH-LCDA)technique for *** presented model involves a two-stage process such as clustering and data ***,three input parameters such as residual energy,distance to Base Station(BS),and node centrality are used in T2FLCH technique for CH selection and cluster ***,the LCDA technique which follows Dictionary Based Encoding(DBE)process is used to perform the data aggregation at ***,the aggregated data is transmitted to the BS where it achieves energy *** experimental validation of the T2FLCH-LCDAtechnique was executed under three different scenarios based on the position of *** experimental results revealed that the T2FLCH-LCDA technique achieved maximum energy efficiency,lifetime,Compression Ratio(CR),and power saving than the compared methods.

关键词： Clustering data aggregation energy consumption cluster head selection wireless sensor networks

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