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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： G. Deepa Y. Kalpana Department of Computer Science VELS Institute of Science Technology and Advanced Studies (VISTAS) Chennai India Department of Computer Application and IT VELS Institute of Science Technology and Advanced Studies (VISTAS) Chennai India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Classification of bone marrow cancer is a crucial step in the analysis of several hematological cancers, such as multiple myeloma and leukemia. This study shows better dense neural network (DNN) architecture for significant the variation among the five main types of bone marrow cells includes lymphocytes, myelocytes, band neutrophils, and plasma cells. To find and diagnose bone marrow cancers like multiple myeloma, acute myeloid leukemia (AML), and acute lymphoblastic leukemia (ALL) early on, it is important to be able to accurately identify these cell types. The suggested DNN model uses unique callbacks to optimize training, avoid overfitting, and guarantee faster convergence. These callbacks include learning rate scheduling, early pausing, and dynamic weight modifications. The system preprocesses the high-resolution bone marrow image database to enhance contrast, adapt to fluctuations in illumination, and utilize dense layers for cell classification and convolutional layers for feature extraction. After extensive hyperparameter tuning, a model that classified these five categories of bone marrow cells obtained an overall accuracy rate of 95%. In terms of accuracy, recall, and F1 scores, the model performs better than conventional machine learning techniques while retaining computing economy. The model is able to increase training times without sacrificing classification performance by incorporating custom callbacks. With its enormous clinical application potential, our improved DNN architecture offers pathologists a dependable and effective tool for accurately classifying bone marrow cells. By improving bone marrow cancer diagnosis accuracy, this approach can improve patient care and treatment results.

关键词： Training Adaptation models Accuracy Computational modeling Neural networks Leukemia Cells (biology) computer architecture Bones Overfitting

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Enhanced Camouflage Detection Using advanced Image Processing Techniques for Real-Time Object Recognition and Pattern Differentiation 5

Enhanced Camouflage Detection Using Advanced Image Processin...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Manikandan, G. Deepa, R. Jayalakshmi, V. Thilakavathy, P. Surendran, R. St. Joseph's College of Engineering Anna University Department of Artificial Intelligence and Machine Learning Chennai India School of Computing SRM Institute of Science and Technology Department of Computing Technologies Chennai India SRM Institute of Science and Technology Department of Computer Science and Applications Chennai India Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530013

The identification of objects that blend in with their surroundings has long been a concern in fields including defence, wildlife monitoring, and surveillance due to the difficulty of detecting such objects. This finding is significant because it could greatly enhance our capacity to detect camouflaged objects, transforming the field of real-time object detection. Existing image processing methods often fail miserably when adequately capturing this aspect. This dissertation's primary focus is identifying devices with complicated patterns, legacy interference, and inadequate contrast;nonetheless, in disguised scenarios, all of these features cause identification to be unsuccessful. This research often suggests a new approach called the Linear Edge Detecting Scheme for Camouflaged Object Detection (LEDS-COD) in these difficult cases. The LEDS-COD system can enhance item visibility in low-comparison settings by highlighting critical edges and descriptions. Pattern differentiation methodologies and modern facility-side recognition techniques are combined to accomplish this goal. The approach successfully differentiated items because it zeroed in on texture and comparative details that would otherwise go unnoticed by humans or advanced algorithms. LEDS-COD is useful in many areas, including the Navy, autonomous surveillance systems, and animal protection, where the real-time identification of hidden devices is essential. Results from a simulation study using various datasets, including concealed objects, show that LEDS-COD surpasses conventional detection methods in recognition accuracy, processing speed, and perseverance. The recommended technique exceeded the requirements for obtaining improved object detection estimates for challenging conditions like those with complex backdrops and changing illumination. According to this study, LEDS-COD can be devastating in improving real-time camouflage detection. © 2024 IEEE.

关键词： Accuracy Image processing Surveillance Image edge detection Wildlife Lighting Object detection Real-time systems Object recognition Protection

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Adaptive Fine-Grained Feature Mining and RoI Feature Interaction Network for Small Object Detection in Aerial Images

Adaptive Fine-Grained Feature Mining and RoI Feature Interac...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Xie, Fangmin Xiong, Zhan Wang, Zhuoyue Yang, Kun Xiao, Guoqiang College of Computer and Information Science Southwest University Chongqing China Institute of Advanced Computer Science Leiden University Netherlands Department of Electrical Engineering and Computer Sciences University of California Berkeley Berkeley United States

ISBN: (纸本)9798350368741

Object detection in drone view images remains challenging due to small-scale objects distributed non-uniformly and exhibiting weak semantic features. Additionally, external factors such as lighting conditions, viewing angles, and background clutter further complicate the detection of small objects. To address these issues, we propose a small object detector called Adaptive Fine-grained Feature Mining Network (AFMNet) based on an anchor-free framework. Firstly, an Adaptive Fine-grained Feature Mining (AFFM) module is designed to generate Regions of Interest (RoI) on high-resolution feature maps and mine fine-grained information that benefits the representation of small objects. Secondly, an Inter-RoI Feature Interaction (IRFI) block is developed to efficiently integrate complementary cues-namely, deep layer semantic information and shallow layer spatial details-and enhance RoI features. Experiments on the VisDrone dataset demonstrate that our method achieves state-of-the-art performance. The source code of our AFMNet is available at https://***/xiefangmin/AFMNet. © 2025 IEEE.

关键词： Drone-View Image Feature Fusion Fine-grained Features Region of Interest Small object detection

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Optimized Deep Learning Approach for Efficient Diabetic Retinopathy Classification Combining VGG16-CNN

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computers, Materials & Continua 2023年第11期77卷 1855-1872页

作者： Heba M.El-Hoseny Heba F.Elsepae Wael A.Mohamed Ayman S.Selmy Department of Computer Science the Higher Future Institute for Specialized Technological StudiesObour11828Egypt Department of Electrical Engineering Benha Faculty of EngineeringBenha UniversityBenha13511Egypt

Diabetic retinopathy is a critical eye condition that,if not treated,can lead to vision *** methods of diagnosing and treating the disease are time-consuming and ***,machine learning and deep transfer learning(DTL)techniques have shown promise in medical applications,including detecting,classifying,and segmenting diabetic *** advanced techniques offer higher accuracy and *** Diagnosis(CAD)is crucial in speeding up classification and providing accurate disease ***,these technological advancements hold great potential for improving the management of diabetic *** study’s objective was to differentiate between different classes of diabetes and verify the model’s capability to distinguish between these *** robustness of the model was evaluated using other metrics such as accuracy(ACC),precision(PRE),recall(REC),and area under the curve(AUC).In this particular study,the researchers utilized data cleansing techniques,transfer learning(TL),and convolutional neural network(CNN)methods to effectively identify and categorize the various diseases associated with diabetic retinopathy(DR).They employed the VGG-16CNN model,incorporating intelligent parameters that enhanced its *** outcomes surpassed the results obtained by the auto enhancement(AE)filter,which had an ACC of over 98%.The manuscript provides visual aids such as graphs,tables,and techniques and frameworks to enhance *** study highlights the significance of optimized deep TL in improving the metrics of the classification of the four separate classes of *** manuscript emphasizes the importance of using the VGG16CNN classification technique in this context.

关键词： No diabetic retinopathy(NDR) convolution layers(CNV layers) transfer learning data cleansing convolutional neural networks a visual geometry group(VGG16)

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GEOSatDB:global civil earth observation satellite semantic database

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Big Earth Data 2024年第3期8卷 522-539页

作者： Ming Lin Meng Jin Juanzi Li Yuqi Bai Department of Earth System Science Institute for Global Change StudiesMinistry of Education Ecological Field Station for East Asian Migratory BirdsTsinghua UniversityBeijingChina Department of Computer Science and Technology Tsinghua UniversityBeijingChina Tsinghua Urban Institute Tsinghua UniversityBeijingChina

Satellite remote sensing,characterized by extensive coverage,fre-quent revisits,and continuous monitoring,provides essential data support for addressing global *** the past six decades,thousands of Earth observation satellites and sensors have been deployed *** valuable Earth observation assets are contributed independently by various nations and organizations employing diverse *** poses a significant challenge in effectively discovering global Earth observation resources and realizing their full *** this paper,we describe the develop-ment of GEOSatDB,the most complete semantic database of civil Earth observation satellites developed based on a unified ontology model.A similarity matching method is used to integrate satellite information and a prompt strategy is used to extract unstructured sensor *** resulting semantic database contains 127,949 semantic statements for 2,340 remote sensing satellites and 1,021 observation *** global Earth observation capabil-ities of 195 countries worldwide have been analyzed in detail,and a concrete use case along with an associated query demonstration is *** database provides significant value in effectively facilitating the semantic understanding and sharing of Earth observa-tion resources.

关键词： Earth observation satellite sensor semantic representation information extraction

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Forest Fire Detection Using Ensemble Deep Learning Model With XAi

Forest Fire Detection Using Ensemble Deep Learning Model Wit...

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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Tanvir, Kazi Kabir, Md. Sayem Anik, Zahid Hasan Hasan, Md. Rakib Tushar, Md. Tanbin Rahman, Rubaba Binte Vellore Institute of Technology School of Advanced Sciences Tamil Nadu Vellore India Department of Computer Science Dhaka Bangladesh

ISBN: (纸本)9798331529765

The incidence of forest fires seems to be rising and causing damages to the environment and properties, as well as loss of lives. The high rate of forest fires therefore requires effective early detection systems to address the catastrophic challenges the disasters pose. Traditional methods of fire detection, which rely heavily on direct visual observation systems and conventional sensor devices, are often slow and inadequate. This study introduces an intelligent detection system that integrates Deep learning with Explainable AI (XAI) to improve the early identification of forest fires. We developed an ensemble model integrating ResNet50V2 and InceptionV3 architectures. This study uses explainer techniques from XAI like LIME and Grad-CAM to ensure that the results meet compliance and performance standards as well as to reveal possible biases and flaws in the model's decision-making process. The findings indicate that incorporating an ensemble model significantly improves detection by achieving a high accuracy of 98%. It also showed better results regarding distinguishing between fire and no-fire conditions. Based on the approaches outlined in this paper, including XAI for model interpretability means that advanced deep learning models can be reliable and more effective even for real-world applications in forest fire detection. © 2024 IEEE.

关键词： Decision making

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Fundamental Security Risk Modeling in Smart Grid in the Modern Era of Artificial Intelligence

Fundamental Security Risk Modeling in Smart Grid in the M...

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International Conference on Power Engineering and Intelligent Systems, PEIS 2023

作者： Kothari, Rakshit Gill, Ayushi Maurya, Vijendra Kumar Paliwal, Anurag Department of Computer Science and Engineering Geetanjali Institute of Technical Studies Dabok Udaipur India Department of Electronics and Communication Geetanjali Institute of Technical Studies Dabok Udaipur India

ISBN: (纸本)9789819972159

The world is moving toward viable as well as reliable energy sources. The implementation of smart grids is one such drive that aims to coordinate the generation, distribution, and consumption of energy more efficiently. However, managing the smart grid is not an easy task as it involves dealing with a large amount of data and making real-time decisions. Artificial Intelligence has emerged as a promising solution to address these challenges. Smart grids enable utilities to optimize their current infrastructure while reducing the need to build more power plants since they are autonomous and increase the effectiveness and efficacy of electrical power management. In this paper, we have analyzed the management of smart grids using AI with various algorithms used in Smart Grid and focusing on power generation and distribution systems. The paper also highlights the benefits of using AI such as improved efficiency, reduced costs, and enhanced reliability with applications for fault detections and provides the comparison of traditional and modern technology in smart grids. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Energy utilization

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Evaluation of Expressing without asserting approaches in RDF. The case of Conjectures 20

Evaluation of Expressing without asserting approaches in RDF...

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20th Conference on Information and Research science Connecting to Digital and Library science, IRCDL 2024

作者： Pasqual, Valentina Manzano, Gerald Uzeir, Eduart Tomasi, Francesca Vitali, Fabio Digital Humanities Advanced Research Center Department of Classica Philology and Italian studies University of Bologna Italy Department of Computer Science University of Bologna Italy

In this paper, we evaluate the existing reification approaches for expressing without asserting (EWA) statements in RDF along with their related contextual information and compare them with a new method, called Conjectures. Conjectures express RDF statements with three states of knowledge: undisputed claims, disputed claims, and settled claims. Conjectures extend the semantics of RDF Named graphs and introduce a new syntactical form to represent both conjectural and asserted information. Our evaluation tests were performed on a large sample of Wikidata entities about artworks interspersed with additional dummy statements to simulate alternative or abandoned claims and enrich the set of non-asserted claims. Our study evaluates metrics such as the total number of triples, loading time, dataset weight, and in particular query execution time for many different and meaningful types of queries. Results show that Conjectures is competitive with existing methods and outperforms other methods in terms of efficiency when retrieving debated statements, thereby demonstrating its potential as an effective tool for expressing nuanced RDF statements. © 2021 Copyright for this paper by its authors.

关键词： Semantics

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Game-based Activity Design in Primary School Students' Learning Style Detection

Game-based Activity Design in Primary School Students' Learn...

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2023 International Conference on ENTERprise Information Systems, CENTERIS 2023 - International Conference on Project MANagement, ProjMAN 2023 - International Conference on Health and Social Care Information Systems and Technologies, HCist 2023

作者： Fernando, Pumudu A. Premadasa, H.K. Salinda Department of Computer Science Informatics Institute of Technology Colombo06 0600 Sri Lanka Centre for Computer Studies University of Sabaragamuwa Belihuloya70140 Sri Lanka

Generation Alpha, the present primary school cohort born after 2010, has significant exposure to mobile devices and gaming. Adopting a "One Size Fits All"approach in modern teaching methods may not be effective, as it overlooks individual learning preferences. Personalized learning can be facilitated by identifying a student's learning style (LS). Adaptive learning based on LS has been found to have positive effects in several studies. However, traditional learning style detection techniques such as questionnaires and self-assessments can be time-consuming and demotivating for primary school students. This study aims to propose a game-based activity framework as an alternative to the Index of Learning Style (ILS) questionnaire linked with Felder Silverman Learning Style Model for LS detection. The proposed game was evaluated with a sample of sixty students, and preliminary results indicate that the game outperforms the original ILS questionnaire in terms of student engagement and motivation to complete LS activities, achieving an overall satisfaction rate of 87.5%. The second phase of the research will focus on evaluating the accuracy of LS prediction using the designed game, which is currently ongoing. © 2024 The Author(s).

关键词： Contrastive Learning

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Survey on digital twins for Internet of Vehicles:Fundamentals,challenges,and opportunities

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Digital Communications and Networks 2024年第2期10卷 237-247页

作者： Jiajie Guo Muhammad Bilal Yuying Qiu Cheng Qian Xiaolong Xu Kim-Kwang Raymond Choo School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China Department of Computer Engineering Hankuk University of Foreign StudiesGyeonggi-do17035South Korea Jiangsu Hydraulic Research Institute Nanjing210044China Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

As autonomous vehicles and the other supporting infrastructures(e.g.,smart cities and intelligent transportation systems)become more commonplace,the Internet of Vehicles(IoV)is getting increasingly *** have been attempts to utilize Digital Twins(DTs)to facilitate the design,evaluation,and deployment of IoV-based systems,for example by supporting high-fidelity modeling,real-time monitoring,and advanced predictive ***,the literature review undertaken in this paper suggests that integrating DTs into IoV-based system design and deployment remains an understudied *** addition,this paper explains how DTs can benefit IoV system designers and implementers,as well as describes several challenges and opportunities for future researchers.

关键词： Internet of vehicles Digital twin Simulation Traffic systems

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