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Nondestructive Chicken Egg Fertility Detection Using CNN-Transfer Learning Algorithms

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arXiv 2023年

作者： Saifullah, Shoffan Drezewski, Rafal Yudhana, Anton Pranolo, Andri Kaswijanti, Wilis Suryotomo, Andiko Putro Putra, Seno Aji Khaliduzzaman, Alin Prabuwono, Anton Satria Japkowicz, Nathalie Institute of Computer Science AGH University of Krakow Krakow30-059 Poland Department of Informatics Universitas Pembangunan Nasional Veteran Yogyakarta Yogyakarta55281 Indonesia Center of Knowledge Engineering and Data Science Department of Electrical Engineering and Informatics Universitas Negeri Malang Malang65145 Indonesia Department of Electrical Engineering Universitas Ahmad Dahlan Yogyakarta55191 Indonesia College of Computer and Information Hohai University Nanjing211100 China Department of Informatics Faculty of Industrial Technology Universitas Ahmad Dahlan Yogyakarta55166 Indonesia Faculty of Agricultural Engineering and Technology Sylhet Agricultural University Sylhet3100 Bangladesh Faculty of Computing and Information Technology in Rabigh King Abdulaziz University Jeddah21589 Saudi Arabia Department of Computer Science American University WashingtonDC20016 United States

This study explored the application of CNN-Transfer Learning for nondestructive chicken egg fertility detection for precise poultry hatchery practices. Four models, VGG16, ResNet50, InceptionNet, and MobileNet, were trained and evaluated on a dataset (200 single egg images) using augmented images (rotation, flip, scale, translation, and reflection). The training results demonstrated that all models achieved high accuracy, indicating their ability to learn and classify chicken eggs’ fertility. However, variations in accuracy and performance were observed when these models were evaluated on the testing datasets. The InceptionNet exhibited the best overall performance, accurately classifying fertile and non-fertile eggs. It demonstrated excellent performance in all parameters of the evaluation metrics for both training and testing datasets. When evaluated on the testing datasets, it achieved an accuracy of 0.98, a sensitivity of 1 for detecting fertile eggs, and a specificity of 0.96 for identifying non-fertile eggs. The higher performance is attributed to its unique architecture, efficiently capturing features at different scales, which leads to improved accuracy and robustness. Further optimization and fine-tuning of the models might be necessary to address the limitations in accurately detecting fertile and non-fertile eggs using other models. This study highlighted the potential of CNN-transfer learning for nondestructive fertility detection and emphasized the need for further research to enhance the models’ capabilities and to ensure accurate *** Codes CS-Class: 68T07, 68T45, 68U10, ICT-Class: 94A08 © 2023, CC BY-SA.

关键词： Image classification

Digital Twin-Driven Intrusion Detection for IoT and Cyber-Physical System

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Digital Twin-Driven Intrusion Detection for IoT and Cyber-Ph...

Intelligent Algorithms for Computational Intelligence systems (IACIS), International Conference on

作者： Saravanan Kandaneri Ramamoorthy L. Sindhu K. Valarmathi Gobinath C P. Umaeswari College of Non-Medicine University: Texila American University Guyana South America Department of Information Technology Sona College of Technology Salem India Department of Information Technology S. A. Engineering College Chennai India School of Computing and Information Technology REVA University Bengaluru India Department of Computer Science and Business Systems R.M.K. Engineering College Kavaraipettai India

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

From smart homes to industrial automation, the efficiency and connectivity of many applications have been greatly improved by the fast expansion of the Internet of Things (IoT) and Cyber-Physical systems (CPS). Sophisticated cyberattacks are becoming more common in this networked ecosystem, thus strong security measures are needed. In order to protect CPS and IoT settings, this study presents a new intrusion detection method that makes use of Digital Twin technology. To build an adaptable and dynamic Intrusion Detection System (IDS), our suggested architecture combines Digital Twins with state-of-the-art machine learning methods. In order to accurately detect and categorize any threats, the IDS constantly learns from data in both the past and the present. When tested on benchmark datasets and in real-world Internet of Things (IoT) settings, the framework outperforms conventional intrusion detection system (IDS) solutions in terms of detection accuracy and false alarm rate reduction. To protect IoT and CPS infrastructures from ever-changing cyber threats, this scalable and resilient security solution is offered by this IDS that is powered by digital twins. We present ICNN-FCID, an Integrated Convolutional Neural Network for Fog Computing Environment, a hybrid deep learning intrusion detection model for fog computing environments.

关键词： Deep learning Computational modeling Intrusion detection Digital twins Internet of Things Convolutional neural networks Synchronization Security Long short term memory Edge computing

Federated Graph Analytics with Differential Privacy

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arXiv 2024年

作者： Liu, Shang Cao, Yang Murakami, Takao Liu, Weiran Liew, Seng Pei Takahashi, Tsubasa Liu, Jinfei Yoshikawa, Masatoshi Graduate School of Informatics Kyoto University Kyoto606-8501 Japan Department of Computer Science Tokyo Institute of Technology Tokyo152-8552 Japan Department of Interdisciplinary Statistical Mathematics Institute of Statistical Mathematics Tokyo190-8562 Japan Alibaba Group Beijing100102 China LY Corporation Tokyo160-0004 Japan College of Computer Science and Technology Zhejiang University Hangzhou310027 China Osaka Seikei University Osaka533-0007 Japan

Collaborative graph analysis across multiple institutions is becoming increasingly popular. Realistic examples include social network analysis across various social platforms, financial transaction analysis across multiple banks, and analyzing the transmission of infectious diseases across multiple hospitals. We define the federated graph analytics, a new problem for collaborative graph analytics under differential privacy. Although differentially private graph analysis has been widely studied, it fails to achieve a good tradeoff between utility and privacy in federated scenarios, due to the limited view of local clients and overlapping information across multiple subgraphs. Motivated by this, we first propose a federated graph analytic framework, named FEAT, which enables arbitrary downstream common graph statistics while preserving individual privacy. Furthermore, we introduce an optimized framework based on our proposed degree-based partition algorithm, called FEAT+, which improves the overall utility by leveraging the true local subgraphs. Finally, extensive experiments demonstrate that our FEAT and FEAT+ significantly outperform the baseline approach by approximately one and four orders of magnitude, respectively. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

A Novel Intrusion Detection System for Internet of Healthcare Things Based on Deep Subclasses Dispersion Information

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TechRxiv

TechRxiv 2022年

作者： Fouda, Marwa Ksantini, Riadh Elmedany, Wael Department of Information Systems College of Information Technology University of Bahrain Southern Governorate Sakhir Bahrain Department of Computer Science College of Information Technology University of Bahrain Southern Governorate Sakhir Bahrain Department of Computer Engineering College of Information Technology University of Bahrain Southern Governorate Sakhir Bahrain

Despite the significant benefits that the Internet of Healthcare Things (IoHT) offered to the medical sector, there are concerns and risks regarding these systems which can delay their wide deployment as they handle sensitive, and often life-critical medical information. In addition to the IoHT concerns and risks, there are security constraints which include hardware, software, and network constraints that pose a security challenge to these systems. Therefore, security measures need to be deployed that can overcome the concerns, mitigate the risks, and meet the constraints of the IoHT. For these reasons, a subclasses intrusion detection system for the IoHT is proposed in this research work based on a novel variation of the standard One-class Support Vector Machine (OSVM), namely Deep Subclass Dispersion One-class Support Vector Machine (Deep SDOSVM), which considers subclasses in the target class, i.e. normal class, in order to minimize the data dispersion within and between subclasses, thereby improving the discriminative power and classification performance of the intrusion detection system. A deep clustering model is used for subclasses generation in the proposed Deep SDOSVM approach, namely the Dynamic Autoencoder Model (DynAE), to overcome the drawbacks of the classical clustering algorithms, and further enhance the classification performance of the intrusion detection system. The proposed deep clustering subclasses intrusion detection system was evaluated on the real-world TON_IoT dataset, and compared to other state-of-the-art one-class classifiers. Experimentation results showed that the proposed approach outperformed the other relevant one-class classifiers for network intrusion detection. © 2022, CC BY-NC-SA.

关键词： Intrusion detection

Lung Melanoma Recognition through Platelet Count Composition Using Supervised ML Technique

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Lung Melanoma Recognition through Platelet Count Composition...

Augmented Intelligence and Sustainable systems (ICAISS), International Conference on

作者： H.Llilly Beaulah R Revathi J Deepika A.Sheryl Oliver R. Krishnamoorthy R. Thiyagarajan Computer Science and Engineering Mahendra College of Engineering Salem Department of Computer Science Karpagam Academy Of Higher Education Department of IT Bannari Amman Institute of Technology Sathyamangalam Tamilnadu St.Joseph's College of Engineering Centre for Nonlinear Systems Chennai Institute of Technology Chennai India Department of IT Prathyusha Engineering College Chennai India

ISBN: (纸本)9781665489638

Blood samples are used to diagnose the health illness in the human body. Complete blood routine test is used as whole-body screening to detect illness in human body. Highly sensitive diagnosis can be detected by blood count such as by using RBC, RNA [12], WBC, DNA, platelet count or even protein biomarkers. Biopsies of liquid blood sample are effectively used nowadays even to detect diagnosis of cancer. By determining the routine indices of blood samples, lung carcinoma can be detected. Rather than using PET scan which is high in cost, Lung carcinoma can be detected by blood indices samples. Machine Learning can be used to decipher the multiple ranges of statistical data by using svm (support vector machine) technique from supervised learning framework. This technique can be used for identification purpose both in clinical research and by patients respectively. From the detailed report, physicians can aid the treatment cycle for the patient. Each patient's report is sent to their registered phone number via SMS using Sim900 with Arduino Uno sensor. The generated detailed report is sent to the registered mobile number as short messaging service. In this approach, advanced way of using blood indices to identify the lung carcinoma is been developed.

关键词： Support vector machines Protocols Costs Blood platelets Supervised learning Lung Medical services

Lightweight Deep Learning for Resource-Constrained Environments: A Survey

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arXiv 2024年

作者： Liu, Hou-I Galindo, Marco Xie, Hongxia Wong, Lai-Kuan Shuai, Hong-Han Li, Yung-Yui Cheng, Wen-Huang Department of Electronics and Electrical Engineering National Yang Ming Chiao Tung University Hsinchu300 Taiwan College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130000 China Faculty of Computing and Informatics Multimedia University Cyberjaya63100 Malaysia Hon Hai Research Institute Taipei114 Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei106 Taiwan

Over the past decade, the dominance of deep learning has prevailed across various domains of artificial intelligence, including natural language processing, computer vision, and biomedical signal processing. While there have been remarkable improvements in model accuracy, deploying these models on lightweight devices, such as mobile phones and microcontrollers, is constrained by limited resources. In this survey, we provide comprehensive design guidance tailored for these devices, detailing the meticulous design of lightweight models, compression methods, and hardware acceleration strategies. The principal goal of this work is to explore methods and concepts for getting around hardware constraints without compromising the model's accuracy. Additionally, we explore two notable paths for lightweight deep learning in the future: deployment techniques for TinyML and Large Language Models. Although these paths undoubtedly have potential, they also present significant challenges, encouraging research into unexplored areas. Copyright © 2024, The Authors. All rights reserved.

关键词： computer hardware

Improving Learning Experience of Students by Early Prediction of Student Performance using Machine Learning

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Improving Learning Experience of Students by Early Predictio...

2020 IEEE International Conference for Innovation in Technology, INOCON 2020

作者： Gull, Hina Saqib, Madeeha Iqbal, Sardar Zafar Saeed, Saqib College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University Department of Computer Information Systems Dammam Saudi Arabia

ISBN: (纸本)9781728197449

Early indications regarding students' progress help academics to optimise their learning strategies and focus on diverse educational practices to make the learning experience successfully. Machine learning application can help academics to predict the expected weaknesses in learning processes and as a result they can proactively engage such students in better learning experience. We applied logistic regression, linear discriminant analysis, K-nearest neighbors, classification and regression trees, gaussian Naive Bayes and support vector machines on historical data of student grades in one of the undergraduate courses and developed a model to predict the grades of students taking the same course in the next term. Our experiments show Linear discrimination analysis as the most effective approach to correctly predict the students' performance outcome in final exams. Out of total 54 records, 49 were predicted by model as expected giving 90.74% of accuracy. © 2020 IEEE.

关键词： Students

A Blockchain-Based Cross-Domain DDoS Mitigation in Consumer Networks

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IEEE Transactions on Consumer Electronics 2025年

作者： Moudoud, Hajar Houda, Zakaria Abou El Brik, Bouziane Département d'informatique et d'ingénierie GatineauQCJ8Y 3G5 Canada GatineauQCJ8Y 3G5 Canada Sharjah University College of Computing and Informatics Computer Science Department Sharjah United Arab Emirates

Distributed Denial of Service (DDoS) attacks pose significant threats to the availability and security of consumer networks and Internet service providers (ISPs). This is a significant concern due to the potential vulnerabilities and security risks associated with the rapid increase in the number of insecure Internet of Things (IoT) devices. Adopting an inter-domain DDoS collaboration strategy is a promising solution to address this issue. However, manual configuration and management of resources across multiple domains can be time-consuming, error-prone, and inefficient. Moreover, the existing inter-domain DDoS mitigation mechanisms (i.e., Cooperative Defense mechanisms) are facing obstacles due to the lack of incentives for cooperation, low flexibility, and high cost. Most importantly, many of them are centralized, which risks single points of failure, hampering collaboration and resource sharing among Autonomous systems (ASs). The new emerging techniques, such as Digital-Twin (DT) empowered by Network Function Virtualization (NFV), Software-Defined Networking (SDN), and Blockchain introduce new opportunities for efficient and flexible inter-domain DDoS collaboration i.e., resources sharing among multiple SDN-based domains. In this context, we propose SecureShare, a novel digital twin-enabled inter-domain DDoS mitigation framework that allows for an efficient, fair, and secure dynamic resource-sharing among SDN-based domains to deal with large-scale DDoS attacks through resource sharing. The deployment of SecureShare is executed within Ethereum's test network, Sepolia. Furthermore, we performed extensive experiments employing Microsoft Azure Digital Twins (ADT), a platform-as-a-service tool for generating twin graphs of physical objects. The experimental results show that SecureShare achieves promising results in terms of efficiency, security, and flexibility. © 1975-2011 IEEE.

关键词： Blockchain

Large-scale digital phenotyping: identifying depression and anxiety indicators in a general UK population with over 10,000 participants

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arXiv 2024年

作者： Zhang, Yuezhou Stewart, Callum Ranjan, Yatharth Conde, Pauline Sankesara, Heet Rashid, Zulqarnain Sun, Shaoxiong Dobson, Richard J.B. Folarin, Amos A. Department of Biostatistics & Health Informatics Institute of Psychiatry Psychology and Neuroscience King’s College London United Kingdom Department of Computer Science University of Sheffield Sheffield United Kingdom Institute of Health Informatics University College London United Kingdom NIHR Maudsley Biomedical Research Centre South London and Maudsley NHS Foundation Trust London United Kingdom NIHR Biomedical Research Centre University College London Hospitals NHS Foundation Trust London United Kingdom

Background: Digital phenotyping offers a novel and cost-efficient approach for managing depression and anxiety. Previous studies, often limited to small-to-medium or specific populations, may lack generalizability. Methods: We conducted a cross-sectional analysis of data from 10,129 participants recruited from a UK-based general population between June 2020 and August 2022. Participants shared wearable (Fitbit) data and self-reported questionnaires on depression (PHQ-8), anxiety (GAD-7), and mood via a study app. We first examined the correlations between PHQ-8/GAD-7 scores and wearable-derived features, demographics, health data, and mood assessments. Subsequently, unsupervised clustering was used to identify behavioural patterns associated with depression or anxiety. Finally, we employed separate XGBoost models to predict depression and anxiety and compared the results using different subsets of features. Results: We observed significant associations between the severity of depression and anxiety with several factors, including mood, age, gender, BMI, sleep patterns, physical activity, and heart rate. Clustering analysis revealed that participants simultaneously exhibiting lower physical activity levels and higher heart rates reported more severe symptoms. Prediction models incorporating all types of variables achieved the best performance (R2=0.41, MAE=3.42 for depression;R2=0.31, MAE=3.50 for anxiety) compared to those using subsets of variables. Limitations: Data collection during the COVID-19 pandemic may introduce biases, despite controlling for pandemic-related variables. Conclusion: This study identified potential indicators for depression and anxiety, highlighting the utility of digital phenotyping and machine learning technologies for rapid screening of mental disorders in general populations. These findings provide robust real-world insights for future healthcare applications. Copyright © 2024, The Authors. All rights reserved.

关键词： Electronic health record