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7th IET Smart Cities Symposium, SCS 2023

作者： Mijwil, Maad M. El-Kenawy, El-Sayed M. Bala, Indu Aljanabi, Mohammad Abotaleb, Mostafa Ibrahim, Abdelhameed Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Egypt School of Electrical and Electronics Engineering Lovely Professional University Punjab India Department of Computer Aliraqia University Baghdad Iraq Department of System Programming South Ural State University Chelyabinsk Russia Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

ISBN: (纸本)9781839539831

Blood is vital for transporting oxygen, nutrients, and hormones to all body parts as it circulates through arteries and veins. It removes carbon dioxide, regulates body temperature, and maintains the body's immune system. Individuals constantly need blood and its derivatives to save their lives and improve their health through medical treatments and surgical operations. Liver diseases are one of the diseases that affects the health of individuals and requires blood to continue living. These diseases cause significant damage to people's health, and early diagnosis plays a crucial role in saving lives. In this paper, machine learning algorithms (support vector machine and random forest) are involved in detecting liver diseases and determining whether donors are suitable to donate blood from blood values. This paper is applied research that found that the performance measures of the random forest algorithm achieved excellent performance in identifying suitable people to donate blood. © The Institution of Engineering & technology 2023.

关键词： Support vector machines

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Personalized Collaborative Search Engine for Decision Making

Personalized Collaborative Search Engine for Decision Making

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Communication, computer Sciences and Engineering (IC3SE), International Conference on

作者： Mohd. Murshleen Prabhu A Garima Jain Department of Computer Science and Engineering Tula's Institute Dehradun India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Department of Computer Science & Business System Noida Institute of Engineering and Technology Greater Noida Uttar Pradesh India

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366853

Web search engines are used for retrieving information but still there exists ambiguity in their retrieval process. It is so because traditional search engines laid emphasis on matching of keywords rather than maintaining semantic relations among terms lack of ability to produce precise results, presence of ambiguous keywords and verifying needs of users are few hindrances that are acting as scenario for web search engines. The paper focuses on improving search quality of users by collecting user experiences through creating hierarchical user profile it leads to collaborative search engine that helps in reaching at particular decision then the collected user's data are uploaded to recommendation server which are than displayed a search engines like Google/Yahoo.

关键词： Semantics Decision making Collaboration Search engines Servers Web search Engines

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A Data Science Approach to Precision Medicine: Allostatic Load as a Predictor of Cardiovascular Disease 7

A Data Science Approach to Precision Medicine: Allostatic Lo...

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7th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2023

作者： Ikome, Otto M. Melapu, Venkata K. Kwembe, Tor A. Watson, Ella Jackson State University Department of Computer Science Engineering and Technology Jackson United States Jackson State University Department of Mathematics and Statistical Sciences Jackson United States Jackson State University Jackson Public School System Jackson United States

ISBN: (纸本)9798350342154

Introduction: Precision medicine, tailored to individual genetic and environmental attributes, has emerged as a practical approach in healthcare. Allostatic load (AL), reflecting the body's response to stressors, holds promise in predicting susceptibility to chronic illnesses like cardiovascular disease (CVD). This study explored the relationship between CVD and AL, employing big data analytics and machine learning to enhance prediction-accuracy. Objectives: 1. Develop a predictive model using AL measures for CVD prediction. 2. Create a clinical AL tool for personalized medicine. 3. Demonstrate the relationship between AL scores and CVD. Methods: Data from the National Center for Health Statistics encompassed demographic, health, and clinical variables. AL was computed using a count-based method, considering factors like blood pressure, body mass index, glucose, lipids, etc. Machine learning (ML) algorithms, including random forest and logistic regression, were employed to predict CVD based on AL factors. Missing data were managed using imputation methods tailored to variable distributions. Results: The count-based AL method correlated more with age than the weighted method. Both methods showed significant correlations with age, emphasizing the association between AL and aging. AL was positively correlated with CVD, with an 82% accuracy rate in predicting CVD using the random forest algorithm. Black/African American populations displayed the highest correlation between AL and CVD. Conclusions: This study highlights the potential of AL as a predictor for CVD and supports its integration into clinical practice. AL provides a comprehensive view of physiological health and empowers healthcare providers to make informed decisions, particularly in vulnerable populations. Big data analytics and ML enhance prediction accuracy and offer insights into the complex factors contributing to CVD risk. The convergence of healthcare, statistics, and computer programming is essential

关键词： Machine learning

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Security Issues and Workable Solutions in the Context of Sensor Cloud

Security Issues and Workable Solutions in the Context of Sen...

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Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI), International Conference on

作者： Sanjeev Kumar Mandal Manali Gupta Braiehdra Sharma Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Department of Computer Science & Business System Noida Institute Of Engineering and Technology Greater Noida Uttar Pradesh India Department of Computer Science and Engineering Tula's Institute Dehradun India

ISBN: (数字)9798350355338

ISBN: (纸本)9798350355345

Notable tech companies such as Facebook, Microsoft, Apple, Google, and a number of game companies have launched bold plans to bring the metaverse to life. It is inevitable that in the years to come, virtual worlds will change significantly as a result of the metaverse ecosystem's ongoing enhancement and the rapid advancement of new technology. Nonetheless, a great deal of work still has to be done before the metaverse can be incorporated into our everyday lives and physical reality. This paper explores the metaverse's rising importance in the area of medicine and looks at its technological roadmap. We see a metaverse having the potential to drastically change intelligent health care facilities in the future, ushering in a new age of creative healthcare solutions.

关键词： Cloud computing Metaverse Social networking (online) Computational modeling Medical services Companies Games Predictive models Security Context modeling

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Unraveling Academic Failure: Examining the Influence of Course Correlations on Student Performance Through Association Rules Algorithms

Unraveling Academic Failure: Examining the Influence of Cour...

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International Conference on computer and Applications (ICCA)

作者： Hana Safour Mansour Essgaer Almahdi Alshareef Computer Sciences Department Faculty of Sciences Sebha University Sebha Libya Information System Department Faculty of Information Technology Sebha University Sebha Libya Computer Sciences Department Faculty of Information Technology Sebha University Sebha Libya

ISBN: (数字)9798350367560

ISBN: (纸本)9798350367577

The field of information technology has witnessed rapid developments in data mining applications in education. In this context, association rule mining, one of the key aspects of data mining, has emerged as a specialized technique for extracting meaningful insights from sets of frequent items. Academic institutions face persistent challenges, including high failure rates and suboptimal academic achievement among students. This study examined the use of the Apriori algorithm to analyze data within an academic setting, focusing on students enrolled in the department of Zoology at Sebha University. The primary objective was to study the effect of interconnectedness between academic courses on the overall academic performance of students in the department of Zoology. The results revealed that high failure rates were observed consistently during the first two academic years. When the Arabic language course was linked to the botany course for second-semester students, an alarming failure rate of 80% occurred. Similarly, linking Arabic with Chemistry in the same semester resulted in a high failure rate of 75%. The English course during the same period showed a failure rate of 52%, which dropped significantly to 51% when linked to the zoology and chemistry courses. Conversely, linking other zoology courses to chemistry increased the failure rate by 59%, highlighting the subtle effects of course linkage on academic outcomes. These findings for the department of Zoology are of fundamental importance to educators, providing valuable insights into improving student performance and identifying those who need additional support.

关键词： Schedules Chemistry Education Neural networks Zoology Focusing Association rule learning Information technology Random forests Faces

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FlexiFed: Personalized Federated Learning for Edge Clients with Heterogeneous Model Architectures 23

FlexiFed: Personalized Federated Learning for Edge Clients w...

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32nd ACM World Wide Web Conference, WWW 2023

作者： Wang, Kaibin He, Qiang Chen, Feifei Chen, Chunyang Huang, Faliang Jin, Hai Yang, Yun School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computing Technologies Swinburne University of Technology Australia School of Information Technology Deakin University Australia Faculty of Information Technology Monash University Australia Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781450394161

Mobile and Web-of-Things (WoT) devices at the network edge account for more than half of the world's web traffic, making a great data source for various machine learning (ML) applications, particularly federated learning (FL) which offers a promising solution to privacy-preserving ML feeding on these data. FL allows edge mobile and WoT devices to train a shared global ML model under the orchestration of a central parameter server. In the real world, due to resource heterogeneity, these edge devices often train different versions of models (e.g., VGG-16 and VGG-19) or different ML models (e.g., VGG and ResNet) for the same ML task (e.g., computer vision and speech recognition). Existing FL schemes have assumed that participating edge devices share a common model architecture, and thus cannot facilitate FL across edge devices with heterogeneous ML model architectures. We explored this architecture heterogeneity challenge and found that FL can and should accommodate these edge devices to improve model accuracy and accelerate model training. This paper presents our findings and FlexiFed, a novel scheme for FL across edge devices with heterogeneous model architectures, and three model aggregation strategies for accommodating architecture heterogeneity under FlexiFed. Experiments with four widely-used ML models on four public datasets demonstrate 1) the usefulness of FlexiFed;and 2) that compared with the state-of-the-art FL scheme, FlexiFed improves model accuracy by 2.6%-9.7% and accelerates model convergence by 1.24 × -4.04 ×. © 2023 ACM.

关键词： Speech recognition

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Smart Traffic Control system for Dubai: A Simulation Study Using YOLO Algorithms

Smart Traffic Control System for Dubai: A Simulation Study U...

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2023 IEEE International Conference on Electro Information technology, eIT 2023

作者： Al Mamlook, Rabia Emhamed Zahrawi, Mohammad Gharaibeh, Hasan Nasayreh, Ahmad Shresth, Sujeet University of Al-Zawiya Industrial Engineering and Engineering Management Libya Trine University Indiana United States British University in Dubai Engineering and Information Technology Dubai United Arab Emirates Yarmouk University Department of Computer Science Information Technology and Computer Science Irbid211633 Jordan Wayne State University Department of Industrial and System Engineering DetroitMI48201 United States

ISBN: (纸本)9781665493765

Dubai's growing population and public transportation have led to an increase in vehicular traffic and associated challenges. To tackle these issues, there is a rising interest in using machine learning (ML)techniques to improve the city's traffic control system. This study aims to explore the potential of ML in enhancing traffic management and creating a sustainable urban environment. A novel approach to traffic management in Dubai that combines AI and ML algorithms has been proposed, with the potential to significantly improve traffic flow and safety. A simulation study on a smart traffic control system that utilizes YOLO algorithms for real-time vehicle detection and counting is presented. The system optimizes traffic light timings and balances traffic load among different roads to reduce congestion and improve traffic flow. The simulation results demonstrate that the system is highly effective in adapting to changing traffic conditions and reducing congestion. The study concludes that the use of ML algorithms such as YOLO has the potential to revolutionize traffic management in urban areas, leading to a more efficient and sustainable transportation system. Further research and development in this area could bring significant benefits to both motorists and the environment. © 2023 IEEE.

关键词： Smart city

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Automation of data flow class testing using hybrid evolutionary algorithms

Recent Advances in Computer Science and Communications

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Recent Advances in computer Science and Communications 2021年第1期14卷 317-330页

作者： Jain, Neetu Porwal, Rabins Department of Computer Application Faculty of System Sciences Mewar University Chittorgarh India Department of Information Technology Lal Bahadur Shastri Institute of Management New Delhi India

Background: Software testing is a time consuming and costly process. Recent advances in complexity of software have gained attention among researchers towards the automation of generation of test data. Objective: This paper focuses on the structural testing of object oriented paradigm based software and proposes a hybrid approach to automate the class testing applying heuristic algorithms. Methods: The proposed algorithm performs data flow testing of classes applying all defuses adequacy criteria by automatically generating test cases. A nested 2-step methodology is applied using meta-heuristic genetic algorithm and its two variant (GA-variant1 and Ga-variant2) to produce optimized method sequences. Results: An experiment is performed applying proposed algorithm on six test classes. The results suggest that proposed approach with GA-variant1 is better than other techniques in terms of Average d-u coverage and Average iterations. © 2021 Bentham Science Publishers.

关键词： Genetic algorithms

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Metaheuristics Enabled Clustering with Routing Scheme for Wireless Sensor Networks

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computers, Materials & Continua 2022年第12期73卷 5491-5507页

作者： Mashael M.Asiri Saud S.Alotaibi Dalia H.Elkamchouchi Amira Sayed A.Aziz Manar Ahmed Hamza Abdelwahed Motwakel Abu Sarwar Zamani Ishfaq Yaseen Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Wireless Sensor Network(WSN)is a vital element in Internet of Things(IoT)as the former enables the collection of huge quantities of data in energy-constrained *** offers independent access to the target region and performs data collection in an effective *** energy constraints remain a challenging issue in WSN since it operates on in-built *** studies conducted earlier recommended that the energy spent on communication processmust be considerably reduced to improve the efficiency of *** organization and optimal selection of the routes are considered as NP hard optimization problems which can be resolved with the help of metaheuristic *** and routing are considered as effective approaches in enhancing the energy effectiveness and lifespan of *** this background,the current study develops an Improved Duck and Traveller Optimization(IDTO)-enabled cluster-based Multi-Hop Routing(IDTOMHR)technique for ***,IDTO algorithm is exploited for the selection of Cluster Head(CH)and construction of ***,Artificial Gorilla Troops Optimization(ATGO)technique is also used to derive an optimal set of routes to the *** clustering and routing approaches derive a fitness function with the inclusion of multiple input *** proposed IDTOMHR model was experimentally validated for its performance under different *** extensive experimental results confirmed the better performance of IDTOMHR model over other recent approaches.

关键词： Wireless sensor network clustering routing metaheuristics energy efficiency lifetime

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A Novel Determination of Soluble NPK Nutrient Measurement system Using K-means and SOM Methods Compared with UV Visible Spectrophotometer

A Novel Determination of Soluble NPK Nutrient Measurement Sy...

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2023 International Electrical Engineering Congress, iEECON 2023

作者： Tipwong, Wallop Sirikham, Adisorn Konpang, Jessada Kirdpipat, Putthiphong Chongjarearn, Yutana Preutisrunyanont, Oradee Rajamangala University of Technology Krungthep Department of Electrical and Telecommunication Engineering Bangkok Thailand Thammasat School of Engineering Thammasat University Faculty of Engineering Department of Electrical and Computer Engineering Pathum Thani Thailand Dhurakij Pundit University Department of Robotic Engineering and Automation System Bangkok Thailand

ISBN: (纸本)9798350332360

Technologies are currently being used in smart farms to improve productivity. Agricultural innovation is also widespread. Therefore, the development of Internet of Things (IoTs) technologies and devices to collect data is important. Finding essential nutrients for growth such as Nitrogen (N), Phosphorus (P) and Potassium (K) are important factors. Competitiveness is the important key for growing vertically or indoors. However, soil testing can be expensive, complicated and time consuming. The development of devices that can effectively measure nutrient NPK levels could be used over time. This paper presents a suitable clustering using the K-means method and a forecasting method using the Self-Organizing Map (SOM) method to enhance the device's ability to analyze nutrients in real time. A total of 50,688 samples were tested using the K-Means clustering algorithm, and the sample data were also analyzed. A SOM was prepared to predict the accuracy of the analysis. A total of 11,486 samples were tested and analyzed, then the results of the soluble NPK nutrient monitoring system were taken. It has an efficiency of 84.15% compared to using a UV Visible spectrophotometer, allowing data to be predicted during nutrient solubility changes and to operate according to the proposed system. Therefore, the unsupervised machine learning will improve the measurement system of future cultivation systems. © 2023 IEEE.

关键词： Nutrients

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