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Deep Learning Enabled Intelligent Healthcare Management System in Smart Cities Environment

Computers, Materials & Continua 2023年第2期74卷 4483-4500页

作者： Hanan Abdullah Mengash Lubna A.Alharbi Saud S.Alotaibi Sarab AlMuhaideb Nadhem Nemri Mrim M.Alnfiai Radwa Marzouk Ahmed S.Salama Mesfer Al Duhayyim Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTabuk UniversityTabuk71491Saudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Information Systems College of Science and Arts at MuhayelKing Khalid UniversitySaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Electrical Engineering Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

In recent times,cities are getting smart and can be managed effectively through diverse architectures and *** cities have the ability to support smart medical systems that can infiltrate distinct events(i.e.,smart hospitals,smart homes,and community health centres)and scenarios(e.g.,rehabilitation,abnormal behavior monitoring,clinical decision-making,disease prevention and diagnosis postmarking surveillance and prescription recommendation).The integration of Artificial Intelligence(AI)with recent technologies,for instance medical screening gadgets,are significant enough to deliver maximum performance and improved management services to handle chronic *** latest developments in digital data collection,AI techniques can be employed for clinical decision making *** the other hand,Cardiovascular Disease(CVD)is one of the major illnesses that increase the mortality rate across the ***,wearables can be employed in healthcare systems that instigate the development of CVD detection and *** this motivation,the current study develops an Artificial Intelligence Enabled Decision Support System for CVD Disease Detection and Classification in e-healthcare environment,abbreviated as AIDSS-CDDC *** proposed AIDSS-CDDC model enables the Internet of Things(IoT)devices for healthcare data ***,the collected data is saved in cloud server for *** by,training 4484 CMC,2023,vol.74,no.2 and testing processes are executed to determine the patient’s health *** accomplish this,the presented AIDSS-CDDC model employs data preprocessing and Improved Sine Cosine Optimization based Feature Selection(ISCO-FS)*** addition,Adam optimizer with Autoencoder Gated RecurrentUnit(AE-GRU)model is employed for detection and classification of *** experimental results highlight that the proposed AIDSS-CDDC model is a promising performer compared to other existing models.

关键词： Smart cities e-healthcare decision support system cardiovascular disease deep learning feature selection

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A Prior Embedding-Driven Architecture for Long Distance Blind Iris Recognition

arXiv

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arXiv 2024年

作者： Xiong, Qi Zhang, Xinman Shen, Jun International College Hunan University of Arts and Sciences Changde415000 China School of Automation Science and Engineering Faculty of Electronic and Information Engineering MOE Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong University Shaanxi Xi'An710049 China School of Computing and Information Technology University of Wollongong WollongongNSW2522 Australia

Blind iris images, which result from unknown degradation during the process of iris recognition at long distances, often lead to decreased iris recognition rates. Currently, little existing literature offers a solution to this problem. In response, we propose a prior embedding-driven architecture for long distance blind iris recognition. We first proposed a blind iris image restoration network called Iris-PPRGAN. To effectively restore the texture of the blind iris, Iris-PPRGAN includes a Generative Adversarial Network (GAN) used as a Prior Decoder, and a DNN used as the encoder. To extract iris features more efficiently, we then proposed a robust iris classifier by modifying the bottleneck module of InsightFace, which called Insight-Iris. A low-quality blind iris image is first restored by Iris-PPRGAN, then the restored iris image undergoes recognition via Insight-Iris. Experimental results on the public CASIA-Irisdistance dataset demonstrate that our proposed method significantly superior results to state-of-the-art blind iris restoration methods both quantitatively and qualitatively, Specifically, the recognition rate for long-distance blind iris images reaches 90% after processing with our methods, representing an improvement of approximately ten percentage points compared to images without restoration. Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

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A Cascaded Design of Best Features Selection for Fruit Diseases Recognition

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Computers, Materials & Continua 2022年第1期70卷 1491-1507页

作者： Faiz Ali Shah Muhammad Attique Khan Muhammad Sharif Usman Tariq Aimal Khan Seifedine Kadry Orawit Thinnukool Department of Computer Science COMSATS University IslamabadWah Campus47080Pakistan Department of Computer Science HITEC University TaxilaTaxila47040Pakistan College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia National University of Science and Technology Islamabad IslamabadPakistan Faculty of Applied Computing and Technology Noroff University CollegeKristiansandNorway Research Group of Embedded Systems and Mobile Application in Health Science College of ArtsMedia and TechnologyChiang Mai UniversityChiang Mai50200Thailand

Fruit diseases seriously affect the production of the agricultural sector,which builds financial pressure on the country’s *** manual inspection of fruit diseases is a chaotic process that is both time and cost-consuming since it involves an accurate manual inspection by an ***,it is essential that an automated computerised approach is developed to recognise fruit diseases based on leaf *** to the literature,many automated methods have been developed for the recognition of fruit diseases at the early ***,these techniques still face some challenges,such as the similar symptoms of different fruit diseases and the selection of irrelevant *** processing and deep learning techniques have been extremely successful in the last decade,but there is still room for improvement due to these ***,we propose a novel computerised approach in this work using deep learning and featuring an ant colony optimisation(ACO)based *** proposed method consists of four fundamental steps:data augmentation to solve the imbalanced dataset,fine-tuned pretrained deep learning models(NasNetMobile andMobileNet-V2),the fusion of extracted deep features using matrix length,and finally,a selection of the best features using a hybrid ACO and a Neighbourhood Component Analysis(NCA).The best-selected features were eventually passed to many classifiers for final *** experimental process involved an augmented dataset and achieved an average accuracy of 99.7%.Comparison with existing techniques showed that the proposed method was effective.

关键词： Fruits diseases data augmentation deep learning features fusion feature selection

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Empirical Investigation of Discipline-Specific Skills Required for the Employability of Built Environment Graduates

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International Journal of Construction Education and Research 2023年第4期19卷 460-479页

作者： Aliu, John Aghimien, Douglas Aigbavboa, Clinton Oke, Ayodeji Ebekozien, Andrew Temidayo, Osunsanmi Institute for Resilient Infrastructure Systems College of Engineering University of Georgia Athens GA United States School of Art Design and Architecture Faculty of Arts Design and Humanities De Montfort University Leicester United Kingdom CIDB Centre of Excellence Faculty of Engineering and the Built Environment University of Johannesburg Johannesburg South Africa Department of Quantity Surveying Federal University of Technology Akure Akure Nigeria CIDB centre of Excellence University of Johannesburg South Africa Department of Construction Management and Quantity Surveying University of Johannesburg Johannesburg South Africa School of Social Sciences Universiti Sains Malaysia Gelugor Malaysia Department of Quantity Surveying Auchi Polytechnic Auchi Nigeria School of Computing Engineering and Built Environment Edinburgh Napier University United Kingdom

As the world continues to experience significant and dynamic changes, the concept of graduate employability remains a well-discussed subject in the body of knowledge. Consequently, the concept has attracted the interest of educators, policymakers, researchers and graduates themselves. As a vital cog in the employability conversation, the quality of present-day graduates is highly dependent on the effectiveness of training received from higher education institutions. This formal training provides learners with discipline-specific skills (academic skills) and knowledge which helps them obtain a firm foundation in their chosen discipline or profession. This study seeks to unearth the various discipline-specific skills (DSS) that built-environment graduates need to possess to thrive in the labor market after graduation. A quantitative research approach was adopted to achieve this study’s objective with close-ended questionnaires developed and administered to built environment professionals based in the Gauteng province of South Africa. Retrieved data were analyzed using several statistical tools such as percentage, frequency, Mean Item Score, One-Sample T-test and Exploratory Factor Analysis. Findings revealed four clusters highlighting the key DSS required by built environment graduates. These include lifelong learning, hands-on experience, digital literacy and knowledge of the subject area. The outcomes of this study will be beneficial to several stakeholders involved in construction education and employability skills discussion. © 2022 Associated Schools of Construction.

关键词： Built environment construction education discipline-specific skills employability employability skills engineering education graduateness pedagogy

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An Efficient Three-Factor Authenticated Key Agreement Technique Using FCM Under HC-IoT Architectures

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Computers, Materials & Continua 2022年第7期72卷 1373-1389页

作者： Chandrashekhar Meshram Agbotiname Lucky Imoize Sajjad Shaukat Jamal Parkash Tambare Adel R.Alharbi Iqtadar Hussain Department of Post Graduate Studies and Research in Mathematics Jayawanti Haksar Government Post-Graduation CollegeCollege of Chhindwara UniversityBetul460001M.P.India Department of Electrical and Electronics Engineering Faculty of EngineeringUniversity of LagosAkokaLagos100213Nigeria Department of Electrical Engineering and Information Technology Institute of Digital CommunicationRuhr University44801BochumGermany Department of Mathematics College of ScienceKing Khalid UniversityAbhaSaudi Arabia Water Resources&Applied Mathematics Research Lab Nagpur440027India College of Computing and Information Technology University of TabukTabuk71491Saudi Arabia Mathematics Program Department of MathematicsStatistics and PhysicsCollege of Arts and SciencesQatar University2713DohaQatar

The Human-Centered Internet of Things(HC-IoT)is fast becoming a hotbed of security and privacy *** users can establish a common session key through a trusted server over an open communication channel using a three-party authenticated key *** of the early authenticated key agreement systems relied on pairing,hashing,or modular exponentiation processes that are computationally intensive and *** order to address this problem,this paper offers a new three-party authenticated key agreement technique based on fractional chaotic *** new scheme uses fractional chaotic maps and supports the dynamic sensing of HC-IoT devices in the network architecture without a password *** projected security scheme utilized a hash function,which works well for the resource-limited HC-IoT *** results show that our new technique is resistant to password guessing attacks since it does not use a ***,our approach provides users with comprehensive privacy protection,ensuring that a user forgery attack causes no ***,our new technique offers better security features than the techniques currently available in the literature.

关键词： Three-party authenticated key agreement anonymity fractional chaotic maps Chebyshev polynomial password table human-centered internet of things(HC-IoT)

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Hyper Elliptic Curve Based Certificateless Signcryption Scheme for Secure IIoT Communications

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Computers, Materials & Continua 2022年第5期71卷 2515-2532页

作者： Usman Ali Mohd Yamani Idna Idris Jaroslav Frnda Mohamad Nizam Bin Ayub Roobaea Alroobaea Fahad Almansour Nura Modi Shagari Insaf Ullah Ihsan Ali Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversity of MalayaKuala Lumpur50603Malaysia Department of Computer Science University of SwatSaidu Sharif19130Pakistan Center for Research in Mobile Cloud Computing University of MalayaKuala Lumpur50603Malaysia Department of Quantitative Methods and Economic Informatics Faculty of Operation and Economics of Transport and CommunicationsUniversity of Zilina01026 ZilinaSlovakia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science College of Sciences and Arts in RassQassim UniversityBuraydah51452Saudi Arabia Department of Computer Science Hamdard Institute of Engineering and TechnologyIslamabad44000Pakistan

Industrial internet of things (IIoT) is the usage of internet of things(IoT) devices and applications for the purpose of sensing, processing andcommunicating real-time events in the industrial system to reduce the unnecessary operational cost and enhance manufacturing and other industrial-relatedprocesses to attain more profits. However, such IoT based smart industriesneed internet connectivity and interoperability which makes them susceptibleto numerous cyber-attacks due to the scarcity of computational resourcesof IoT devices and communication over insecure wireless channels. Therefore, this necessitates the design of an efficient security mechanism for IIoTenvironment. In this paper, we propose a hyperelliptic curve cryptography(HECC) based IIoT Certificateless Signcryption (IIoT-CS) scheme, with theaim of improving security while lowering computational and communicationoverhead in IIoT environment. HECC with 80-bit smaller key and parameterssizes offers similar security as elliptic curve cryptography (ECC) with 160-bitlong key and parameters sizes. We assessed the IIoT-CS scheme security byapplying formal and informal security evaluation techniques. We used Realor Random (RoR) model and the widely used automated validation of internet security protocols and applications (AVISPA) simulation tool for formalsecurity analysis and proved that the IIoT-CS scheme provides resistance tovarious attacks. Our proposed IIoT-CS scheme is relatively less expensivecompared to the current state-of-the-art in terms of computational cost andcommunication overhead. Furthermore, the IIoT-CS scheme is 31.25% and 51.31% more efficient in computational cost and communication overhead,respectively, compared to the most recent protocol.

关键词： IoT security authentication protocols hyperelliptic curve cryptography certificateless public key cryptography

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Effective Crowd Anomaly Detection Through Spatio-temporal Texture Analysis

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International Journal of Automation and computing 2019年第1期16卷 27-39页

作者： Yu Hao Zhi-Jie Xu Ying Liu Jing Wang Jiu-Lun Fan School of Computer Science and Technology Xi'an University of Posts and Telecommunications School of Computing and Engineering University of Huddersfield Faculty of Arts Computing Engineering and Sciences Sheffield Hallam University

Abnormal crowd behaviors in high density situations can pose great danger to public safety. Despite the extensive installation of closed-circuit television(CCTV) cameras, it is still difficult to achieve real-time alerts and automated responses from current systems. Two major breakthroughs have been reported in this research. Firstly, a spatial-temporal texture extraction algorithm is developed. This algorithm is able to effectively extract video textures with abundant crowd motion details. It is through adopting Gaborfiltered textures with the highest information entropy values. Secondly, a novel scheme for defining crowd motion patterns(signatures)is devised to identify abnormal behaviors in the crowd by employing an enhanced gray level co-occurrence matrix model. In the experiments, various classic classifiers are utilized to benchmark the performance of the proposed method. The results obtained exhibit detection and accuracy rates which are, overall, superior to other techniques.

关键词： Crowd behavior spatial-temporal texture gray level co-occurrence matrix information entropy

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Dynamics predictive of neurodegenerative diseases by using the generalized Caputo operator through computational and multiscale modeling

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Journal of Applied Mathematics and computing 2025年 1-32页

作者： Talib, Ammara Farman, Muhammad Ibrahim, Abdullahi Umar Nisar, Kottakkaran Sooppy Sambas, Aceng Department of Biomedical Engineering Near East University Nicosia Northern Cyprus Turkey Department of Mathematics Faculty of Arts and Sciences Near East University Nicosia Northern Cyprus Turkey Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Campus Besut Malaysia Research Center of Applied Mathematics Khazar University Baku Azerbaijan Department of Mathematics College of Science and Humanities Prince Sattam Bin Abdulaziz University Alkharj Saudi Arabia Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan Department of Mechanical Engineering Universitas Muhammadiyah Tasikmalaya Tamansari Gobras Indonesia

Neurodegenerative diseases, such as Alzheimer’s and Parkinson’s, pose a significant global health burden, progressively impairing cognitive and motor functions. The complex interplay between neuronal health, immune response, and pathological protein accumulation necessitates advanced mathematical modeling for better understanding and intervention strategies. In order to analyze brain diseases, we have developed a five-compartment nonlinear mathematical model to observe the dynamics and focuses on extracellular $$\alpha$$ -synuclein function, functioning neurons, infected neurons, activated microglia density, and $$T$$ -cells, which may contribute to neuroinflammation in neurodegenerative contexts. Since our model hasn’t been put forth in the literature before, it is novel. In addition, we modified the recently created model by adding fractional order derivatives to better comprehend the relationship between immune response dynamics and neuronal health in brain disorders. To better comprehend these intricate processes and advance medical therapies, our study combines novel mathematical methods with computer simulations. Stability analysis confirms the existence of a feasible disease-free equilibrium. In contrast, sensitivity analysis highlights the critical influence of parameters such as neuron production $$(\Pi_N)$$ , infection rate $$(\gamma)$$ , and microglial activation $$(\Theta)$$ . Numerical simulations reveal that lower fractional orders $$(\nu < 1)$$ slow disease progression, indicating the long-term impact of neuroinflammatory feedback mechanisms. To assess the computational efficiency and accuracy of the proposed fractional-order model, we compare numerical solutions obtained using the Lagrange interpolation method and the ODE45 solver. The results demonstrate that the Lagrange interpolation method exhibits superior accuracy and stability in capturing the long-term behavior of neurodegenerative progression, whereas ODE45, a classical numerical approach

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Dijkstra’s Algorithm with Bellman-Ford for Shortest Path Discovery in Big Data

Dijkstra’s Algorithm with Bellman-Ford for Shortest Path Di...

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Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： M. Karthikeyan Satheeshkumar Sekar H. Anwer Basha P. Epsiba E. N. Ganesh B. Meenakshi Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Mphasis Corporation Chandler Arizona USA Department of Computer Science Saveetha College of Liberal Arts & Sciences SIMATS University Chennai Tamil Nadu India Department of Electronics and Communication Engineering Sri Indu College of Engineering and Technology Hyderabad Telangana India Department of Electronics and Communication Engineering St. Peters Institute of Higher Education and Research (SPIHER) Avadi Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Sri Sairam Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798350368413

ISBN: (纸本)9798350368420

Many applications, from logistics to social networks, rely on quickly navigating the shortest pathways inside large-scale graphs. For shortest route finding in huge data settings, this study aims to compare Dijkstra’s method with the Bellman-Ford algorithm. Applying these methods to large datasets will allow us to compare their speed, accuracy, and scalability. Graph density and negative weight cycles are two of the intrinsic properties of big data settings that will be used to determine which method is most suited to each. Optimal algorithm selection for given data situations is guided by insights into each algorithm’s operational strengths and shortcomings, which are revealed via rigorous testing and performance metrics analysis. This comparison helps real-world applications of each method and provides valuable information for future advancements in large data processing shortest route calculation algorithms. In a study comparing Dijkstra’s Algorithm and Bellman-Ford using the RandomGraph1 dataset, the results showed that for nodes $100-500$, the former’s ms value ranged from 5.2 to 52.3 and the latter’s ms value ranged from 6.8 to 68.9. Similarly, for nodes 100-500, the MB value range for Dijkstra’s Algorithm was 10.2-46.7 and for Bellman-Ford, it was 14.8-71.9. Data from a different SparseGraph1 dataset reveals that, for values between 100 and 500, Dijkstra’s Algorithm has a percentage range of 98.5 to 99.8, while for Bellman-Ford, it varies from 97.2 to 99.3.

关键词： Measurement Social networking (online) Navigation Scalability Big Data Data processing Computational efficiency Artificial intelligence Testing Logistics

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Enhancing Students’ Learning Motivation and Comprehension by Reflecting on the Practical Applications of Learning Materials in an Education Learning Journal

Enhancing Students’ Learning Motivation and Comprehension b...

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Science, engineering and Business for Sustainable Development Goals (SEB-SDG), International Conference on

作者： Samuel-Soma M. Ajibade Lislee Valle Edralin General Joshlen Baclayon-Lim Sarah Jane Colina Kayode Akinlekan Akintoye Anthonia Oluwatosin Adediran Dept of Computing and Information Systems Sunway University Selangor Malaysia Dept of Comp. Engineering Istanbul Ticaret Univ Turkey College of Education Arts and Sciences Cebu Technological University Danao City Cebu Philippines Dept of Computer Science The Federal Polytechnic Ado Ekiti Ekiti State Faculty of Architecture and Urban Design Federal University of Uberlandia Uberlandia Brazil

ISBN: (数字)9798350358155

ISBN: (纸本)9798350358162

Evaluating the learning materials' personal utility and worth is vital for cultivating motivation and involvement in high-quality learning experiences. The researchers set out to find out how using a personal-utility prompt in journal writing affected the students' motivation to learn and their understanding of biological concepts in the classroom. Sixty high school students from a Nigerian school participated in the quasi-experimental field investigation. Students were asked to maintain a weekly learning journal starting from the first week of the semester for a total of eight weeks. Students were given short instructions on how to write their journal entries, which might or might not have included a suggestion about how the entries could benefit them individually. The results show that having students think about the educational resources' personal value was a great success with the personal-utility prompt. Consequently, students whose teachers allowed them to employ a personal-utility prompt had more interest in learning and had more success with comprehension than those whose teachers did not. A clear and effective strategy for improving students' motivation and understanding in secondary science education is to have them write reflective journals in which they consider the relevance and usefulness of various learning materials.

关键词： Immunology Education Writing Mathematics Biology Sustainable development Pupils

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