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Emerging Systems and Intelligent Computing (ESIC), International Conference on

作者： Rajdeep Haldar Saptadip Rooj Neeraj Sharma Ritambhar Das Arnab Mandal Anjan Bandyopadhyay Dept. of Information Technology Kalinga Institute of Industrial Technology (KIIT) Bhubaneswar India School of Computer Science and Engineering Lovely Professional University Jalandhar India School of Computer Engineering Kalinga Institute of Industrial Technology (KIIT) Bhubaneswar India

ISBN: (数字)9798331522100

ISBN: (纸本)9798331522117

This paper explores the application of game theoretic approaches to load balancing across edge nodes within distributed computing systems. Focusing on a non-cooperative game model, we aim to enhance system efficiency by strategically optimizing resource allocation and load distribution among autonomous edge nodes. Initial simulations demonstrate that our approach not only addresses the inherent complexities of edge environments but also outperforms traditional load balancing mechanisms in terms of both operational efficiency and system responsiveness.

关键词： Computational modeling Focusing Games Load management Complexity theory Resource management Game theory Distributed computing Load modeling Edge computing

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Use of supervised and unsupervised approaches to make zonal application maps for variable-rate application of crop growth regulators in commercial cotton fields

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Journal of Cotton Research 2025年第1期8卷 1-20页

作者： ANDREA Maria C.da S. OLIVEIRA Cristiano F.de MOTA Fabrícia C.M. SANTOS Rafael C.dos RODRIGUES JUNIOR Edilson F. BIANCHI Lucas M. OLIVEIRA Rodrigo S.de GOUVEIA Caio M.de BARBOSA Victor G.S. BISPO E SILVA Marco A. Nuvem Tecnologia CuiabáMato Grosso 78005-375Brazil Institute of Sci-ence and Technology Federal University of São PauloSão Josédos CamposSP 12247-014Brazil Institute of Math and Computer Science University of São PauloSão CarlosSP 13566-590Brazil Department of Agricultural Engineering Federal University of ViçosaViçosaMG 36570-000Brazil Department of Computer Science Federal University of ParanáCuritibaPR 80060-000Brazil

Background Zonal application maps are designed to represent field variability using key variables that can be translated into tailored management *** cotton,zonal maps for crop growth regulator(CGR)applications under variable-rate(VR)strategies are commonly based exclusively on vegetation indices(VIs)***,VIs often saturate in dense crop vegetation areas,limiting their effectiveness in distinguishing variability in crop *** study aimed to compare unsupervised framework(UF)and supervised framework(SUF)approaches for generat-ing zonal application maps for CGR under VR *** 2022-2023 agricultural seasons,an UF was employed to generate zonal maps based on locally collected field data on plant height of cotton,satellite imagery,soil texture,and phenology ***,a SUF(based on historical data between 2020-2021 to 2022-2023 agricultural seasons)was developed to predict plant height using remote sensing and phenology data,aiming to replicate same zonal maps but without relying on direct field measurements of plant *** approaches were tested in three fields and on two different dates per *** The predictive model for plant height of SUF performed well,as indicated by the model ***,when comparing zonal application maps for specific field-date combinations,the predicted plant height exhibited lower variability compared with field *** led to variable compatibility between SUF maps,which utilized the model predictions,and the UF maps,which were based on the real field *** characterized by much pronounced soil texture variability yielded the highest compatibility between the zonal application maps produced by both SUF and UF *** was predominantly due to the greater consistency in estimating plant development patterns within these heterogeneous field *** VR application approach can facilitate product savings during the application operation,other k

关键词： Cotton Site-specific management Crop growth regulator Unsupervised framework Supervised framework Zonal application maps

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Variational Onsager Neural Network optimized with Golden search optimization algorithm fostered for lung disease detection system in IoT

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Biomedical Signal Processing and Control 2025年 108卷

作者： Sudha, G. Angayarkanni, V. Kanagavalli, K.R. Pattewar, Tareek Department of Biomedical Engineering Muthayammal Engineering College Tamil Nadu Rasipuram637408 India Department of Computing Technologies Faculty Of Engineering And Technology SRM Institute of Science and Technology Kattankulathur Chengalpattu District Tamil Nadu 603203 India Department of Computer Science Government Arts and Science College Tamil Nadu Sivakasi626124 India Department of Computer Science and Engineering Vishwakarma University Pune India

Pneumonia causes a high rate of newborn morbidity and mortality. The challenge is accurately identifies respiratory disorders while overcoming the limitations of existing technologies such as low accuracy, delayed response, and restricted scalability. To overcome this complication, Variational Onsager Neural Network optimized with Golden search optimization algorithm fostered for Lung Disease Detection system in IoT (LDD-VONN-CXR-IoT) is proposed. Initially, input CXR images are gathered from chest-X-ray Dataset. Then, pre-process the input CXR images using Two-way Recursive filtering (TWRF) for normalizing image and increasing the quality of the images. Afterwards, the preprocessed image is supplied to the feature extraction. Adaptive Synchro Extracting Transform (ASET) is employed to extract the statistical features. Finally, the extracted features are fed into Variational Onsager Neural Networks (VONN) which classifies the input CXR image into normal and pneumonia. The Golden Search Optimization Algorithm (GSOA) is used to optimize VONN that accurately detects the Lung Disease. The proposed LDD-VONN-CXR-IoT method is implemented. The performance metrics, like precision, accuracy, F1-score, Sensitivity, specificity, Error rate, ROC, computational time are examined. The proposed LDD-VONN-CXR-IoT approach attains 99.57%, 98.46%, and 98.13% for accuracy, F1 score, and precision respectively. These outcomes prove that this method for the Lung Disease Detection system in IoT is effectual tool to assist in clinical diagnosis. This method allows expertise to acquire exact results, thus providing the proper treatment. © 2025 Elsevier Ltd

关键词： Lung cancer

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Exploring Deep Learning Paradigms for Handwritten Digit Recognition: Insights and Advances

Exploring Deep Learning Paradigms for Handwritten Digit Reco...

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International Conference on Communication Systems and Network Technologies (CSNT)

作者： Amit Kumar Gupta Priya Mathur Nysa Maheshwari Ayushi Mathur Computer Science and Engineering Manipal University Jaipur Jaipur Rajasthan India Department of Mathematics Poornima Institute of Engineering and Technology Jaipur Rajasthan India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

In the application area of postal automation to signature verification, the Handwritten digit recognition (HRD) is major challenging field in the era of pattern recognition and machine learning. The convolutional neural networks (CNNs) becoming most emerging technology in the field of deep learning. The CNN made significant advancement in landscape of digit recognition, surpassing traditional methods in accuracy and performance. Motivated by the significance of accurate digit classification, this research presents a comprehensive review and synthesis of seminal works in the field. Through a meticulous examination of pioneering studies, we identify trends, challenges, and emerging paradigms characterizing contemporary research in handwritten digit recognition. Five deep learning methods, including a Baseline MLP, Simple CNN Model, Larger CNN Model, Basic ResNet Architecture, and Inception V3, are evaluated for their performance in digit recognition tasks. Results demonstrate that the Inception V3 model achieves the highest accuracy of 0.99, with a precision of 0.9929 and a recall of 0.9995. The review encompasses foundational works laying the groundwork for digit recognition algorithms, as well as cutting-edge research leveraging transfer learning, hybrid models, and advanced deep learning architectures. By synthesizing insights from these studies, our aim is to provide researchers and practitioners with a comprehensive understanding of state-of-art techniques, enabling informed decision-making and inspiring future advancements in HRD.

关键词： Deep learning Handwriting recognition Technological innovation Accuracy Reviews Communication systems Decision making Transfer learning Market research Convolutional neural networks

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Quadrant Analysis of Learner’s Performance in Quantitative Aptitude and Coding

Quadrant Analysis of Learner’s Performance in Quantitative ...

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Emerging Systems and Intelligent Computing (ESIC), International Conference on

作者： Surya Pavan Kumar Gudla N V Murali Krishna Raja Karri Chandra Sekhar S. Anand Kumar B. Ravi Kumar C P Pavan Kumar Hota Department of CSE Aditya Institute of Technology and Management Department of CSE Sri Vasavi Engineering College Computer Science and Engineering (Artificial Intelligence) Madanapalle Institute of Technology & Science Department of CSE Anil Neerukonda Institute of Technology and Sciences Department of Artificial Intelligence Shri Vishnu Engineering College for Women

ISBN: (数字)9798331522100

ISBN: (纸本)9798331522117

With the aim of improving learning for varied learners in various situations, abstract educational data mining (EDM) entails utilising methodologies, tools, and research to extract significant insights from educational data repositories. The primary objective of this research is to examine the quantitative aptitude and coding performance of 366 engineering undergraduate students. A convenience and random sampling technique was used to gather the dataset, and preprocessing was then performed to guarantee data completeness. Descriptive statistics demonstrate that students do well overall in both areas, with slightly more variability in Quantitative Aptitude scores than in Coding, where performance is more constant. Based on how well the students performed in these two areas, a quadrant analysis was used to classify the students into four groups: Excellent, Good, Average, and Poor. The results of this analysis demonstrated the differences in the way students developed in the two skill areas, highlighting the significance of tailored educational interventions. Quantitative aptitude and coding had a weakly positive link, according to the correlation study, indicating that these abilities are largely independent of one another. The study highlights that in order to help pupils in these areas, specific instructional practices are required. It is advised to connect technology with pedagogy through the Metacognition-aided Technological Pedagogical Content Knowledge (TPACK) framework. This will improve student outcomes in programming courses and enhance their metacognition. The results offer insightful information on the patterns of student performance that can guide the creation of instructional interventions that are more successful.

关键词： Correlation Education Metacognition Encoding Complexity theory Data mining Pupils Programming profession

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IR-MBiTCN: Computational prediction of insulin receptor using deep learning: A multi-information fusion approach with multiscale bidirectional temporal convolutional network

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International Journal of Biological Macromolecules 2025年 311卷 143844页

作者： Ali, Farman Masmoudi, Atef Alkhalifah, Tamim Alturise, Fahad Alghamdi, Wajdi Khalid, Majdi Department of Computer Science Bahria University Islamabad Pakistan Department of Computer Science College of Computer Science King Khalid University Abha61421 Saudi Arabia Department of Computer Engineering College of Computer Qassim University Buraydah Saudi Arabia Department of Cybersecurity College of Computer Qassim University Buraydah Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computing Umm Al-Qura University Makkah21955 Saudi Arabia

The insulin receptor (IR) is a transmembrane protein that controls glucose homeostasis and is highly associated with chronic diseases including cancer and neurological. Traditional experimental methods have provided essential insights into IR structure and function, but they are constrained by time, cost, and scalability. To address these limitations, we present a computational technique for IR prediction based on deep learning and multi-information fusion. First, we built sequence-based training and testing datasets. Second, the compositional, word embedding, and evolutionary features were retrieved using the Weighted-Group Dipeptide Composition (W-GDPC), FastText, and Bi-Block-Position Specific Scoring Matrix (BB-PSSM), respectively. Third, we use compositional, word embedding, and evolutionary features to generate multi-perspective fused features (MPFF). Fourth, the Multiscale Bidirectional Temporal Convolutional Network (MBiTCN) is used to train the model to process features at multiscale and analyze sequences in both forward and backward directions. The proposed approach (IR-MBiTCN) outperforms competing deep learning (DL) and machine learning (ML)-based models on training and testing datasets, achieving 83.50 % and 79.43 % accuracy, respectively. This study represents a pioneering use of computational methodology in IR prediction, providing a scalable, efficient alternative to experimental procedures and paving the way for advances in chronic disease therapy and drug discovery. © 2025 Elsevier B.V.

关键词： Deep learning

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Lightweight Detection Algorithm for Breast-Mass Features in Ultrasound Images

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IEEE Access 2025年 13卷 75754-75768页

作者： Li, Taojuan Liu, Wen Song, Mingxian Gu, Zheng Hai, Ling School of Computer Science and Technology Xinjiang Normal University Ürümqi830000 China Artificial Intelligence and Smart Mine Engineering Technology Center Xinjiang Institute of Engineering Ürümqi830023 China Xinjiang Changsen Data Technology Company Ltd. Ürümqi830011 China Family Planning Service Center of Maternal Child Health Hospital Xinjiang Tacheng China

Real-time analysis of ultrasound videos using embedded terminals enables the rapid detection of breast masses and plays a crucial role in early breast cancer screening and diagnosis. However, as a paired organ with a large area, the breast consists of interwoven fatty layers, mammary ducts, glandular tissues, and masses, leading to a high false-positive rate in target detection. Unlike standard imaging modalities such as computed tomography, X-ray, and magnetic resonance imaging, ultrasound detection heavily depends on the sonographer’s techniques and expertise, which can lower detection accuracy, particularly in identifying small *** paper proposes a novel architecture, LEW-YOLO, to address the real-time detection demands of embedded terminal devices for breast ultrasound. First, we introduce an efficient multiscale convolutional (EMSC) module to improve feature extraction from complex backgrounds and enhance multiscale representation. Unlike traditional methods, EMSC employs multiple convolution branches with varying kernel sizes, enabling more effective multiscale feature capture. Second, the detection head of YOLOv8 is replaced with a lightweight shared detail-enhanced convolutional detection head (LSDECD) to improve the model’s ability to detect small masses. Finally, a weighted intersection-over-union (WIoU) loss function is integrated to better capture the complex boundaries of malignant *** on the public BUSI dataset demonstrate that LEW-YOLO achieves an mAP50 of 91.6%, surpassing the YOLOv8n baseline (89.0%) by 2.6%. On the BUET dataset, LEW-YOLO attains an mAP50 of 83.3%, outperforming YOLOv8 (72.3%) by 11.0%. Moreover, the parameter count and GFLOPs are reduced by 26.6% and 22.2%, respectively. Thus, the proposed model effectively balances detection performance and lightweight design, making it well-suited for real-time applications on resource-constrained computing devices. © 2013 IEEE.

关键词： Ultrasonography

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Hesitant Bi-fuzzy Dombi Operator Based Group Decision-making Approach for Electric Vehicle Selection

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Process Integration and Optimization for Sustainability 2025年 1-35页

作者： Golui, S. Das, Bidyut Mahapatra, G. S. Mahapatra, B. S. Department of Engineering Sciences and Humanities Siliguri Institute of Technology Siliguri India Department of Applied Mathematics Maulana Abul Kalam Azad University of Technology Haringhata India Department of Master of Computer Applications Siliguri Institute of Technology Siliguri India Department of Mathematics National Institute of Technology Puducherry Karaikal India School of Applied Science and Humanities Haldia Institute of Technology Haldia India

Electric vehicles (EVs) are one of the sustainable modes of transportation that can reduce the air pollution caused by fossil fuel-powered automobiles. The literature overlooks non-technical criteria, including environmental considerations and social responsibility in the EV selection process, which may navigate the adoption of EVs. This study addresses the EV selection process in the hesitant bi-fuzzy context, considering technical and non-technical EV features. A multi-criteria group decision-making approach is developed to evaluate EVs utilizing a modified hesitant bi-fuzzy COmbinative Distance-based Assessment technique. This study proposes a novel hesitant bi-fuzzy Dombi weighted average operator to integrate the decision maker’s opinions with their weightage. The proposed approach incorporates the importance of the criteria evaluated by the method based on the removal effects of criteria and hesitant information in Euclidean and Taxicab metrics to produce the ranking sequence of EVs. The Python code for the suggested group decision-making model is provided to rationalize the computational process. This study highlights the relative importance of charging time, price, and range compared to the remaining features in selecting EVs. The study’s findings indicate that the technical features of an EV are still a primary consideration for the customers. These findings will assist policymakers and EV industry stakeholders in making decisions to focus on EV modification. A comparative analysis with the existing decision-making models encompasses the applicability of the proposed technique. In contrast, the sensitivity analysis demonstrates the suggested approach’s robustness through the alteration in decision makers’ s preferences, internal parameters, and criteria importance to assess their impact on the final rankings of EVs.

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Multi AI Agents based Black Box Audio Transcriber and Aviation Incident Report Generator

Multi AI Agents based Black Box Audio Transcriber and Aviati...

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Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： Ghanashyam Vagale Mohammed Naif Khan Rajat Goutam Devarakonda Nikhil Matur Rohith Kumar Tarun Narula Khoury College of Computer Sciences Northeastern University Boston USA Department of Computer Science Ramaiah University Bangalore India Centre for Artificial Intelligence National Institute of Technology Jalandhar India Department of Computer Science National Institute of Technology Jalandhar India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

This paper introduces a framework that automates the transcription of black box audio data and the generation of aviation incident reports using AI agents. Traditionally, manual transcription of cockpit recordings is time-consuming and errorprone. The proposed system employs Groq-Whisper for audio transcription and various AI agents for report generation, streamlining the analysis of incident data. Each agent focuses on specific tasks, such as refining transcriptions and structuring conversations, leading to the identification of critical events and causal factors in the reports. This approach enhances accuracy and efficiency in aviation safety investigations, providing a reliable tool for real-time decision-making. The paper highlights the significance of AI integration to improve efficiency while ensuring explainability and trust in aviation contexts, ultimately contributing to enhanced air travel safety.

关键词： Electronic equipment Accuracy Flight recording Refining Manuals Real-time systems Safety Reliability Noise measurement Artificial intelligence

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CRCL: Causal Representation Consistency Learning for Anomaly Detection in Surveillance Videos

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IEEE Transactions on Image Processing 2025年 34卷 2351-2366页

作者： Liu, Yang Wang, Hongjin Wang, Zepu Zhu, Xiaoguang Liu, Jing Sun, Peng Tang, Rui Du, Jianwei Leung, Victor C. M. Song, Liang Fudan University Academy for Engineering and Technology Shanghai200433 China University of Toronto Department of Computer Science TorontoONM5S 1A1 Canada Hunan University College of Electrical and Information Engineering Hunan Changsha410082 China Duke Kunshan University Division of Natural and Applied Sciences Jiangsu Suzhou215316 China University of California at Davis DataLab: Data Science and Informatics DavisCA95616 United States The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada Duke Kunshan University Division of Natural and Applied Sciences Suzhou215316 China Fudan University School of Information Science and Technology Shanghai200433 China China Mobile Chengdu Institute of Research and Development Chengdu610065 China

Video Anomaly Detection (VAD) remains a fundamental yet formidable task in the video understanding community, with promising applications in areas such as information forensics and public safety protection. Due to the rarity and diversity of anomalies, existing methods only use easily collected regular events to model the inherent normality of normal spatial-temporal patterns in an unsupervised manner. Although such methods have made significant progress benefiting from the development of deep learning, they attempt to model the statistical dependency between observable videos and semantic labels, which is a crude description of normality and lacks a systematic exploration of its underlying causal relationships. Previous studies have shown that existing unsupervised VAD models are incapable of label-independent data offsets (e.g., scene changes) in real-world scenarios and may fail to respond to light anomalies due to the overgeneralization of deep neural networks. Inspired by causality learning, we argue that there exist causal factors that can adequately generalize the prototypical patterns of regular events and present significant deviations when anomalous instances occur. In this regard, we propose Causal Representation Consistency Learning (CRCL) to implicitly mine potential scene-robust causal variable in unsupervised video normality learning. Specifically, building on the structural causal models, we propose scene-debiasing learning and causality-inspired normality learning to strip away entangled scene bias in deep representations and learn causal video normality, respectively. Extensive experiments on benchmarks validate the superiority of our method over conventional deep representation learning. Moreover, ablation studies and extension validation show that the CRCL can cope with label-independent biases in multi-scene settings and maintain stable performance with only limited training data available. © 1992-2012 IEEE.

关键词： Adversarial machine learning

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