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Variant Wasserstein Generative Adversarial Network Applied on Low Dose CT Image Denoising

computers, Materials & Continua 2023年第5期75卷 4535-4552页

作者： Anoud A.Mahmoud Hanaa A.Sayed Sara S.Mohamed Department of Mathematics and Computer Science Faculty of ScienceNew Valley UniversityNew Valley72511Egypt Department of Computer Science College of Computer Science and Engineering Taibah UniversityMedina42221KSA Department of Mathematics Faculty of ScienceAssiut UniversityAssiut71516Egypt

Computed Tomography(CT)images have been extensively employed in disease diagnosis and treatment,causing a huge concern over the dose of radiation to which patients are *** the radiation dose to get a better image may lead to the development of genetic disorders and cancer in the patients;on the other hand,decreasing it by using a Low-Dose CT(LDCT)image may cause more noise and increased artifacts,which can compromise the ***,image reconstruction from LDCT image data is necessary to improve radiologists’judgment and *** study proposed three novel models for denoising LDCT images based on Wasserstein Generative Adversarial Network(WGAN).They were incorporated with different loss functions,including Visual Geometry Group(VGG),Structural Similarity Loss(SSIM),and Structurally Sensitive Loss(SSL),to reduce noise and preserve important information on LDCT images and investigate the effect of different types of loss ***,experiments have been conducted on the Graphical Processing Unit(GPU)and Central Processing Unit(CPU)to compare the performance of the proposed *** results demonstrated that images from the proposed WGAN-SSIM,WGAN-VGG-SSIM,and WGAN-VGG-SSL were denoised better than those from state-of-the-art models(WGAN,WGAN-VGG,and SMGAN)and converged to a stable equilibrium compared with WGAN and *** proposed models are effective in reducing noise,suppressing artifacts,and maintaining informative structure and texture details,especially WGAN-VGG-SSL which achieved a high peak-signalto-noise ratio(PNSR)on both GPU(26.1336)and CPU(25.8270).The average accuracy of WGAN-VGG-SSL outperformed that of the state-ofthe-art methods by 1 *** prove that theWGAN-VGG-SSL is more stable than the other models on both GPU and CPU.

关键词： Machine learning deep learning image denoising low dose CT loss function

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Bald Eagle Search Algorithm with Hierarchical Deep Learning for Internet of Things Assisted Sleep Quality Recognition

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SN computer science 2024年第5期5卷 564页

作者： Duhayyim, Mesfer Al Sharif, Mahir Mohammed Munjal, Muskaan Hilal, Anwer A. Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj 16273 Saudi Arabia Department of Computer Science Faculty of Computer Science and Information Technology Omdurman Islamic University Omdurman Sudan Computer Engineering Thapar Institute of Engineering and Technology Punjab Patiala India Department of Information System Faculty of Computer Science and Information Technology Omdurman Islamic University Omdurman Sudan

Sleep quality prediction in Internet of Things (IoT) involves leveraging a system of interrelated devices to gather as well as analyse related data. Smart devices like wearable devices or smart mattresses endlessly monitor many parameters such as body movement, heart rate, and environmental situations in bedroom. Real-time recommendations and feedback transported to consumers, allowing them to create informed lifestyle changes for superior sleep hygiene. As IoT ecosystem continues to develop, a combination of various data streams as well as classy analytics holds assurance for further refining sleep quality forecasts and improving overall well-being. The article designs a Bald Eagle Search Algorithm with Hierarchical Deep Learning for Internet of Things Assisted Sleep Quality Recognition (BESHDL-SQP) technique. The purpose of the BESHDL-SQP technique is to recognize and classify the quality of the sleep level in the IoT environment. In the presented BESHDL-SQP technique, the preliminary stage of data pre-processing using min–max normalization involved to normalize the input data. For predicting sleep quality, hierarchical long short-term memory (LSTM) technique exploited. Lastly, BES model utilized for optimal hyperparameter selection of LSTM method which helps in attaining enhanced results. To validate better performance of BESHDL-SQP technique, a huge range of simulation analysis is carried out. The extensive results stated that the BESHDL-SQP technique exhibits supreme performance over other models. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Bald eagle search Internet of things LSTM Sleep quality recognition Smart devices

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Performance Analysis of Existing SDN Load Balancing Controller Using Mininet 1

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International Conference on Advanced engineering, Technology and Applications, ICAETA 2024

作者： Baniya, Pashupati Nand, Parma Bhushan, Bharat Hameed, Alaa Ali Jamil, Akhtar Agrawal, Atul Department of Computer Science and Engineering School of Engineeringand Technology IEC College of Engineering and Technology Greater Noida India Department of Computer Science and Engineering School of Engineering and Technology ITS Engineering College Greater Noida India Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida India Department of Computer Engineering Faculty of Engineering and Natural Sciences Istinye University Istanbul34396 Turkey Department of Computer Science National University of Computer and Emerging Sciences Islamabad44000 Pakistan

ISBN: (数字)9783031709241

ISBN: (纸本)9783031709234

Due to an increase in the load of network, load balancing service, i.e., a service that gives an equal volume of each task assignment to each of the servers in data centers, it is usually performed by the specialized hardware. Networks have to adjust due to the load and also as a result of dealing with faults and complexity which are provided in the implementation and configuration process. Network engineers or administrators must be aware of selecting controllers according to the network requirement, which can lead to performance issues, scalability challenges, reliability and fault tolerance and many more problems in the network. To tackle this problem, this research analyses four different controllers in an experiment: In-band, open flow, OVS, and remote controller, which are the key aspects. The paper shows how these controllers, which are detected through a comprehensive evaluation of network performance attributes (e.g., Throughput and Jitter), affect the overall system’s performance. This information provides beneficial information for the researchers and practitioners who wish to make informed choices whenever they select suitable controls for specific network needs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Remote control

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Enhancing Evapotranspiration Estimation: A Bibliometric and Systematic Review of Hybrid Neural Networks in Water Resource Management

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computer Modeling in engineering & sciences 2025年第2期142卷 1109-1154页

作者： Moein Tosan Mohammad Reza Gharib Nasrin Fathollahzadeh Attar Ali Maroosi Department of Water Science and Engineering Faculty of AgricultureUniversity of BirjandBirjand***Iran Department of Mechanical Engineering University of Torbat HeydariehTorbat Heydarieh***Iran Department of Water Engineering Faculty of AgricultureUrmia UniversityUrmia***Iran Department of Computer Engineering University of Torbat HeydariehTorbat Heydarieh***Iran

Accurate estimation of evapotranspiration(ET)is crucial for efficient water resource management,particularly in the face of climate change and increasing water *** study performs a bibliometric analysis of 352 articles and a systematic review of 35 peer-reviewed papers,selected according to PRISMA guidelines,to evaluate the performance of Hybrid Artificial Neural Networks(HANNs)in ET *** findings demonstrate that HANNs,particularly those combining Multilayer Perceptrons(MLPs),Recurrent Neural Networks(RNNs),and Convolutional Neural Networks(CNNs),are highly effective in capturing the complex nonlinear relationships and tem-poral dependencies characteristic of hydrological *** hybrid models,often integrated with optimization algorithms and fuzzy logic frameworks,significantly improve the predictive accuracy and generalization capabilities of ET *** growing adoption of advanced evaluation metrics,such as Kling-Gupta Efficiency(KGE)and Taylor Diagrams,highlights the increasing demand for more robust performance assessments beyond traditional *** the promising results,challenges remain,particularly regarding model interpretability,computational efficiency,and data *** research should prioritize the integration of interpretability techniques,such as attention mechanisms,Local Interpretable Model-Agnostic Explanations(LIME),and feature importance analysis,to enhance model transparency and foster stakeholder ***,improving HANN models’scalability and computational efficiency is crucial,especially for large-scale,real-world *** such as transfer learning,parallel processing,and hyperparameter optimization will be essential in overcoming these *** study underscores the transformative potential of HANN models for precise ET estimation,particularly in water-scarce and climate-vulnerable *** integrating CNNs for automatic feature extraction and leveraging hybr

关键词： Artificial neural networks bibliometric analysis evapotranspiration hybrid models research trends systematic literature review water resources management

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Regression analysis of squeezing-induced hybrid nanofluid flow in Darcy-Forchheimer porous medium

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Applied Mathematics and Mechanics(English Edition) 2025年第1期46卷 193-208页

作者： K.MUHAMMAD M.SARFRAZ Department of Humanities and Sciences School of Electrical Engineering and Computer ScienceNational University of Sciences and TechnologyIslamabad 44000Pakistan Department of Mathematics Quaid-i-Azam UniversityIslamabad 44000Pakistan

This article presents a mathematical model addressing a scenario involving a hybrid nanofluid flow between two infinite parallel *** plate remains stationary,while the other moves downward at a squeezing *** space between these plates contains a Darcy-Forchheimer porous medium.A mixture of water-based fluid with gold(Au)and silicon dioxide(Si O2)nanoparticles is *** contrast to the conventional Fourier's heat flux equation,this study employs the Cattaneo-Christov heat flux equation.A uniform magnetic field is applied perpendicular to the flow direction,invoking magnetohydrodynamic(MHD)***,the model accounts for Joule heating,which is the heat generated when an electric current passes through the *** problem is solved via NDSolve in *** and statistical analyses are conducted to provide insights into the behavior of the nanomaterials between the parallel plates with respect to the flow,energy transport,and skin *** findings of this study have potential applications in enhancing cooling systems and optimizing thermal management *** is observed that the squeezing motion generates additional pressure gradients within the fluid,which enhances the flow rate but reduces the frictional ***,the fluid is pushed more vigorously between the plates,increasing the flow *** the fluid experiences higher flow rates due to the increased squeezing effect,it spends less time in the region between the *** thermal relaxation,however,abruptly changes the temperature,leading to a decrease in the temperature fluctuations.

关键词： convective boundary condition Darcy-Forchheimer medium hybrid nanofuid Joule heating magnetohydrodynamic(MHD) numerical solution squeezing flow regression analysis

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SightAid: empowering the visually impaired in the Kingdom of Saudi Arabia (KSA) with deep learning-based intelligent wearable vision system

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Neural Computing and Applications 2024年第19期36卷 11075-11095页

作者： Talaat, Fatma M. Farsi, Mohammed Badawy, Mahmoud Elhosseini, Mostafa Machine Learning Department Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt Faculty of Computer Science & amp Engineering New Mansoura University Gamasa35712 Egypt College of Computer Science and Engineering Taibah University Yanbu46421 Saudi Arabia Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt Computer Science and Information Department Applied College Taibah University Madinah46537 Saudi Arabia

In the Kingdom of Saudi Arabia, visual impairment poses significant challenges for approximately 17.5% of school-aged children, mainly due to refractive errors. These challenges extend to everyday navigation, environmental interaction, and overall life quality. Motivated by the desire to empower visually impaired individuals, who face navigational limitations, difficulties in object recognition, and inadequate assistance from traditional technologies, we propose SightAid. This innovative wearable vision system utilizes a deep learning-based framework, addressing the gaps left by current assistive solutions. Traditional methods, such as canes and GPS devices, often fail to meet the nuanced and dynamic needs of the visually impaired, especially in accurately identifying objects, understanding complex environments, and providing essential real-time feedback for independent navigation. SightAid comprises a seven-phase framework involving data collection, preprocessing, and training of a sophisticated deep neural network with multiple convolutional and fully connected layers. This system is integrated into smart glasses with augmented reality displays, enabling real-time object detection and recognition. Interaction with users is facilitated through audio or haptic feedback, informing them about the location and type of objects detected. A continuous learning mechanism, incorporating user feedback and new data, ensures the system's ongoing refinement and adaptability. For performance assessment, we utilized the MNIST dataset, and an Indoor Objects Detection dataset tailored for the visually impaired, featuring images of everyday objects crucial for safe indoor navigation. SightAid demonstrates remarkable performance with accuracy up to 0.9874, recall values between 0.98 and 0.99, F1-scores ranging from 0.98 to 0.99, and AUC-ROC values reaching as high as 0.9999. These metrics significantly surpass those of traditional methods, highlighting SightAid's potential to substan

关键词： Object recognition

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Enhancing intra-aural disease classification with attention-based deep learning models

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Neural Computing and Applications 2025年第9期37卷 6601-6616页

作者： Demircan, Furkancan Ekinci, Murat Cömert, Zafer Software Engineering Faculty of Engineering and Natural Sciences Samsun University 19 Mayıs Samsun Turkey Computer Engineering Faculty of Engineering Karadeniz Technical University Trabzon Ortahisar Turkey Department of Technical Sciences of the Western Caspian University Baku Azerbaijan

Ear diseases are defined as pathological conditions that indicate dysfunction or abnormal function of the ear organ, which is part of the auditory system of living organisms that regulates hearing and balance functions. These diseases usually manifest as conditions that affect the internal components of the ear structure and can manifest themselves with symptoms such as hearing loss, ear pain, balance problems, and fluid accumulation in the ear. The accuracy of the diagnosis depends on expert knowledge and subjective opinion. This method is prone to human error. This study presents a novel computer-aided diagnosis system for otoscope images of ear diseases, utilizing a vision transformer-based feature extractor combined with machine learning classifiers to provide accurate second opinions for ENT specialists. For this purpose, a new model based on state-of-the-art vision transformer feature extractor and machine learning models is proposed. In the experimental study, the dataset, comprising 880 eardrum images categorized into four classes (CSOM, earwax, myringosclerosis, and normal), was split into training (70%), validation (10%), and testing (20%) subsets. Each image was preprocessed to 420 × 380 pixels to fit the input dimensions of the models. The vision transformer architecture was utilized for feature extraction, followed by classification using various machine learning algorithms including kNN, SVM, and random forest. As a result, the model using vision transformer feature extractor and k-nearest neighbors (kNN) algorithm achieved 99.00% accuracy. In this study, a deep learning-based and computer-aided diagnosis system, in other words, a computational model, was developed instead of the current human error-prone disease diagnosis method used by ear nose throat (ENT) specialists. The main purpose of the deep learning-based decision support system is to support the diagnosis process where expert knowledge is difficult to access and to provide an alternative opi

关键词： Deep learning

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Energy Efficient Green Routing for UAVs Ad-Hoc Network

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Intelligent Automation & Soft Computing 2023年第7期37卷 1111-1127页

作者： M.Muthukumar Rajasekar Rangasamy Irshad Hussain Salman A.AlQahtani Department of Computer Science and Engineering Sree Sowdambika College of EngineeringTamil NaduIndia Department of Computer Science and Engineering-AI&ML School of EngineeringMalla Reddy UniversityHyderabadIndia Faculty of Electrical and Computer Engineering University of Engineering and Technology Peshawar25000PeshawarPakistan Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Kingdom of Saudi Arabia

The purpose of this article is to propose Stability-based Energy-Efficient Link-State Hybrid Routing(S-ELHR),a low latency routing proto-col that aims to provide a stable mechanism for routing in unmanned aerial vehicles(UAV).The S-ELHR protocol selects a number of network nodes to create a Connected Dominating Set(CDS)using a parameter known as the Stability Metric(SM).The SM considers the node’s energy usage,connectivity time,and node’s *** the highest SM nodes are chosen to form *** node declares a Willingness to indicate that it is prepared to serve as a relay for its neighbors,by employing its own energy state.S-ELHR is a hybrid protocol that stores only partial topological information and routing tables on CDS *** of relying on the routing information at each intermediary node,it uses source routing,in which a route is generated on-demand,and data packets contain the addresses of the nodes the packet will transit.A route recovery technique is additionally utilized,which first locates a new route to the destination before forwarding packets along *** simulation for various network sizes and mobility speeds,the efficiency of S-ELHR is *** findings demonstrate that S-ELHR performs better than Optimized Link State Routing(OLSR)and Energy Enhanced OLSR(EE-OLSR)in terms of packet delivery ratio,end-to-end delay,and energy consumption.

关键词： Connected dominatingset hybridrouting UAVAdhocnetworks link state stability

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Comprehensive analysis of multiple machine learning techniques for rock slope failure prediction

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Journal of Rock Mechanics and Geotechnical engineering 2024年第11期16卷 4386-4398页

作者： Arsalan Mahmoodzadeh Abed Alanazi Adil Hussein Mohammed Hawkar Hashim Ibrahim Abdullah Alqahtani Shtwai Alsubai Ahmed Babeker Elhag Department of Civil Engineering University of HalabjaHalabjaKurdistan Region46018Iraq Department of Computer Science College of Computer Engineering and Sciences in Al-KharjPrince Sattam bin Abdulaziz UniversityP.O.Box 151Al-Kharj11942Saudi Arabia Department of Communication and Computer Engineering Faculty of EngineeringCihan University-ErbilKurdistan RegionIraq Department of Civil Engineering College of EngineeringSalahaddin University-Erbil44002ErbilKurdistan RegionIraq Software Engineering Department College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityP.O.Box 151Al-Kharj11942Saudi Arabia Department of Civil Engineering College of EngineeringKing Khalid UniversityAbha61413Saudi Arabia

In this study,twelve machine learning(ML)techniques are used to accurately estimate the safety factor of rock slopes(SFRS).The dataset used for developing these models consists of 344 rock slopes from various open-pit mines around Iran,evenly distributed between the training(80%)and testing(20%)*** models are evaluated for accuracy using Janbu's limit equilibrium method(LEM)and commercial tool GeoStudio *** assessment metrics show that the random forest model is the most accurate in estimating the SFRS(MSE=0.0182,R2=0.8319)and shows high agreement with the results from the LEM *** results from the long-short-term memory(LSTM)model are the least accurate(MSE=0.037,R2=0.6618)of all the models ***,only the null space support vector regression(NuSVR)model performs accurately compared to the practice mode by altering the value of one parameter while maintaining the other parameters *** is suggested that this model would be the best one to use to calculate the SFRS.A graphical user interface for the proposed models is developed to further assist in the calculation of the SFRS for engineering *** this study,we attempt to bridge the gap between modern slope stability evaluation techniques and more conventional analysis methods.

关键词： Rock slope stability Open-pit mines Machine learning(ML) Limit equilibrium method(LEM)

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A Review on Role of IoT and Machine Learning in Agriculture 7

A Review on Role of IoT and Machine Learning in Agriculture

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Kumar, Gulshan Choudhary, Vishal Kumar Kumar, Shravan Kumar, Rajan Kumar, Mukesh Gourshettiwar, Palash M. Faculty of Engineering And Technology Department of Computer Science And Design Maharashtra Wardha India Faculty of Engineering And Technology Department of Computer Science And Medical Engineering Maharashtra Wardha India

ISBN: (纸本)9798350359299

The agricultural area has undergone a significant transformation owing to the progress made in IoT. It is imperative to have a dependable remote monitoring solution right now. This study aims to accomplish two goals. First, a solution based on an app is offered, which effectively assists in remotely administering the field by displaying the current sensor values. In an Internet of Things based prototype surveillance system, a multi-class categorization method utilizing machine learning and deep learning is incorporated to classify labels such as clear farm, cow, horse, wild boar, and wild elephant. The analysis involved Support Vector Machines and Convolutional Neural Networks, with the accuracy measure being employed to determine the optimal model. The advancements in the Internet of Things (IoT) have significantly transformed the daily lives of individuals. A comprehensive understanding of soil properties gives a variety of soil-related information that can be used to improve crop yields. Machine learning is a cutting-edge technology that aids with agricultural precision and provides answers to crop yield issues. Significant informative advances and Machine Learning (ML) open up new opportunities for deciphering, quantifying, and comprehending data-intensive prediction in agricultural settings. This study thoroughly examines a number of machine learning topics for Internet of Things-based smart agriculture systems. © 2024 IEEE.

关键词： Support vector machines

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