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Covid-19 CT Lung Image Segmentation Using Adaptive Donkey and Smuggler Optimization Algorithm

computers, Materials & Continua 2022年第4期71卷 1133-1152页

作者： P.Prabu K.Venkatachalam Ala Saleh Alluhaidan Radwa Marzouk Myriam Hadjouni Sahar A.El_Rahman Department of Computer Science CHRIST(Deemed to be University)BengaluruIndia Department of Computer Science and Engineering CHRIST(Deemed to be University)BengaluruIndia Information Systems Department College of Computer and Information SciencePrincess Nourah Bint Abdulrahman UniversityRiyadhKSA 84428Saudi Arabia Department of Mathematics Faculty of ScienceCairo UniversityGiza12613Egypt Computer Sciences Department College of Computer and Information SciencePrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia Department of Electrical Engineering Faculty of Engineering-ShoubraBenha UniversityCairoEgypt

COVID’19 has caused the entire universe to be in existential healthcrisis by spreading globally in the year 2020. The lungs infection is detected inComputed Tomography (CT) images which provide the best way to increasethe existing healthcare schemes in preventing the deadly virus. Nevertheless,separating the infected areas in CT images faces various issues such as lowintensity difference among normal and infectious tissue and high changes inthe characteristics of the infection. To resolve these issues, a new inf-Net (LungInfection Segmentation Deep Network) is designed for detecting the affectedareas from the CT images automatically. For the worst segmentation results,the Edge-Attention Representation (EAR) is optimized using AdaptiveDonkey and Smuggler Optimization (ADSO). The edges which are identifiedby the ADSO approach is utilized for calculating dissimilarities. An IFCM(Intuitionistic Fuzzy C-Means) clustering approach is applied for computingthe similarity of the EA component among the generated edge maps andGround-Truth (GT) edge maps. Also, a Semi-Supervised Segmentation(SSS) structure is designed using the Randomly Selected Propagation (RP)technique and Inf-Net, which needs only less number of images and unlabelleddata. Semi-Supervised Multi-Class Segmentation (SSMCS) is designed usinga Bi-LSTM (Bi-Directional Long-Short-Term-memory), acquires all theadvantages of the disease segmentation done using Semi Inf-Net and enhancesthe execution of multi-class disease labelling. The newly designed SSMCSapproach is compared with existing U-Net++, MCS, and *** such as MAE (Mean Absolute Error), Structure measure, Specificity(Spec), Dice Similarity coefficient, Sensitivity (Sen), and Enhance-AlignmentMeasure are considered for evaluation purpose.

关键词： Adaptive donkey and snuggler optimization.bi-directional long short term memory coronavirus disease 2019 randomly selected propagation semi-supervised learning

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Feature Selection in Socio-Economic Analysis: A Multi-Method Approach for Accurate Predictive Outcomes

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International Journal of Crowd science 2025年第1期9卷 64-78页

作者： Al-Qerem, Ahmad Ali, Ali Mohd Jebreen, Issam Nabot, Ahmad Rajab, Mohammed Alauthman, Mohammad Aldweesh, Amjad Aburub, Faisal Alangari, Someah Alzgol, Musab Computer Science Department Faculty of Information Technology Zarqa University Zarqa13110 Jordan Communications and Computer Engineering Department Faculty of Engineering Al-Ahliyya Amman University Amman19328 Jordan University Headquarter University of Anbar Ramadi31001 Iraq Department of Information Security University of Petra Amman11196 Jordan College of Computing and Information Technology Shaqra University Shaqra11911 Saudi Arabia Department of Business Intelligence and Data Analytics University of Petra Amman11196 Jordan Computer Information Systems Department Faculty of Information Technology Isra University Amman11622 Jordan

Feature selection is a cornerstone in advancing the accuracy and efficiency of predictive models, particularly in nuanced domains like socio-economic analysis. This study explores nine distinct feature selection methods, utilizing a heart disease dataset as a representative model for complex socio-economic systems. Our findings identified four universally recognized features as critical across all selection methods. However, the divergence in significance attributed to other features by different methods underscores the inherent variability in selection techniques. When the top four features were incorporated into twelve classification models, a noticeable surge in predictive accuracy was observed, emphasizing their foundational role in enhancing model outcomes. The variations among methods stress the need for a methodical and discerning approach to feature selection, especially in data-rich socio-economic landscapes. As we venture further into an era defined by data-driven decision-making, rigour and precision in feature selection become indispensable. Future research should extend this approach to broader datasets, ensuring the robustness and adaptability of our findings. © The author(s) 2025.

关键词： Prediction models

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OntoMG: a unique and ontological-based intelligent framework for early identification of myasthenia gravis (MG)

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International Journal of Information Technology (Singapore) 2024年第6期16卷 3847-3853页

作者： Mahajan, Prerna Agarwal, Trapty Vekariya, Daxa Gupta, Rajesh Malviya, Ashwini Anandaraj, S.P. Jain, Garima Anand, D. Department of Computer Science and Information Technology Jain (Deemed to Be University) Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Lucknow India Department of Computer Science and Engineering Faculty of Engineering and Technology Parul University Gujarat Vadodara India Department of Management Sanskriti University UP Mathura India Department of UGDX ATLAS SkillTech University Mumbai India Department of Computer Science Engineering Presidency University Bengaluru India Department of Computer Science and Business Systems (CBS) Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India

Myasthenia Gravis (MG) is a neuromuscular disease causing extreme muscular fatigue, triggering problems with vision, swallowing, speech, mobility, dexterity, and breathing. However, early detection and prediction of MG is a crucial and quite challenging task for medical practitioners. So, in our proposed work, we are focused on introducing an ontology-based intelligent system for the prognosis of patients affected by this neuromuscular disease. The given paper presents a novel framework that employs the concepts of ontology, semantic web rules, and reasoner for deriving whether the patient is tested positive or negative. The suggested framework's effectiveness is assessed by comparing it to machine learning classifiers using parameters from the NIH repository for Myasthenia Gravis. The experimental results show that the ontological-based framework is able to achieve higher accuracy (80.85%), higher precision (78.37%), and higher recall (76.55%) than the existing classifiers used in the recent studies. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Machine learning (ML) Myasthenia gravis disease Ontology Reasoners Semantic web rules language (SWRL)

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Efficient UAV Routing Strategies for Wireless Sensor Network Data Retrieval

Efficient UAV Routing Strategies for Wireless Sensor Network...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Al-Sharhanee, Kareem Ali Malalah Najim, Ali Hamza Alheeti, Khattab M. Ali Satar, Nurhizam Safie Mohd Hashim, Aisha Hassan Abdallah Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq College of Engineering Techniques Department of Communication Technical Engineering Al-Farahidi University Baghdad10011 Iraq Department of Computer Technical Al-Diwaniyah Iraq College of Computer and Information Technology University of Anbar Computer Networking Systems Department Anbar Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia International Islamic University Malaysia Faculty of Engineering Department of Electrical and Computer Engineering Selangor Malaysia

ISBN: (纸本)9798350369106

WSNs are applied in many disciplines where certain conditions require the ability to adapt to network sink mobility and changes in the dynamics of the area coverage. To meet these needs, it is necessary to develop integrated systems where the ''terrestrial' systems and the Unmanned Aerial Vehicles (UAVs)' collect data from sensors efficiently. Planning of UAV trajectories is another challenging issue if many WSN nodes are randomly deployed. This challenge has a big impact on the overall effectiveness of the system. This paper presents a novel approach for reducing the traveled distance of unscrewed aerial vehicles (UAVs) to improve the data collection efficiency in WSNs with poor node density. Yet the method that has been developed is aimed at increasing 'the efficiency and efficacy of data collection... with the assistance of Unmanned Aerial Vehicles (UAVs)' which means this work reveals practical application solutions. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Case Studies of Development of Verified Programs with Dafny for Accessibility Assessment 1

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10th IPM International Conference on Fundamentals of Software engineering, FSEN 2023

作者： Faria, João Pascoal Abreu, Rui Faculty of Engineering of the University of Porto Porto Portugal INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal INESC ID Lisbon Portugal

ISBN: (数字)9783031424410

ISBN: (纸本)9783031424403

Formal verification techniques aim at formally proving the correctness of a computer program with respect to a formal specification, but the expertise and effort required for applying formal specification and verification techniques and scalability issues have limited their practical application. In recent years, the tremendous progress with SAT and SMT solvers enabled the construction of a new generation of tools that promise to make formal verification more accessible for software engineers, by automating most if not all of the verification process. The Dafny system is a prominent example of that trend. However, little evidence exists yet about its accessibility. To help fill this gap, we conducted a set of 10 case studies of developing verified implementations in Dafny of some real-world algorithms and data structures, to determine its accessibility for software engineers. We found that, on average, the amount of code written for specification and verification purposes is of the same order of magnitude as the traditional code written for implementation and testing purposes (ratio of 1.14) – an "overhead" that certainly pays off for high-integrity software. The performance of the Dafny verifier was impressive, with 2.4 proof obligations generated per line of code written, and 24 ms spent per proof obligation generated and verified, on average. However, we also found that the manual work needed in writing auxiliary verification code may be significant and difficult to predict and master. Hence, further automation and systematization of verification tasks are possible directions for future advances in the field. © 2023, IFIP International Federation for Information Processing.

关键词： Formal verification

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All-optically linearized silicon modulator with ultrahigh SFDR of 131 dB·Hz^(6/7)

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Photonics Research 2025年第2期13卷 433-441页

作者： QIANG ZHANG QIKAI HUANG PENGHUI XIA YAN LI XINGYI JIANG SHUYUE ZHANG SHENGYU FANG JIANYI YANG HUI YU Zhejiang Lab Hangzhou 311100China Institute of Integrated Microelectronic Systems College of Information Science and Electronic EngineeringZhejiang UniversityHangzhou 310027China Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo 315211China

Integrated high-linearity modulators are crucial for high dynamic-range microwave photonic(MWP)*** linearization schemes usually involve the fine tuning of radio-frequency(RF)power distribution,which is rather inconvenient for practical applications and can hardly be implemented on the integrated photonics *** this paper,we propose an elegant scheme to linearize a silicon-based modulator in which the active tuning of RF power is *** device consists of two carrier-depletion-based Mach-Zehnder modulators(MZMs),which are connected in series by a 1×2 thermal optical switch(OS).The OS is used to adjust the ratio between the modulation depths of the two *** a proper ratio,the complementary third-order intermodulation distortion(IMD3)of the two sub-MZMs can effectively cancel each other *** measured spurious-free dynamic ranges for IMD3 are 131,127,118,110,and 109 d B·Hz^(6∕7)at frequencies of 1,10,20,30,and 40 GHz,respectively,which represent the highest linearities ever reached by the integrated modulator chips on all available material platforms.

关键词： microwave photonic systems ultrahigh SFDR all optical linearization silicon modulator active tuning rf power high linearity modulators third order intermodulation distortion integrated photonics

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Fuzzy Difference Equations in Diagnoses of Glaucoma from Retinal Images Using Deep Learning

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computer Modeling in engineering & sciences 2024年第4期139卷 801-816页

作者： D.Dorathy Prema Kavitha L.Francis Raj Sandeep Kautish Abdulaziz S.Almazyad Karam M.Sallam Ali Wagdy Mohamed Department of Mathematics Voorhees CollegeVelloreTamil NaduIndia Chandigarh Group of Colleges JhanjeriMohaliPunjabIndia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Faculty of Science and Technology School of IT and SystemsUniversity of CanberraCanberraAustralia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11937Jordan

The intuitive fuzzy set has found important application in decision-making and machine *** enrich and utilize the intuitive fuzzy set,this study designed and developed a deep neural network-based glaucoma eye detection using fuzzy difference equations in the domain where the retinal images *** image detections are categorized as normal eye recognition,suspected glaucomatous eye recognition,and glaucomatous eye *** degrees associated with weighted values are calculated to determine the level of concentration between the fuzzy partition and the retinal *** proposed model was used to diagnose glaucoma using retinal images and involved utilizing the Convolutional Neural Network(CNN)and deep learning to identify the fuzzy weighted regularization between *** methodology was used to clarify the input images and make them adequate for the process of glaucoma *** objective of this study was to propose a novel approach to the early diagnosis of glaucoma using the Fuzzy Expert System(FES)and Fuzzy differential equation(FDE).The intensities of the different regions in the images and their respective peak levels were *** the peak regions were identified,the recurrence relationships among those peaks were then *** partitioning was done due to varying degrees of similar and dissimilar concentrations in the *** and dissimilar concentration levels and spatial frequency generated a threshold image from the combined fuzzy matrix and *** distinguished between a normal and abnormal eye condition,thus detecting patients with glaucomatous eyes.

关键词： Convolutional Neural Network(CNN) glaucomatous eyes fuzzy difference equation intuitive fuzzy sets image segmentation retinal images

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Constructing Varied and Attractive Shortlists from Databases: A Group Decision Approach 7th

Constructing Varied and Attractive Shortlists from Databas...

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7th Seminar on Quantitative Methods of Group Decision Making, 2021

作者： Ramsey, David M. Mariański, Aleksander Department of Computer Science and Systems Engineering Faculty of Computer Science and Telecommunications Wroclaw University of Science and Technology Wroclaw Poland Department of Applied Computer Science Faculty of Computer Science and Telecommunications Wroclaw University of Science and Technology Wroclaw Poland

ISBN: (纸本)9783662665961

Thanks to the Internet, basic information can be found about a very large number of offers at very little cost. Many sites give basic information about real estate offers, including price, size, location and number of rooms. Information of this kind is sufficient to assess whether an offer is potentially attractive or not, but does not suffice in making an ultimate decision. The number of real estate offers in a city may be very large. In such a case, it can be beneficial to use an automatic procedure to first eliminate offers that do not satisfy the basic criteria of an individual or family and then construct a shortlist of varied and potentially attractive offers. This article recalls an algorithm that derives such a shortlist for an individual searcher. The novelty of this article lies in adapting this algorithm to scenarios in which a group decision is made. A practical example based on real estate offers in Warsaw is used to illustrate the algorithm. © 2022, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Software engineering

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Early Detection of Childhood Malnutrition Using Survey Data and Machine Learning Approaches 4

Early Detection of Childhood Malnutrition Using Survey Data ...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Zumma, Thoufiq Rahaman, Anikur Islam Prova, Nuzhat Noor Haque, Tasriful Joy Bose, Julias Caeser Youki, Ravina Akter Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh Washington University Science and Technology Department of Computer Science New York United States Pace University Department of Seidenberg School of Csis New York United States Victorian Institute of Technology Master of Information Technology and Systems Australia University of Wollongong Faculty of Artificial Intelligence and Science Australia University of Technology Sydney Faculty of Engineering and It Australia

ISBN: (纸本)9798331523923

Children's physical, mental and emotional development depends heavily on sleep, with age-specific sleep needs fluctuating. Malnutrition may result from eating too little, absorbing nutrients poorly, being unwell, living in poverty or not knowing enough about nutrition. Food allergies and iron insufficiency are two examples of illnesses whose onsets might differ depending on environmental exposures and genetics. By providing proper diet, getting medical attention when necessary and encouraging healthy behaviors, parents play a crucial role in supporting their children's health. For children to grow and develop properly, they need meals high in fruits, vegetables, milk, protein, calcium, iron and fiber. Key advantages of a balanced diet include aiding bone formation, improving heart health and encouraging regular bowel motions and digestion. In general, children's general health and wellness depend on caregivers being aware of their dietary requirements and giving them the proper attention. The findings of the research imply that machine learning algorithms may be useful in understanding and predicting the prevalence of malnutrition in children. Furthermore, these algorithms could provide perceptive data that might help politicians create effective policies. In this research, we make use of five different machine learning algorithms. With a decision tree, the accuracy is 0.92%, followed by svm which provides 0.91%, KNN, which provides 0.84%, GBM classifier, which provides 0.79% and random forest, which provides 0.65%. © 2025 IEEE.

关键词： Adversarial machine learning

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Enhancing face recognition performance: a comprehensive evaluation of deep learning models and a novel ensemble approach with hyperparameter tuning

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Soft Computing 2024年第20期28卷 12399-12424页

作者： Selvaganesan, Jana Sudharani, B. Shekhar, S. N. Chandra Vaishnavi, K. Priyadarsini, K. Raju, K. Srujan Rao, T. Srinivasa Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R & ampD Institute of Science and Technology Avadi Tamil Nadu Chennai600062 India Guntur Dist. Andhra Pradesh Vadlamudi India Department of Electronics and Communication Engineering Sreenidhi Institute of Science and Technology Telangana Hyderabad501301 India Department of Computer Science and Engineering Sona College of Technology Tamil Nadu Salem636005 India Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of Computer Science and Engineering CMR Technical Campus Medchal Telangana Hyderabad501401 India Department of Electronics and Communication Engineering Aditya Engineering College Surampalem533437 India

In response to growing security concerns and the increasing demand for face recognition (FR) technology in various sectors, this research explores the application of deep learning techniques, specifically pre-trained Convolutional Neural Network (CNN) models, in the field of FR. The study harnesses the power of five pre-trained CNN models—DenseNet201, ResNet152V2, MobileNetV2, SeResNeXt, and Xception—for robust feature extraction, followed by SoftMax classification. A novel weighted average ensemble model, meticulously optimized through a grid search technique, is introduced to augment feature extraction and classification efficacy. Emphasizing the significance of robust data pre-processing, encompassing resizing, data augmentation, splitting, and normalization, the research endeavors to fortify the reliability of FR systems. Methodologically, the study systematically investigates hyperparameters across deep learning models, fine-tuning network depth, learning rate, activation functions, and optimization methods. Comprehensive evaluations unfold across diverse datasets to discern the effectiveness of the proposed models. Key contributions of this work encompass the utilization of pre-trained CNN models for feature extraction, extensive evaluation across multiple datasets, the introduction of a weighted average ensemble model, emphasis on robust data pre-processing, systematic hyperparameter tuning, and the utilization of comprehensive evaluation metrics. The results, meticulously analyzed, unveil the superior performance of the proposed method, consistently outshining alternative models across pivotal metrics, including Recall, Precision, F1 Score, Matthews Correlation Coefficient (MCC), and Accuracy. Notably, the proposed method attains an exceptional accuracy of 99.48% on the labeled faces in the wild (LFW) dataset, surpassing erstwhile state-of-the-art benchmarks. This research represents a significant stride in FR technology, furnishing a dependable and accurate

关键词： Convolutional neural networks

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