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Adaptive Deep Learning Model to Enhance Smart Greenhouse Agriculture

Computers, Materials & Continua 2023年第11期77卷 2545-2564页

作者： Medhat A.Tawfeek Nacim Yanes Leila Jamel Ghadah Aldehim Mahmood A.Mahmood Department of Computer Science College of Computer and Information SciencesJouf UniversityP.O.Box 72314SkakaSaudi Arabia Department of Computer Science Faculty of Computers and InformationMenoufia UniversityP.O.Box 6121890MenoufiaEgypt Department of Software Engineering College of Computer and Information SciencesJouf UniversityP.O.Box 72314SkakaSaudi Arabia RIADI Laboratory La Manouba UniversityP.O.Box2010ManoubaTunisia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems and Technology Faculty of Graduate Studies and ResearchCairo UniversityP.O.Box 3753450GizaEgypt

The trend towards smart greenhouses stems from various factors,including a lack of agricultural land area owing to population concentration and housing construction on agricultural land,as well as water *** study proposes building a full farming adaptation model that depends on current sensor readings and available datasets from different agricultural research *** proposed model uses a one-dimensional convolutional neural network(CNN)deep learning model to control the growth of strategic crops,including cucumber,pepper,tomato,and *** proposed model uses the Internet of Things(IoT)to collect data on agricultural operations and then uses this data to control and monitor these operations in real *** helps to ensure that crops are getting the right amount of fertilizer,water,light,and temperature,which can lead to improved yields and a reduced risk of crop *** dataset is based on data collected from expert farmers,the photovoltaic construction process,agricultural engineers,and research *** experimental results showed that the precision,recall,F1-measures,and accuracy of the one-dimensional CNN for the tested dataset were approximately 97.3%,98.2%,97.25%,and 97.56%,*** new smart greenhouse automation system was also evaluated on four crops with a high turnover *** system has been found to be highly effective in terms of crop productivity,temperature management and water conservation.

关键词： Greenhouse wireless sensor network deep learning Internet of Things strategic crops monitoring smart irrigation

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Encryption Algorithm for Securing Non-Disclosure Agreements in Outsourcing Offshore software Maintenance

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Computers, Materials & Continua 2022年第11期73卷 3827-3845页

作者： Atif Ikram Masita Abdul Jalil Amir Bin Ngah Nadeem Iqbal Nazri Kama Azri Azmi Ahmad Salman Khan Yasir Mahmood Assad Alzayed Faculty of Ocean Engineering Technology and Informatics University Malaysia TerengganuKuala TerengganuMalaysia Department of Computer Science&Information Technology Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Department of Software Engineering Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Advanced Informatics Department Razak Faculty of Technology and InformaticsUniversiti Teknologi Malaysia54100Kuala LumpurMalaysia Computer Science and Information Systems Department College of Business Studies Public Authority for Applied Education and Training(PAAET)AdailiyaKuwait

Properly created and securely communicated,non-disclosure agreement(NDA)can resolve most of the common disputes related to outsourcing of offshore software maintenance(OSMO).Occasionally,these NDAs are in the form of *** the work is done offshore,these agreements or images must be shared through the Internet or stored over the *** breach of privacy,on the other hand,is a potential threat for the image owners as both the Internet and cloud servers are not void of *** article proposes a novel algorithm for securing the NDAs in the form of *** an agreement is signed between the two parties,it will be encrypted before sending to the cloud server or travelling through the public network,the *** the image is input to the algorithm,its pixels would be scrambled through the set of randomly generated rectangles for an arbitrary amount of *** confusion effects have been realized through an XOR operation between the confused image,and chaotic ***,5D multi-wing hyperchaotic system has been employed to spawn the chaotic vectors due to good properties of chaoticity it *** machine experimentation and the security analysis through a comprehensive set of validation metric vividly demonstrate the robustness,defiance to the multifarious threats and the prospects for some real-world application of the proposed encryption algorithm for the NDA images.

关键词： Non-disclosure agreement encryption decryption secret key chaoticmap confusion diffusion

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New Mathematical Modelling on BMR and Weight Prediction for Ghanaians

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E-Health Telecommunication systems and Networks 2023年第4期12卷 78-90页

作者： Ian Yaw Asare Ezer Osei Yeboah-Boateng Faculty of Computing & Information Systems Ghana Communication Technology University Accra Ghana Department of Computer Science & Technology Knutsford University College Accra Ghana

Background: Basal Metabolic Rate (BMR) is the quantum of calories needed for optimum body function when at rest. This has long been an indicator of one’s health and the basis for determining the metabolic age of individuals. Many scholastic projects have led to the establishment of mathematical models and inventions that measure the BMR and other body composition parameters. However, existing computations have limitations as they do not offer accurate results for Ghanaians. Aim: The purpose of the study was to model BMR metrics that are most suitable for Ghanaians and to investigate the effect of caloric difference on weight, Lean Body Mass (LBM) and % fat composition that can be implemented with information technology. Research Methods and Procedures: This was an experimental study that adopted a quantitative approach. BMR and body composition were measured in a sample of 242 Ghanaian adults (141 males and 101 females) from 19 to 30 years of age. Body composition was measured using bioelectrical impendence analysis (BIA) in all participants. Each participant was under study for 7 days. A simple linear regression model was used to examine associations between BMR/calorie intake and total body weight and LBM. Results: There was a significant statistical relation between BMR and LBM and between BMR and weight of both men and women. Equations for BMR and weight were established for males and females. Furthermore, caloric intake differences affected changes in total weight as well as differences in % fat composition. Caloric intake however did not affect the difference in LBM. Conclusion: Caloric difference had an impact on total body weight and Lean Body Mass. The model derived from the study predicts weight change and BMR of Ghanaians from 19 to 30 years of age. It is termed the Health and Age Monitoring System (HAMS).

关键词： Basal Metabolic Rate (BMR) Lean Body Mass (LBM) Weight Calories

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Cat Swarm with Fuzzy Cognitive Maps for Automated Soil Classification

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Computer systems Science & Engineering 2023年第2期44卷 1419-1432页

作者： Ashit Kumar Dutta Yasser Albagory Manal Al Faraj Majed Alsanea Abdul Rahaman Wahab Sait Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Kingdom of Saudi Arabia Department of Computing Arabeast CollegesRiyadh11583Kingdom of Saudi Arabia Department of Archives and Communication King Faisal UniversityAl AhsaHofuf31982Kingdom of Saudi Arabia

Accurate soil prediction is a vital parameter involved to decide appro-priate crop,which is commonly carried out by the *** an auto-mated soil prediction tool helps to considerably improve the efﬁcacy of the *** the same time,fuzzy logic(FL)approaches can be used for the design of predictive models,particularly,Fuzzy Cognitive Maps(FCMs)have involved the concept of uncertainty representation and cognitive *** other words,the FCM is an integration of the recurrent neural network(RNN)and FL involved in the knowledge engineering *** this aspect,this paper introduces effective fuzzy cognitive maps with cat swarm optimization for automated soil classiﬁca-tion(FCMCSO-ASC)*** goal of the FCMCSO-ASC technique is to identify and categorize seven different types of *** accomplish this,the FCMCSO-ASC technique incorporates local diagonal extrema pattern(LDEP)as a feature extractor for producing a collection of feature *** addition,the FCMCSO model is applied for soil classiﬁcation and the weight values of the FCM model are optimally adjusted by the use of CSO *** exam-ining the enhanced soil classiﬁcation outcomes of the FCMCSO-ASC technique,a series of simulations were carried out on benchmark dataset and the experimen-tal outcomes reported the enhanced performance of the FCMCSO-ASC technique over the recent techniques with maximum accuracy of 96.84%.

关键词： Soil classiﬁcation intelligent models fuzzy cognitive maps cat swarm optimization fuzzy logic

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ICT for remote data collection: A study on hydrological and weather stations in Afghanistan 7

ICT for remote data collection: A study on hydrological and ...

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7th International Conference on Applied System Innovation, ICASI 2021

作者： Esmat, Ahmad Tamim Graduate School of Information Technology Kobe Institute of Computing Kobe Department of Information Systems Hyogo Japan

ISBN: (纸本)9781665441438

Collecting data from remote hydrological and weather stations in Afghanistan has been a severe challenge to the relevant departments, as the lack of internet infrastructures is evident across the country. The staff members commute to the stations regularly to collect the data manually. This manual practice requires constant human labor and substantial financial resources and is inefficient in accessing timely data and managing the massive amount of collected data afterward. Until recent years, some stations were accessible through a satellite link;however, they are now disconnected due to high cost of the service. On the other hand, telecommunication services in Afghanistan had a rapid growth in the last two decades. People even in rural areas now have access to phone call and SMS;however, internet access has remained limited to the cities and populated areas. Hence, this research proposes the SMS solution to address the challenges of collecting data from remote hydrological and weather stations. A survey was conducted to evaluate the coverage of SMS vs. mobile data on 183 stations. A set of lab experiments were also conducted as a proof of concept. The results show that SMS is an applicable solution, considering current conditions in the region. © 2021 IEEE.

关键词： Data acquisition

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Model Reduction and Chattering Mitigation in Multi-Model Predictive Control for Quadrotor UAVs

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IEEE Access 2025年 13卷 102640-102648页

作者： Mustafa Abro, Ghulam E. Memon, Sufyan Ali Rubab, Saddaf Alshammari, Kaznah Khan, Faheem King Fahd University of Petroleum and Minerals Interdisciplinary Research Centre for Aviation and Space Exploration (IRC-ASE) Dhahran 31261 Saudi Arabia Sejong University Department of Defense Systems Engineering Gwangjin-gu Seoul 05006 South Korea University of Sharjah College of Computing and Informatics Department of Computer Engineering Sharjah 27272 United Arab Emirates Northern Border University Faculty of Computing and Information Technology Department of Information Technology Rafha 91911 Saudi Arabia Gachon University Department of Computer Engineering Seongnam-si 13120 South Korea

This study introduces a multi-model predictive control (MMPC) framework for quadrotor attitude regulation, combining the high-precision capabilities of nonlinear model predictive control (NMPC) with the computing efficiency of linear model predictive control (LMPC). Although NMPC provides excellent regulation, its substantial computational expense restricts its real-time use in high-refresh-rate applications like attitude control. In contrast, LMPC exhibits computational efficiency but encounters challenges with stability and precision in nonlinear dynamics. In order to resolve this discrepancy, we propose a MMPC approach that integrates Principal Component Analysis (PCA) for model reduction and employs linear quadrotor attitude models. This approach reduces the computational complexity and the number of necessary Linear Model Predictive Controllers (LMPCs). To improve performance, we implement an adaptive gain scheduling algorithm to mitigate the chattering effect frequently seen in multi-model approaches. This seamless switching technique diminishes high-frequency noise in brushless DC motor operation, minimises peak overshoot, and mitigates actuator wear, thereby enhancing system stability and durability. Comparative assessments indicate that the suggested MMPC framework attains NMPC-equivalent performance while incurring LMPC-level computational expenses, exceeding the efficacy of conventional incremental nonlinear dynamic inversion, sliding mode control, and traditional NMPC and LMPC methodologies. This study offers a computationally efficient and stable control solution for real-time UAV applications, including model reduction, chattering mitigation, and predictive control optimisation for autonomous aerial systems. © 2013 IEEE.

关键词： Attitude Control Chattering Mitigation Gain Scheduling Multi-model Predictive Control Nonlinear Dynamics Quadrotor UAVs

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Quality of Service (QoS)-Driven Edge computing and Smart Hospitals: A Vision, Architectural Elements, and Future Directions 1

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NIELIT's International Conference on Communication, Electronics and Digital technology, NICE-DT 2023

作者： Buyya, Rajkumar Srirama, Satish N. Mahmud, Redowan Goudarzi, Mohammad Ismail, Leila Kostakos, Vassilis Lab School of Computing and Information Systems The University of Melbourne Melbourne Australia School of Computer and Information Sciences The University of Hyderabad Hyderabad India School of Electrical Engineering Computing and Mathematical Sciences Curtin University Perth Australia Lab Department of Computer Science and Software Engineering National Water and Energy Center United Arab Emirates University Abu Dhabi United Arab Emirates

ISBN: (数字)9789819916993

ISBN: (纸本)9789819916986

The Internet of Things (IoT) paradigm is drastically changing our world by making everyday objects an integral part of the Internet. This transformation is increasingly being adopted in the healthcare sector, where Smart Hospitals are now relying on IoT technologies to track staff, patients, devices, and equipment, both within a hospital and beyond. This paradigm opens the door to new innovations for creating novel types of interactions among objects, services, and people in smarter ways to enhance the quality of patient services and the efficient utilisation of resources. However, the realisation of real-time IoT applications in health care and, ultimately, the development of Smart Hospitals are constrained by their current Cloud-based computing environment. Edge computing emerged as a new computing model that harnesses Edge-based resources alongside Clouds for real-time IoT applications. It helps to capitalise on the potential economic impact of the IoT paradigm of $11 trillion per year, with a trillion IoT devices deployed by 2025 to sense, manage, and monitor the hospital systems in real time. This vision paper proposes new algorithms and software systems to tackle important challenges in Edge computing-enabled Smart Hospitals, including how to manage and execute diverse real-time IoT applications and how to meet their diverse and strict Quality of Service (QoS) requirements in hospital settings. The vision we outline can help tackle timely challenges that hospitals increasingly face. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Quality of service

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Grade It: A Quantitative Essay Grading System 6th

Grade It: A Quantitative Essay Grading System

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6th International Conference on Innovative computing and Communication, ICICC 2023

作者： Vanga, Roopchand Reddy Bharath, M.S. Sindhu, C. Vadivu, G. Hsu, Hsiu Chun Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur603203 India Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur603203 India Department of Information Management National Chung Cheng University Minxiong Taiwan

ISBN: (纸本)9789819930098

Automated writing evaluation system that employs automated essay scoring technologies generates a rating to the writings which helps students with self-assessment. It provides an efficient and easy way for grading essays, which usually takes abundance of time to be evaluated by human graders;this can be greatly useful for educational institutions like schools and colleges. It provides ratings for essays based on the grammatical errors and topic relevancy by using specific tools implemented in the auto grading system. The generated result will help students grading essays which helps students in self-assessment, this will be useful for the student to understand the mistakes that he makes which will be pinpointed by the system in matter of time, not only that the system also displays the strengths of the user, but also the system will improve the accuracy compared to already existing automated grading system. The system uses scores from multiple features (handcraft features, coherence score, prompt-relevant score, and semantic score). These key features illustrated above will provide enough information on the essay for the student. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Semantics

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PRAGA: Prototype-aware Graph Adaptive Aggregation for Spatial Multi-modal Omics Analysis 39

PRAGA: Prototype-aware Graph Adaptive Aggregation for Spatia...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Huang, Xinlei Ma, Zhiqi Meng, Dian Liu, Yanran Ruan, Shiwei Sun, Qingqiang Zheng, Xubin Qiao, Ziyue School of Computing and Information Technology Great Bay University Dongguan523000 China Guangdong Provincial Key Laboratory of Mathematical and Neural Dynamical Systems Dongguan523000 China Shenzhen518000 China College of Business City University of Hong Kong Hong Kong999077 Hong Kong

ISBN: (纸本)157735897X

Spatial multi-modal omics technology, highlighted by Nature Methods as an advanced biological technique in 2023, plays a critical role in resolving biological regulatory processes with spatial context. Recently, graph neural networks based on K-nearest neighbor (KNN) graphs have gained prominence in spatial multi-modal omics methods due to their ability to model semantic relations between sequencing spots. However, the fixed KNN graph fails to capture the latent semantic relations hidden by the inevitable data perturbations during the biological sequencing process, resulting in the loss of semantic information. In addition, the common lack of spot annotation and class number priors in practice further hinders the optimization of spatial multi-modal omics models. Here, we propose a novel spatial multi-modal omics resolved framework, termed PRototype-Aware Graph Adaptative Aggregation (PRAGA). PRAGA constructs a dynamic graph to capture latent semantic relations and comprehensively integrate spatial information and feature semantics. The learnable graph structure can also denoise perturbations by learning cross-modal knowledge. Moreover, a dynamic prototype contrastive learning is proposed based on the dynamic adaptability of Bayesian Gaussian Mixture Models to optimize the multi-modal omics representations for unknown biological priors. Quantitative and qualitative experiments on simulated and real datasets with 7 competing methods demonstrate the superior performance of PRAGA. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Graph neural networks

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Enhancing Low-Dose CT Image Quality Through Deep Learning: A DoG-Sharpened U-Net Approach With Attention Mechanism

Enhancing Low-Dose CT Image Quality Through Deep Learning: A...

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2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent systems, ICETSIS 2024

作者： Zubair, Muhammad Rais, Helmi B Md Ullah, Fasee Yousafzai, Arsalaan Khan Hassan, Farrukh Universiti Teknologi Petronas Department of Computer and Information Sciences Seri Iskandar32610 Malaysia Universiti Teknologi Petronas Department of Civil and Enviromental Engineering Seri Iskandar32610 Malaysia Sunway University Bandar University Sunway Department of Computing and Information Systems School of Engineering and Technology Selangor Darul Ehsan 47500 Malaysia

ISBN: (纸本)9798350372229

Computed Tomography (CT) scans utilize electromagnetic radiation. However, the excessive exposure of a patient's body during CT acquisition poses potential health risks. Subsequently, the integration of low-dose CT scans has resulted in an escalation of noise, artifacts, and a discernible decline in the overall quality of CT imaging, significantly impacting the diagnostic capabilities of Computer-Aided Diagnosis (CAD) system. Removing these noises and artifacts while preserving critical information is a challenging task. Traditional noise reduction algorithms are expensive, produce blurry results, and rely on challenging sinogram data. As a result, deep learning-based image-denoising approaches have emerged. This study introduces a DoG-UNet+ model which incorporates a novel layer called the 'Difference of Gaussians (DoG) Sharpening Layer' into the U-Net architecture. This layer utilizes two distinct convolutional kernels, termed 'fat' and 'skinny,' aimed to capturing diverse scales of features. To increase the clinical detection precision, an attention mechanism has been added to focus on the critical features in CT images. This model has been compared with the most recent algorithms based on the Peak Signal-to-Noise Ratio (PSNR), Root Mean Squared Error (RMSE), and Structural Similarity Index (SSIM). The outcomes showed superior performance of DoG-UNet+ model, showcasing promising and notable results compared to existing methods. © 2024 IEEE.

关键词： Computed Tomography Deep Learning Image Enhancement LDCT Noise Removal

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