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A Framework for Promoting Passive Breast Cancer Monitoring: Deep Learning as an Interpretation Tool for Breast Thermograms

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Iranian Journal of Medical Physics 2024年第4期21卷 237-248页

作者： Firouzmand, Mohamad Majidzadeh, Keivan Jafari, Maryam Haghighat, Shahpar Esmaeili, Rezvan Moradi, Leila Misaghi, Nima Ensafi, Mahsa Batmanghelich, Fatemeh Keyvanpour, Mohammad Reza Shojaedini, Seyed Vahab Biomedical engineering Department Iranian Research Organization for Science & Technology Tehran Iran Breast Cancer Research Center Motamed Cancer Institute ACECR Tehran Iran Department of Computer Engineering Faculty of Engineering Islamic Azad University E-Campus Tehran Iran Department of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran

Introduction: Several types of cancer can be detected early through thermography, which uses thermal profiles to image tissues in recent years, thermography has gained increasing attention due to its non-invasive and radiation-free nature. There is a growing need for thermographic images of breast cancer lesions in different nationalities and ages to develop this technique, however. This study aims to introduce a dataset of breast thermograms. Material and Methods: In this study, thermographic images of breast cancer from Iranian samples were prepared and confirmed due to the limited number of breast thermogram databases. The prepared database was tested using artificial intelligence and another well-known DMR database (Database for Mastology Research) in this study to determine its reliability. Results: A variety of deep learning architectures and transfer learning are used to evaluate these databases for accuracy, sensitivity, speed, training compliance, and validation compliance. According to best-fitted structures for both types of databases, the database obtained from this study has a quality comparable to the DMR reference database, with minimum accuracy, sensitivity, specificity, precision, and F-score of 80%, 86%, 86%, 88%, and 87%, respectively. Conclusion: Using thermography as a method of early breast screening is demonstrated to be effective. In comparison to DMR, the lower statistics of the proposed database (between 2 and 7 percent) indicates that more diverse breast thermograms should be captured in conjunction with improvements to imaging equipment as well as adherence to thermography recording protocols in order to improve the reliability and efficiency of the database. © (2024), (Mashhad University of Medical sciences). All rights reserved.

关键词： Medical imaging

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AI-Driven Learning Management Systems:Modern Developments, Challenges and Future Trends during theAge of ChatGPT

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computers, Materials & Continua 2024年第8期80卷 3289-3314页

作者： Sameer Qazi Muhammad Bilal Kadri Muhammad Naveed Bilal AKhawaja Sohaib Zia Khan Muhammad Mansoor Alam Mazliham Mohd Su’ud Department of Computer Science Faculty of EngineeringScience and TechnologyIQRA UniversityKarachi75500Pakistan College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Faculty of Engineering Department of Electrical EngineeringIslamic University of MadinahP.O.BOX 170Madinah41411Saudi Arabia Faculty of Engineering Department of Mechanical EngineeringIslamic University of MadinahP.O.BOX 170Madinah41411Saudi Arabia Faculty of Computing Riphah International UniversityIslamabad46000Pakistan Faculty of Computing and Informatics(FCI)Multimedia University Cyberjaya63100Malaysia Faculty of Engineering and Information Technology School of Computer ScienceUniversity of Technology SydneySydneyNSW 2007Australia

COVID-19 pandemic restrictions limited all social activities to curtail the spread of the *** foremost and most prime sector among those affected were schools,colleges,and *** education system of entire nations had shifted to online education during this *** shortcomings of Learning Management Systems(LMSs)were detected to support education in an online mode that spawned the research in Artificial Intelligence(AI)based tools that are being developed by the research community to improve the effectiveness of *** paper presents a detailed survey of the different enhancements to LMSs,which are led by key advances in the area of AI to enhance the real-time and non-real-time user *** AI-based enhancements proposed to the LMSs start from the Application layer and Presentation layer in the form of flipped classroom models for the efficient learning environment and appropriately designed UI/UX for efficient utilization of LMS utilities and resources,including AI-based *** layer enhancements are also required,such as AI-based online proctoring and user authentication using *** extend to the Transport layer to support real-time and rate adaptive encrypted video transmission for user security/privacy and satisfactory working of *** also needs the support of the Networking layer for IP-based geolocation features,the Virtual Private Network(VPN)feature,and the support of Software-Defined Networks(SDN)for optimum Quality of Service(QoS).Finally,in addition to these,non-real-time user experience is enhanced by other AI-based enhancements such as Plagiarism detection algorithms and Data Analytics.

关键词： Learning management systems chatbots ChatGPT online education Internet of Things(IoT) artificial intelligence(AI) convolutional neural networks natural language processing

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A Deep Learning Approach for Landmines Detection Based on Airborne Magnetometry Imaging and Edge Computing

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computer Modeling in engineering & sciences 2024年第5期139卷 2117-2137页

作者： Ahmed Barnawi Krishan Kumar Neeraj Kumar Bander Alzahrani Amal Almansour Faculty of Computing and Information Technology King Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science and Engineering Thapar Institute of Engineering and TechnologyDeemed to be UniversityPatialaIndia

Landmines continue to pose an ongoing threat in various regions around the world,with countless buried landmines affecting numerous human *** detonation of these landmines results in thousands of casualties reported worldwide ***,there is a pressing need to employ diverse landmine detection techniques for their *** effective approach for landmine detection is UAV(Unmanned Aerial Vehicle)based AirborneMagnetometry,which identifies magnetic anomalies in the local terrestrial magnetic *** can generate a contour plot or heat map that visually represents the magnetic field *** the effectiveness of this approach,landmine removal remains a challenging and resource-intensive task,fraughtwith *** computing,on the other hand,can play a crucial role in critical drone monitoring applications like landmine *** processing data locally on a nearby edge server,edge computing can reduce communication latency and bandwidth requirements,allowing real-time analysis of magnetic field *** enables faster decision-making and more efficient landmine detection,potentially saving lives and minimizing the risks involved in the ***,edge computing can provide enhanced security and privacy by keeping sensitive data close to the source,reducing the chances of data exposure during *** paper introduces the MAGnetometry Imaging based Classification System(MAGICS),a fully automated UAV-based system designed for landmine and buried object detection and *** have developed an efficient deep learning-based strategy for automatic image classification using magnetometry dataset *** simulating the proposal in various network scenarios,we have successfully detected landmine signatures present in themagnetometry *** trained models exhibit significant performance improvements,achieving a maximum mean average precision value of 97.8%.

关键词： CNN deep learning landmine detection magnetometer mean average precision UAV

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QuakeGuard Insight: An IoT-Enabled Machine Learning System 4

QuakeGuard Insight: An IoT-Enabled Machine Learning System

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4th IEEE International Conference on Data engineering and Communication Systems, ICDECS 2024

作者： Amat, Kapileshwor Ray Baitha, Aashutosh Adhikarichhetri, Bhabishya Bohara, Neha Bista, Chirayou Shreenidhi, H.S. University Computer Science and Engineering-IOT Faculty of Engineering and Technology Bangalore India

ISBN: (纸本)9798350393354

In this work, an earthquake prediction system utilizing machine learning (ML) techniques and Internet of Things (IoT) technologies is presented, using accelerometer data from the ADXL335 sensor. In order to analyze seismic patterns, the system records multi-axis accelerations. Various machine learning models are then used for predictive analytics. This technology seeks to predict probable seismic events by combining sensor data with sophisticated algorithms, assisting early warning systems for disaster readiness. The ADXL335 accelerometer is the central component of the Earthquake Prediction System described in this work. It records accelerations on the X, Y, and Z axes and converts them into analogue signals for further processing. These data streams are transmitted for feature extraction by utilizing IoT infrastructure, with an emphasis on seismic patterns that may indicate future earthquake events. To evaluate the accelerometer data and produce predicted insights, the system incorporates a variety of machine learning models, such as decision trees and support vector machines. The goal is to support disaster management plans by enabling early detection and warning of seismic activity through this combination of sensor technology and advanced analytics. A wide variety of machine learning models, such as decision trees, support vector machines, and recurrent neural networks, are used to derive actionable insights. These algorithms produce predictive analytics to support catastrophe management methods by carefully analyzing accelerometer data. The ultimate objective is to enable more proactive disaster mitigation planning by facilitating early detection and alerts of seismic activity. This system, which combines advanced analytics with sensor technology, is a critical step in strengthening disaster management systems. Its capacity to predict seismic events may help minimize the effects of earthquakes on impacted areas, help with evacuation plans, and provide timely a

关键词： Internet of things

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Federated architecture for serverless platforms aimed at transparent execution in the edge-cloud continuum

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International Journal of Cloud Computing 2025年第1期14卷 115-144页

作者： Kjorveziroski, Vojdan Filiposka, Sonja Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Rudzer Boshkovikj 16 Skopje1000 Macedonia

The stateless nature of serverless computing makes it a viable choice for establishing the long-desired edge-cloud continuum. Current efforts to provide a unified view over both the cloud and the edge are vendor-centric, with proprietary interfaces. This makes interoperability between different infrastructures difficult, while also raising questions about future-proofing. To overcome this problem, we introduce a federation layer which provides a unified view over distinct edge-cloud solutions. We summarise the current open questions and define a set of functional requirements for a unifying federation layer. We identify the main pillars required for fulfilling the requirements from a technical perspective and discuss concrete implementation approaches. Finally, we also showcase a practical verification of the architecture, leveraging a federation of geographically distributed Kubernetes clusters. The verification is done using multiple serverless runtime options, with computing environments scattered both in the cloud and in the edge, overcoming various connectivity restrictions in place. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Cloud platforms

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An Efficient Machine Learning Classification Model for Rainfall Prediction in Bangladesh 2nd

An Efficient Machine Learning Classification Model for Rainf...

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2nd International Conference on Big Data, IoT and Machine Learning, BIM 2023

作者： Biplob, Md. Badiuzzaman Haque, Md. Mokammel Computer Science and Engineering Department Chittagong University of Engineering and Technology Chattogram Bangladesh Computer Science and Engineering Department Faculty of Engineering Chittagong University of Engineering and Technology Chattogram Bangladesh

ISBN: (纸本)9789819989362

One of the most significant and difficult tasks in the modern world is rainfall forecast. Rainfall is a complicated and nonlinear phenomenon that requires sophisticated computer modeling and simulation to anticipate with any degree of accuracy. In many regions of the world, daily rainfall totals are dispersed based on various frequency distribution functions. Rainfall has become a significant factor in agricultural countries. We provide a machine learning-based framework for forecasting the total monthly precipitation. In this study, three Bangladeshi weather stations’ daily rainfall was forecasted with a 365-day lead time using backpropagation long short-term memory (LSTM), random forest, and neural network algorithms. The predicted results from the selected algorithms were compared with the observed data to determine prediction precision by mean absolute error (MAE) test results. We found that selected algorithms predicted daily rainfall with reasonable accuracy. Therefore, year-long, month-long, and day-long rainfall can be predicted using these models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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Hybrid DCNN-SVM Architecture for Optimizing Sugarcane Leaf Disease Classification

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2025年第4期52卷 974-985页

作者： Kurniawan, Rudi Siaga, Erna Samsuryadi, Samsuryadi Susilo, Andrianto Tri Sunardi, Lukman Faculty of Engineering Science Universitas Bina Insan Lubuklingau31626 Indonesia Faculty of Agriculture Universitas Sriwijjaya Palembang30862 Indonesia Faculty of Computer Science Universitas Sriwijaya Palembang30862 Indonesia

Sugarcane leaf diseases are major agricultural issues that lead to reduced production and economic losses. Early detection is crucial for controlling their spread. This study introduces a solution for identifying sugarcane leaf diseases using image processing and deep learning techniques. Preprocessing steps include normalization for pixel scale stabilization and Gaussian blur for noise reduction. Features are extracted using a pre-trained DenseNet201 model, and classification is performed using a Support Vector Machine (SVM) with an RBF kernel. The proposed hybrid DCNN-SVM architecture achieves a test accuracy of 96.74%, demonstrating effectiveness in disease classification. Comprehensive evaluations highlight the model’s strengths and areas for improvement, providing a foundation for future research in plant disease detection. © (2025.0), (International Association of Engineers). All Rights Reserved.

关键词： Noise abatement

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A Battery-Powered Wild Animal Tracking Device Using a PTZ Camera and Deep Learning

A Battery-Powered Wild Animal Tracking Device Using a PTZ Ca...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Semba, Shogo Saito, Hiroshi Tomioka, Yoichi Kohira, Yukihide The University of Aizu Faculty of Computer Science and Engineering Japan

ISBN: (纸本)9781665410205

In this paper, we propose a battery-powered wild animal tracking device using a Pan-Tilt-Zoom (PTZ) camera and deep learning. The proposed tracking device detects wild animals using YOLOv5 and tracks the detected wild animals using DeepSort. In addition, the proposed tracking device realizes tracking for a wide range by controlling a PTZ camera according to the movement direction of the detected wild animals. In the experiment, we developed a prototype for the proposed tracking device and conducted the field test of the developed prototype. We confirmed and discussed cases where the developed prototype could and could not track wild animals. The energy consumption of the developed prototype during tracking was 526.03J at the daytime and 730.99J at the nighttime. © 2024 IEEE.

关键词： Invertebrates

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Integrated Microfluidic Rapid Response and Highly Sensitive Electrochemical Aptasensor for Simultaneous Detection of Aflatoxin B1 and Deoxynivalenol 38

Integrated Microfluidic Rapid Response and Highly Sensitive ...

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38th IEEE International Conference on Micro Electro Mechanical Systems, MEMS 2025

作者： Wu, Jinlei Jin, Qinghui Yang, Ping Gao, Wanlei Ningbo University The Faculty of Electrical Engineering and Computer Science China

ISBN: (纸本)9798331508890

A novel electrochemical aptasensor was designed for the simultaneous detection of aflatoxin B1 (AFB1) and deoxynivalenol (DON) using dual-working microelectrodes and PDMS-based microfluidic channels. The system provides high sensitivity, with detection limits of 7.74 pg/mL for AFB1 and 0.172 ng/mL for DON across broad ranges (0.03 - 300 ng/mL for AFB1, 0.3 - 3000 ng/mL for DON). Results can be obtained within 20 minutes, and the platform exhibits excellent reproducibility, selectivity, and stability. Validation with corn extract highlights its potential for on-site food safety monitoring. © 2025 IEEE.

关键词： Aflatoxins

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Beyond p-y method:A review of artificial intelligence approaches for predicting lateral capacity of drilled shafts in clayey soils

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Journal of Rock Mechanics and Geotechnical engineering 2024年第9期16卷 3812-3840页

作者： M.E.Al-Atroush A.E.Aboelela Ezz El-Din Hemdan Civil and Environmental Engineering Program Engineering Management DepartmentCollege of EngineeringPrince Sultan UniversityRiyadhSaudi Arabia Department of Structural Engineering Faculty of EngineeringAin Shams UniversityCairoEgypt Structure and Materials Research Lab Prince Sultan UniversityRiyadhSaudi Arabia Computer Science and Engineering Department Faculty of Electronic EngineeringMenoufia UniversityMenoufiaEgypt

In 2023,pivotal advancements in artificial intelligence(AI)have significantly *** that in mind,traditional methodologies,notably the p-y approach,have struggled to accurately model the complex,nonlinear soil-structure interactions of laterally loaded large-diameter drilled *** study undertakes a rigorous evaluation of machine learning(ML)and deep learning(DL)techniques,offering a comprehensive review of their application in addressing this geotechnical challenge.A thorough review and comparative analysis have been carried out to investigate various AI models such as artificial neural networks(ANNs),relevance vector machines(RVMs),and least squares support vector machines(LSSVMs).It was found that despite ML approaches outperforming classic methods in predicting the lateral behavior of piles,their‘black box'nature and reliance only on a data-driven approach made their results showcase statistical robustness rather than clear geotechnical insights,a fact underscored by the mathematical equations derived from these ***,the research identified a gap in the availability of drilled shaft datasets,limiting the extendibility of current findings to large-diameter *** extensive dataset,compiled from a series of lateral loading tests on free-head drilled shaft with varying properties and geometries,was introduced to bridge this *** paper concluded with a direction for future research,proposes the integration of physics-informed neural networks(PINNs),combining data-driven models with fundamental geotechnical principles to improve both the interpretability and predictive accuracy of AI applications in geotechnical engineering,marking a novel contribution to the field.

关键词： Laterally loaded drilled shaft load transfer and failure mechanisms Physics-informed neural networks(PINNs) P-y curves Artificial intelligence(AI) Dataset

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