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Multi-gradient-direction based deep learning model for arecanut disease identification

CAAI Transactions on Intelligence technology 2022年第2期7卷 156-166页

作者： S.B.Mallikarjuna Palaiahnakote Shivakumara Vijeta Khare M.Basavanna Umapada Pal B.Poornima Department of Computer Science and Engineering Bapuji Institute of Engineering and TechnologyDavanagereAffiliated to Visvesvaraya Theological UniversityBelagaviKarnatakaIndia Faculty of Computer Science and Information Technology University of MalayaKuala LumpurMalaysia Adani Institute of Infrastructure Engineering AhmedabadIndia Department of Computer Science Davanagere UniversityDavanagereKarnatakaIndia Computer Vision and Pattern Recognition Unit Indian Statistical InstituteKolkataWest BengalIndia

Arecanut disease identification is a challenging problem in the field of image *** this work,we present a new combination of multi-gradient-direction and deep con-volutional neural networks for arecanut disease identification,namely,rot,split and *** to the effect of the disease,there are chances of losing vital details in the *** enhance the fine details in the images affected by diseases,we explore multi-Sobel directional masks for convolving with the input image,which results in enhanced *** proposed method extracts arecanut as foreground from the enhanced images using Otsu ***,the features are extracted for foreground information for disease identification by exploring the ResNet *** advantage of the proposed approach is that it identifies the diseased images from the healthy arecanut *** results on the dataset of four classes(healthy,rot,split and rot-split)show that the proposed model is superior in terms of classification rate.

关键词： deep learning image analysis pattern recognition

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A New HourGlass Network for Detecting Text in Shaky and Non-shaky Video Frames 27th

A New HourGlass Network for Detecting Text in Shaky and Non-...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Halder, Arnab Palaiahnakote, Shivakumara Pal, Umapada Blumenstein, Michael Gornale, Shivanand S. Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata Kolkata India School of Science Engineering and Environment University of Salford Manchester United Kingdom University of Technology Sydney Sydney Australia Department of Computer Science Rani Channamma University Belagavi India

ISBN: (纸本)9783031784972

Shaky and non-shaky videos are quite common in real-time applications such as surveillance and monitoring vehicles and human movements in protected areas. As a result, text detection in such videos is a formidable challenge due to motion blur, noise, shaky cameras, poor quality and poor visibility. In contrast to existing text detection methods, which focus on text detection in scene images or specific types of images, the present work focuses on text detection in both shaky and non-shaky video frames. Inspired by the impressive performance of the HourGlass network for adverse situations, we explore the HourGlass network for successful text detection in shaky, non-shaky video frames and natural scene images. To improve the performance of the HourGlass network, we employ the Real-Time Model (RTMHead) for predicting text precisely and the Cross Stage Partial Network (CSPNet), which is a neck architecture for robust feature fusion. In addition, the integration of the SiLu activation function with the HourGlass network improves the discriminative power ability. To test the efficacy of the proposed method, we conducted experiments on shaky and non-shaky video frames, as well as ICDAR 2015 video frames. Furthermore, to show the effectiveness of the proposed method, we used Total-Text scene images for experimentation. The results on different datasets and a comparative study with the state-of-the-art models show that the proposed model outperforms the existing methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image enhancement

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Automated Autism Spectral Disorder Classification Using Optimal Machine Learning Model

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computers, Materials & Continua 2023年第3期74卷 5251-5265页

作者： Hanan Abdullah Mengash Hamed Alqahtani Mohammed Maray Mohamed K.Nour Radwa Marzouk Mohammed Abdullah Al-Hagery Heba Mohsen Mesfer Al Duhayyim Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computer ScienceCenter of Artificial Intelligence and Unit of CybersecurityKing Khalid UniversityAbhaSaudi Arabia Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of ComputerQassim UniversitySaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Autism Spectrum Disorder (ASD) refers to a neuro-disorder wherean individual has long-lasting effects on communication and interaction *** information technologywhich employs artificial intelligence(AI) model has assisted in early identify ASD by using pattern *** advances of AI models assist in the automated identification andclassification of ASD, which helps to reduce the severity of the *** study introduces an automated ASD classification using owl searchalgorithm with machine learning (ASDC-OSAML) model. The proposedASDC-OSAML model majorly focuses on the identification and classificationof ASD. To attain this, the presentedASDC-OSAML model follows minmaxnormalization approach as a pre-processing stage. Next, the owl searchalgorithm (OSA)-based feature selection (OSA-FS) model is used to derivefeature subsets. Then, beetle swarm antenna search (BSAS) algorithm withIterative Dichotomiser 3 (ID3) classification method was implied for ASDdetection and classification. The design of BSAS algorithm helps to determinethe parameter values of the ID3 classifier. The performance analysis of theASDC-OSAML model is performed using benchmark dataset. An extensivecomparison study highlighted the supremacy of the ASDC-OSAML modelover recent state of art approaches.

关键词： Autism spectral disorder machine learning owl search algorithm feature selection id3 classifier

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CNN-Based Deep Learning Model for Early Identification and Categorization of Melanoma Skin Cancer Using Medical Imaging

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SN computer science 2024年第7期5卷 911页

作者： Pandimurugan, V. Ahmad, Sultan Prabu, A.V. Rahmani, Mohammad Khalid Imam Abdeljaber, Hikmat A. M. Eswaran, Malathi Nazeer, Jabeen Computer Science and Engineering Acharya Institute of Technology Bangalore India Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia University Center for Research and Development (UCRD) Department of Computer Science and Engineering Chandigarh University Gharuan Punjab Mohali 140413 India Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP Guntur 522502 India College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia Department of Computer Science Faculty of Information Technology Applied Science Private University Amman Jordan MEU Research Unit Middle East University Amman 11831 Jordan Department of Computer Technology Kongu Engineering College Perundurai Tamil Nadu Erode India

In human life, skin cancer is a curse. If not appropriately diagnosed, it spreads around all body parts in the earlier stage. The melanoma skin cancer death rate is 75% all over the world. There is an urgent need for a cure to be in place. Swarm and evolutionary computation for healthcare deals with real-world healthcare applications. One of the best ways to do this is to find the genes that need to be turned off such that only cancer cells can die. The current therapies used to cure cancer focus on turning off cancer cells but, at times, affect healthy tissues. Using the power of Artificial Intelligence, one can easily mark out the cancer-affected area and provide treatment accordingly. Segmentation techniques can be used to mark cancer lesions, and advanced deep learning algorithms in computer vision can help classify which class of cancer lesions. Automatic detection of melanoma by using a dermoscopic sample is tricky to find the stage and percentage of lesions affected by using deep convolutional neural networks with the help of machine vision tools. To predict skin lesions, we developed a model with three layers, each having output channels of 16, 32, and 64, respectively. For this research, different samples were collected from the international skin imaging collaboration database (ISIC2019, ISIC2020, ISIC2021). The study computed the most important parameters for classifying and identifying the lesion with accuracy, precision, recall, and specificity values. The proposed DCNN model classifier attained an accuracy of 88.82%, 93.45%, and 95.15% with the respective datasets of ISIC2019, ISIC2020, and ISIC2021, indicating high performance compared with the other state-of-the-art networks. The proposed model is a better framework for automating early detection and classification of melanoma skin cancer to protect many lives and avoid mishaps. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification Convolution neural network Deep learning Medical imaging Melanoma Skin cancer Swarm and evolutionary computation

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Prairie Dog Optimization-based Tilt-Integral-Derivative Controller for Frequency Regulation of Power System 8

Prairie Dog Optimization-based Tilt-Integral-Derivative Cont...

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8th International Artificial Intelligence and Data Processing Symposium, IDAP 2024

作者： Ekinci, Serdar Izci, Davut Abualigah, Laith Ghandour, Raymond Salman, Mohammad Batman University Department of Computer Engineering Batman Turkey Meu Research Unit Middle East University Computer Science Department Amman Jordan American University of the Middle East College of Engineering and Technology Kuwait

ISBN: (纸本)9798331531492

The load frequency control (LFC) system is the primary subject of this investigation because of the critical role it plays in regulating both the generation and demand of electrical power. It is becoming increasingly important to ensure that the LFC system is implemented with precision as the level of uncertainty surrounding renewable energy sources and altering demand patterns continues to increase. An investigation into the dynamics of low-frequency converters (LFC) in a hybrid power system that functions in two domains and is comprised of a photovoltaic (PV) system and a reheat thermal system is the focus of this scientific study. The implementation of a tilt-integral-derivative (TID) controller is done in order to improve the performance of the system. In addition, the paper provides a description of the process by which the controller settings were adjusted by employing a one-of-a-kind optimization technique known as the Prairie dog optimizer (PDO). There has been no investigation into the use of this approach in LFC designs. It is an integral of time-multiplied absolute error (ITAE) that serves as the objective function of the optimization approach. A comparison is made between the performance of the PDO-tuned TID controller and other established strategies that have been published in recent literature. A decrease in overshoot values and an improvement in damping durations for both system frequency and tie-line power fluctuations are two of the evidences that demonstrate that the suggested strategy is superior to the methodologies that are currently in use. The results reveal that the proposed strategy is superior. © 2024 IEEE.

关键词： Two term control systems

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Avian abundance and diversity in three urbanized university campuses at different elevations in Northern Mindanao, Philippines

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Biodiversity 2025年第1期26卷 62-73页

作者： Pedrera, Joven A. Ascaño, Cordulo P. Piloton, Queenilyn A. Relox, Richel E. Cagurin, Giovanni B. Mugot, Dennis A. Palma, Richmund A. Maglunsod, Jessica G. Gonzaga, Apolinario B. Taylaran, Renante D. Quirino, John Preach C. Acma, John Mikel M. Valdez, Ruthy Mae B. Getuaban, Geisha Mae V. Agbas, Jessa Marie A. Acosta, Pelmar M. Roa, Mitchie M. Cardoza, Marjho Mae S. Calvis, Liza H. Llevado, Jomar C. Landicho, Junar A. Moncada, John David O. Handag, Sansen A. Walag, Angelo Mark P. Department of Environmental Science and Technology University of Science and Technology of Southern Philippines–Cagayan de Oro Cagayan de Oro Philippines Department of Science Education University of Science and Technology of Southern Philippines–Cagayan de Oro Cagayan de Oro Philippines Mathematics and Science Service Unit University of Science and Technology of Southern Philippines–Claveria Claveria Philippines Department of Agroforestry University of Science and Technology of Southern Philippines–Claveria Claveria Philippines Department of Agronomy University of Science and Technology of Southern Philippines–Claveria Claveria Philippines Department of Technology and Livelihood Education University of Science and Technology of Southern Philippines–Claveria Claveria Philippines Department of Manufacturing Engineering Technology University of Science and Technology of Southern Philippines–Jasaan Jasaan Philippines Department of Technology Communication Management University of Science and Technology of Southern Philippines–Jasaan Jasaan Philippines Department of Information Technology University of Science and Technology of Southern Philippines–Villanueva Villanueva Philippines Department of Electro-Mechanical Technology University of Science and Technology of Southern Philippines–Villanueva Villanueva Philippines Arts and Sciences Unit University of Science and Technology of Southern Philippines–Oroquieta Oroquieta Philippines Department of Food Processing and Technology University of Science and Technology of Southern Philippines–Oroquieta Oroquieta Philippines Department of Marine Biology University of Science and Technology of Southern Philippines–Panaon Panaon Philippines Department of Information Technology University of Science and Technology of Southern Philippines–Cagayan de Oro Cagayan de Oro Philippines Department of Computer Science University of Science and Technology of Southern Philippines–Cagayan de Oro Cagayan de Oro Philippines

This study investigates the impact of urbanization and elevation on avian diversity across three university campuses in the Philippines. Urbanization, a significant driver of global environmental change, negatively affects biodiversity by altering natural habitats. Birds, frequently used as bioindicators, are vital subjects for studying urban impacts due to their visibility and sensitivity to environmental changes. This research compares the abundance of bird families across campuses at different elevations, revealing significant variations influenced by urban structure, vegetation, and resource availability. Line transect, point count, and mist-netting methods were employed to survey the birds in the study area following established methods. The family Pycnonotidae is most abundant in the low-elevation urban campus (University of science and technology of Southern Philippines - Cagayan de Oro), while families like Nectariniidae and Cuculidae are more prevalent in the high-elevation campus (USTPClaveria). These findings underscore the importance of habitat diversity and resource availability in supporting avian communities. The study highlights the potential of urbanized university campuses to serve as refuges for biodiversity if managed appropriately. Enhancing habitat complexity, maintaining green spaces, and ensuring diverse food resources are crucial for sustaining bird populations. The results emphasize the need for targeted conservation efforts within urban campuses to preserve and promote avian diversity in increasingly urbanized landscapes. © 2024 Biodiversity Conservancy International.

关键词： Avian diversity conservation strategies urban campuses urban spaces urbanization

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Endpoint Detection in Breast Images for Automatic Classification of Breast Cancer Aesthetic Results 1st

Endpoint Detection in Breast Images for Automatic Classifi...

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1st Deep Breast Workshop on AI and Imaging for Diagnostic and Treatment Challenges in Breast Care, Deep-Breath 2024

作者： Freitas, Nuno Veloso, Carlos Mavioso, Carlos Cardoso, Maria J. Oliveira, Hélder P. Cardoso, Jaime S. Faculdade de Engenharia Universidade do Porto Porto Portugal Institute for Systems and Computer Engineering Technology and Science Porto Portugal Breast Unit Champalimaud Foundation Lisbon Portugal

ISBN: (纸本)9783031777882

Breast cancer is the most common type of cancer in women worldwide. Because of high survival rates, there has been an increased interest in patient Quality of Life after treatment. Aesthetic results play an important role in this aspect, as these treatments can leave a mark on a patient’s self-image. Despite that, there are no standard ways of assessing aesthetic outcomes. Commonly used software such as *** or BAT require the manual annotation of keypoints, which makes them time-consuming for clinical use and can lead to result variability depending on the user. Recently, there have been attempts to leverage both traditional and Deep Learning algorithms to detect keypoints automatically. In this paper, we compare several methods for the detection of Breast Endpoints across two datasets. Furthermore, we present an extended evaluation of using these models as input for full contour prediction and aesthetic evaluation using the *** software. Overall, the YOLOv9 model, fine-tuned for this task, presents the best results considering both accuracy and usability, making this architecture the best choice for this application. The main contribution of this paper is the development of a pipeline for full breast contour prediction, which reduces clinician workload and user variability for automatic aesthetic assessment. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Lung cancer

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Maximizing Resource Efficiency in Cloud Data Centers through Knowledge-Based Flower Pollination Algorithm (KB-FPA)

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computers, Materials & Continua 2024年第6期79卷 3757-3782页

作者： Nidhika Chauhan Navneet Kaur Kamaljit Singh Saini Sahil Verma Kavita Ruba Abu Khurma Pedro A.Castillo University Institute of Computing Department Chandigarh UniversityPunjab140413India Department of Computer Science and Engineering Chandigarh UniversityPunjab140413India Universidade Federal do Piauí TeresinaPiauí64049-550Brazil MEU Research Unit Faculty of Information TechnologyMiddle East UniversityAmman11831Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan Department of Computer Engineering Automatics and RoboticsUniversity of GranadaGranada18071Spain

Cloud computing is a dynamic and rapidly evolving field,where the demand for resources fluctuates *** paper delves into the imperative need for adaptability in the allocation of resources to applications and services within cloud computing *** motivation stems from the pressing issue of accommodating fluctuating levels of user demand *** adhering to the proposed resource allocation method,we aim to achieve a substantial reduction in energy *** reduction hinges on the precise and efficient allocation of resources to the tasks that require those most,aligning with the broader goal of sustainable and eco-friendly cloud computing *** enhance the resource allocation process,we introduce a novel knowledge-based optimization *** this study,we rigorously evaluate its efficacy by comparing it to existing algorithms,including the Flower Pollination Algorithm(FPA),Spark Lion Whale Optimization(SLWO),and Firefly *** findings reveal that our proposed algorithm,Knowledge Based Flower Pollination Algorithm(KB-FPA),consistently outperforms these conventional methods in both resource allocation efficiency and energy consumption *** paper underscores the profound significance of resource allocation in the realm of cloud *** addressing the critical issue of adaptability and energy efficiency,it lays the groundwork for a more sustainable future in cloud computing *** contribution to the field lies in the introduction of a new resource allocation strategy,offering the potential for significantly improved efficiency and sustainability within cloud computing infrastructures.

关键词： Cloud computing resource allocation energy consumption optimization algorithm flower pollination algorithm

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Improve Harris Hawkes optimizer algorithm via Laplace crossover

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Journal of Ambient Intelligence and Humanized Computing 2024年第4期15卷 2057-2072页

作者： Nasab, Seyed Taha Mousavi Abualigah, Laith Department of Applied Mathematics Iran University of Science and Technology Tehran Iran Computer Science Department Al al-Bayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman11831 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

Nowadays, the speed of solving optimization problems by increasing various issues and the number of variables is critical. The Harris Hawk optimization method is a brand-new, intelligent system that resolves optimization issues by mathematically simulating the natural behavior of hawks. In this study, the Harris Hawks optimization method and the Laplace crossover operator are merged, and a new enhanced Harris Hawks algorithm (LX-HHO) based on the Laplace optimization algorithm is suggested. The purpose of improving this algorithm is to increase the convergence speed while maintaining good accuracy in achieving the optimal solution. The results of the proposed method on the test functions demonstrate that the algorithm has achieved faster convergence than the original version of the Harris Hawks algorithm and other meta-heuristic algorithms. This is due to the algorithm's accuracy in obtaining the best answer in these functions and to a reduction in the evaluation of the optimization function. Also, comparing and evaluating the proposed algorithm with other algorithms by Friedman and MAE test shows the superiority of the LX-HHO algorithm. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Optimization

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Enhancing Timely Graduations: An Explainable AI Approach to Predict Academic Risks in South African Students

Enhancing Timely Graduations: An Explainable AI Approach to ...

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2023 IEEE International Conference on Electrical, computer and Energy Technologies, ICECET 2023

作者： Bokgoshi, Lenny Sixhaxa, Khanyisile Jadhav, Ashwini Nyamane, Seipati Ajoodha, Ritesh School of Computer Science and Applied Mathematics University of Witwatersrand Johannesburg South Africa University of Witwatersrand Science Teaching and Learning Unit Faculty of Science Johannesburg South Africa Sol Plaatje University Department of Computer Science and Information Technology Kimberley South Africa

ISBN: (纸本)9798350327816

With rising dropout rates and extended degree completion times in South African institutions, there's a pressing need to better understand and address the hurdles faced by students during their academic journey. This research harnesses the power of explainable AI to predict and classify students based on their likelihood of not completing their degrees on time. Utilizing synthetic data generated via a Bayesian network, we used predictive models that categorize students into four distinct risk profiles. This clarity in prediction not only illuminates the underlying causes of academic delays but also empowers faculty, advisors, and student support services with actionable insights. The goal of this research is to facilitate timely interventions, tailored support, and seamless transitions for students transferring between universities, ensuring more students can realize their academic aspirations within expected time frames. © 2023 IEEE.

关键词： academic performance at-risk students classification models Machine learning student attrition

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