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arXiv 2024年

作者： Gheorghiu, Adrian Tăiatu, Iulian-Marius Cercel, Dumitru-Clementin Marin, Iuliana Pop, Florin Computer Science and Engineering Department National University of Science and Technology POLITEHNICA Bucharest Romania Faculty of Engineering in Foreign Languages National University of Science and Technology POLITEHNICA Bucharest Romania National Institute for Research and Development in Informatics - ICI Bucharest Romania Academy of Romanian Scientists Bucharest Romania

The detection and classification of diseases in Robusta coffee leaves are essential to ensure that plants are healthy and the crop yield is kept high. However, this job requires extensive botanical knowledge and much wasted time. Therefore, this task and others similar to it have been extensively researched subjects in image classification. Regarding leaf disease classification, most approaches have used the more popular PlantVillage dataset while completely disregarding other datasets, like the Robusta Coffee Leaf (RoCoLe) dataset. As the RoCoLe dataset is imbalanced and does not have many samples, fine-tuning of pre-trained models and multiple augmentation techniques need to be used. The current paper uses the RoCoLe dataset and approaches based on deep learning for classifying coffee leaf diseases from images, incorporating the pix2pix model for segmentation and cycle-generative adversarial network (CycleGAN) for augmentation. Our study demonstrates the effectiveness of Transformer-based models, online augmentations, and CycleGAN augmentation in improving leaf disease classification. While synthetic data has limitations, it complements real data, enhancing model performance. These findings contribute to developing robust techniques for plant disease detection and classification. © 2024, CC BY-NC-SA.

关键词： Image classification

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Explainability-Driven Leaf Disease Classification using Adversarial Training and Knowledge Distillation

arXiv

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arXiv 2023年

作者： Echim, Sebastian-Vasile Tăiatu, Iulian-Marius Cercel, Dumitru-Clementin Pop, Florin Computer Science and Engineering Department National University of Science and Technology POLITEHNICA Bucharest Romania National Institute for Research and Development in Informatics - ICI Bucharest Romania Academy of Romanian Scientists Bucharest Romania

This work focuses on plant leaf disease classification and explores three crucial aspects: adversarial training, model explainability, and model compression. The models’ robustness against adversarial attacks is enhanced through adversarial training, ensuring accurate classification even in the presence of threats. Leveraging explainability techniques, we gain insights into the model’s decision-making process, improving trust and transparency. Additionally, we explore model compression techniques to optimize computational efficiency while maintaining classification performance. Through our experiments, we determine that on a benchmark dataset, the robustness can be the price of the classification accuracy with performance reductions of 3%-20% for regular tests and gains of 50%-70% for adversarial attack tests. We also demonstrate that a student model can be 15-25 times more computationally efficient for a slight performance reduction, distilling the knowledge of more complex models. © 2023, CC BY-NC-SA.

关键词： Distillation

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可信联邦学习进化优化算法综述

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engineering 2024年第3期34卷 23-42页

作者： Qiqi Liu Yuping Yan Yaochu Jin Xilu Wang Peter Ligeti Guo Yu Xueming Yan School of Engineering Westlake UniversityHangzhou 310030China Faculty of Informatics Department of Computer AlgebraEötvös Loránd UniversityBudapest 1053Hungary Faculty of Technology Bielefeld UniversityBielefeld 33619Germany Institute of Intelligent Manufacturing Nanjing Tech UniversityNanjing 211816China School of Information Science and Technology Guangdong University of Foreign StudiesGuangzhou 510006China

With the development of edge devices and cloud computing,the question of how to accomplish machine learning and optimization tasks in a privacy-preserving and secure way has attracted increased attention over the past *** a privacy-preserving distributed machine learning method,federated learning(FL)has become popular in the last few ***,the data privacy issue also occurs when solving optimization problems,which has received little attention so *** survey paper is concerned with privacy-preserving optimization,with a focus on privacy-preserving data-driven evolutionary *** aims to provide a roadmap from secure privacy-preserving learning to secure privacy-preserving optimization by summarizing security mechanisms and privacy-preserving approaches that can be employed in machine learning and *** provide a formal definition of security and privacy in learning,followed by a comprehensive review of FL schemes and cryptographic privacy-preserving ***,we present ideas on the emerging area of privacy-preserving optimization,ranging from privacy-preserving distributed optimization to privacy-preserving evolutionary optimization and privacy-preserving Bayesian optimization(BO).We further provide a thorough security analysis of BO and evolutionary optimization methods from the perspective of inferring attacks and active *** the basis of the above,an in-depth discussion is given to analyze what FL and distributed optimization strategies can be used for the design of federated optimization and what additional requirements are needed for achieving these ***,we conclude the survey by outlining open questions and remaining challenges in federated data-driven *** hope this survey can provide insights into the relationship between FL and federated optimization and will promote research interest in secure federated optimization.

关键词： Federated learning Privacy-preservation Security Evolutionary optimization Data-driven optimization Bayesian optimization

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Efficient Resource Management in IoTs Using Evolutionary and Swarm Intelligence Algorithms

Efficient Resource Management in IoTs Using Evolutionary and...

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Innovative Approaches in Smart Technologies (ISAS), International Symposium on

作者： Keshav Dev Gupta Korhan Cengiz Charu Awasthi Siva Akash Ramini Shyamapriya Chatterjee M Pavithra Department of Computer Science Engineering Poornima College of Engineering jaipur India College of Information Technology University of Fujairah UAE Department of Computer Engineering Istinye University Istanbul Turkey Department of Information Technologies Faculty of Informatics and Management University of Hradec Kralove Kralove Czech Republic Department of information technology JSS academy of Technical Education Noida India Department of School of Computer Science and Engineering (SCOPE) VIT-AP University Amaravati Andhra Pradesh Department of Information Technology Narula Institute of Technology Kolkata West Bengal Department of Computer Science and Engineering Jansons Institute of Technology Karumathampatti Coimbatore

The proliferation of the Internet of Things (IoT) has ushered in a transformative era of connected devices, emphasizing the critical need for effective resource management. This study introduces an innovative approach that harnesses Evolutionary and Swarm Intelligence algorithms for IoT Task Scheduling, addressing resource optimization challenges. The approach offers superior resource utilization through advanced optimization techniques, reducing energy consumption and enhancing efficiency. Furthermore, it significantly reduces task scheduling execution time, enabling prompt decision-making in dynamic IoT environments. This results in improved task allocation quality, meeting application-specific requirements, and prioritizing critical tasks. Notably, the approach excels in minimizing completion time, making it particularly advantageous for real-time IoT applications. This research contributes to the advancement of IoT resource management, offering an adaptive, efficient, and intelligent solution with broad applicability. In the evolving landscape of IoT, the findings presented here lay a solid foundation for future research and practical implementations, ultimately promoting more responsive, sustainable, and intelligent IoT ecosystems.

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Interpreting Industrial IoT Data Streams Through Fuzzy Querying With Hysteretic Fuzzy Sets on Apache Kafka

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IEEE Transactions on Fuzzy Systems 2024年第8期32卷 4671-4684页

作者： Bożena Małysiak-Mrozek Bartłomiej Ryba Marek Molęda Che-Lun Hung Witold Pedrycz Weiping Ding Dariusz Mrozek Department of Distributed Systems and Informatic Devices Silesian University of Technology Gliwice Poland Department of Applied Informatics Silesian University of Technology Gliwice Poland Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei City Taiwan Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada Department of Measurements and Control Systems Silesian University of Technology Gliwice Poland Research Center of Performance and Productivity Analysis Istinye University Istanbul Türkiye School of Artificial Intelligence and Computer Science Nantong University Nantong China Faculty of Data Science City University of Macau Macau China

In industrial settings, querying data streams from Internet of Things (IoT) devices benefits from utilizing elastic criteria to enhance the interpretability of the current state of the monitored environment. Fuzzy sets provide this elasticity, enabling the aggregation and representation of similar values in a human-comprehensible manner. However, many sensor signals exhibit temporal oscillations, leading to varying interpretations of the signal based on its current trend (rising or falling). This hysteresis in signal (and subsequently of the production device) interpretation inspired us to introduce this phenomenon into data stream processing, resulting in the novel concept of hysteretic fuzzy sets. This article demonstrates how fuzzy searching and grouping can be applied to IoT sensor signals in flexible Big Data stream processing on Apache Kafka. We illustrate the impact of data stream querying with KSQL queries involving fuzzy sets (encompassing fuzzy filtering of data stream events, fuzzy transformation of data stream attributes, fuzzy grouping, and joining) on the flexibility of executed operations and computational resources utilized by the Kafka processing engine. Finally, our experiments with hysteretic fuzzy sets while analyzing sensor signals in power plants demonstrate that this novel approach effectively reduces the number of alarms while monitoring the state of the production machine.

关键词： Streams Fuzzy sets Internet of Things Big Data Monitoring Production Real-time systems

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Enhanced Student Stress Prediction in Higher Education Using a TriBranch CNN Framework

Enhanced Student Stress Prediction in Higher Education Using...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Fateh Bahadur Kunwar Satyajee Srivastava R Dhivya Monika Gulati K. Amudha D Antony Arul Raj Department of Computer Science and Engineering Geetanjali Institute of Technical Studies Udaipur India Department of Computer Science and Engineering School of Engineering and Technology Manav Rachna International Institute of Research and Studies Faridabad India Department of Information Technology M.Kumarasamy College of Engineering Karur India MMIM Maharishi Markandeshwar (Deemed to be University) Mullana Ambala India Department of Electronics and Communication Engineering Kongunadu College of Engineering and Technology Trichy India Department of Software Systems PSG College of Arts & Science Coimbatore India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Students' stress levels have a significant impact on their academic performance in higher education, thus it's important to identify signs of stress early on and intervene to improve academic performance. Machine learning has evolved into a powerful tool for stress prediction, allowing for proactive support of at-risk youngsters. In order to better anticipate college students' stress levels, this study presents an enhanced TriBranch CNN framework. In order to provide the best possible performance, the technique includes data preparation, feature extraction, and model training. The dimensionality and redundancy are reduced using Principal Component Analysis (PCA). After being trained and tested against other models, the Fully Conditional Temporal Bayesian Network (Fully CTBN) outperformed them all with an accuracy of 93.29%, beating both traditional CNN and FCN approaches. These findings demonstrate that the Fully CTBN approach is useful for identifying students who may be struggling academically and providing schools with the information they need to implement data-driven interventions that boost student well-being and performance.

关键词： Training Accuracy Redundancy Human factors Machine learning Mental health Convolutional neural networks Integrated circuit modeling Stress Principal component analysis

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Deep Convolutional Neural Network Approach for COVID-19 Detection

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computer Systems Science & engineering 2022年第7期42卷 201-211页

作者： Yu Xue Bernard-Marie Onzo Romany F.Mansour Shoubao Su School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China Engineering Research Center of Digital Forensics Ministry of EducationNanjing University of Information Science and TechnologyNanjingChina Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharja72511Egypt Jiangsu Key Laboratory of Data Science and Smart Software Jinling Institute of TechnologyNanjing211169China

Coronavirus disease 2019(Covid-19)is a life-threatening infectious disease caused by a newly discovered strain of the *** by the end of 2020,Covid-19 is still not fully understood,but like other similar viruses,the main mode of transmission or spread is believed to be through droplets from coughs and sneezes of infected *** accurate detection of Covid-19 cases poses some questions to scientists and *** two main kinds of tests available for Covid-19 are viral tests,which tells you whether you are currently infected and antibody test,which tells if you had been infected ***-tine Covid-19 test can take up to 2 days to complete;in reducing chances of false negative results,serial testing is *** image processing by means of using Chest X-ray images and Computed Tomography(CT)can help radiologists detect the *** imaging approach can detect certain characteristic changes in the lung associated with *** this paper,a deep learning model or tech-nique based on the Convolutional Neural Network is proposed to improve the accuracy and precisely detect Covid-19 from Chest Xray scans by identifying structural abnormalities in scans or X-ray *** entire model proposed is categorized into three stages:dataset,data pre-processing andﬁnal stage being training and classiﬁcation.

关键词： COVID-19 deep learning convolutional neural network X-ray

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IoT Based Alcohol Detection and Vehicle Control System

IoT Based Alcohol Detection and Vehicle Control System

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Data Intelligence and Cognitive informatics (ICDICI), International Conference on

作者： I. Sapthami V V Narasimha Raju V. Vaithianathan P. Chinnasamy G Kumaran Ramesh Kumar Ayyasamy Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India Marri Laxman Reddy Institute of Technology and Management Dundigal Hyderabad Telangana India Department of Electronics and Communication Engineering Sri Sivasubramaniya Nadar College of Engineering Kalavakkam Chennai Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Srivilliputtur TamilNadu Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamilnadu India Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman (UTAR) Kampar Malaysia

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389616

It is of the utmost importance to be aware of safety measures when operating a motor vehicle in the contemporary world, characterized by a fast-paced and hazardous environment. It has become necessary to develop innovative technology in order to improve road safety as a result of the alarmingly high number of incidents that can be linked to drivers who are under the influence of alcohol. As a solution to this significant problem, this study uses the capabilities offered by the Internet of Things (IoT) to develop an integrated system for detecting and controlling alcohol in commercial vehicles. This study aims to reduce the dangers connected with driving under the influence of alcohol by allowing for the seamless integration of cutting-edge alcohol detection technologies with real-time vehicle control mechanisms. The system makes use of specific sensors for the detection of alcohol. It interacts with the control mechanisms of the vehicle in order to restrict its functioning when alcohol is present. To make this possible, the hardware component of the system mounted in the vehicle uses the MQ3 sensor, also known as the alcohol sensor. This sensor is responsible for monitoring the alcohol molecules in the air around the driver to determine whether or not the driver is intoxicated. A buzzer, a DC motor, an LCD, a GPS, and a GSM are all components utilized by the system for data transfer. Additionally, the gear is powered by a 12-volt battery. As soon as the system determines that there is alcohol present in the vehicle, it immediately locks the accelerator. Using the GPS, the driver can obtain their location coordinates.

关键词： Refining Control systems Sensor systems Road safety Sensors Safety Internet of Things Reliability Vehicles Global Positioning System

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Integrative multi-platform meta-analysis of hepatocellular carcinoma gene expression profiles for identifying prognostic and diagnostic biomarkers

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Genes & Diseases 2023年第4期10卷 1194-1196页

作者： Maryam Gholizadeh Morteza Hadizadeh Seyed Reza Mazlooman Saeid Eslami Sadegh Raoufi Marziye Farsimadan Maryam Rashidifar Marek Drozdzik Mehrnaz Mehrabani Department of Medical Informatics Faculty of MedicineMashhad University of Medical SciencesMashhad 91388-13944Iran Physiology Research Center Institute of NeuropharmacologyKerman University of Medical SciencesKerman ***Iran Department of Computer Engineering Central Tehran BranchIslamic Azad UniversityTehran ***Iran Modeling in Health Research Center Institute for Futures Studies in HealthKerman University of Medical SciencesKerman ***Iran Department of Biology Faculty of SciencesUniversity of GuilanRasht 13111-41937Iran Department of Plant Biotechnology Faculty of Life Science and BiotechnologyShahid Beheshti UniversityTehran 198396-3113Iran Department of Experimental and Clinical Pharmacology Pomeranian Medical UniversitySzczecin 70-111Poland

Hepatocellular carcinoma(HCC),as one of the most com-mon types of primary liver cancer(PLV),accounts for approximately 75%e90%of all PLV cases identified worldwide.^(1)Currently,the most effective treatment for HCC patients constitutes liver ***,due to high rates of recurrence a poor prognosis is predicted.^(2)Therefore,accurate HCC biomarkers are ur-gently needed to develop innovative *** of earlier investigations to identify biomarkers have been severely limited by sample size.

关键词： liver prognosis hepatocellular

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Cooperative Plug-and-Play-KalmanNet for 4D situational awareness in autonomous driving

Cooperative Plug-and-Play-KalmanNet for 4D situational aware...

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Industrial Cyber-Physical Systems (ICPS)

作者： Nikos Piperigkos Alexandros Gkillas Christos Anagnostopoulos Aris S. Lalos Computer Engineering & Informatics Department University of Patras Greece AviSense.AI Patras Science Park Greece Athena Research Center Industrial Systems Institute Patras Science Park Greece

ISBN: (数字)9798350363012

ISBN: (纸本)9798350363029

Fully operational autonomous driving requires accurate 4D situational awareness from vehicles, i.e., 3D location information over time. For that purpose, each vehicle establishes connectivity links with its direct neighboring vehicles, collects, exchanges and fuses multi-modal measurements in order to increase the accuracy of localization information. In this work, we focus on efficiently tracking the unknown system dynamics, and as a matter of fact boosting the performance of 4D situational awareness, without knowing beforehand the statistics of sensor measurement noise. More specifically, we exploit the so-called KalmanNet, a recurrent neural network, which has been explicitly designed to estimate the covariance and Kalman gain matrices of standard Extended Kalman Filter through a typical deep learning approach. Pre-trained KalmanNet is then used as the core module of a cooperative localization approach which effectively realizes dynamic 4D situational awareness and improves sensor fusion performance. An extensive evaluation study in CARLA autonomous driving simulator demonstrates that our cooperative plug-and-play approach significantly improves localization accuracy, as well as reduces drift error which is a critical error metric in the automotive industry.

关键词： Location awareness Solid modeling Accuracy Uncertainty Three-dimensional displays System dynamics Kalman filters

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