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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Putri Ardyanti, Anak Agung Ayu Xie, Zhiqiang Karnyoto, Andrea Stevens Pardamean, Bens Estiyanti, Ni Made School of Computer Science and Technology Harbin University of Science and Technology Harbin150000 China Faculty of Information Technology and Design Primakara University Bali 80226 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia School of Management and Business University of Debrecen Debrecen Hungary

The industry is rapidly transitioning from the 4.0 era to the 5.0 era, prompting renewed interest among scholars in scheduling problems. They allow operations to process and assemble various components simultaneously. This bibliometric analysis provides a comprehensive understanding of the diverse research perspectives on the potential challenges of integrated scheduling within the framework. We identified 357 Scopus articles and analysed co-occurrence keyword (CNK) by VOSViewer tools and Global Citation Score (GCS) by Scopus tools. The research data obtained from 1993 to 2023 mainly in the subject areas of Engineering, computer science, Maths, and others. CNK identified six cluster keywords, and nine most cited articles based on normalised GCS. These results contribute to our understanding of the development of scheduling algorithms, shifting from an emphasis on manufacturing transfers to contemporary demand-centric research directions. Furthermore, this research provides a valuable perspective for policymakers, plant personnel, and manufacturing managers to make informed decisions. © 2025 The Authors. Published by Elsevier B.V.

关键词： Job shop scheduling

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Early Detection and Classification of Tomato Leaf Blight Disease Using Deep Neural Networks 2

Early Detection and Classification of Tomato Leaf Blight Dis...

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2nd IEEE International Conference on data science and Network Security, ICDSNS 2024

作者： Halder, Anangsha Guha Neogi, Subhrendu Dutta, Shibdas Khamaru, Koustov Bhattacharya, Arghya Roy, Shovan The Neotia University Department of Computer Science and Engineering Sarisha India Dcg Data-Core System India Department of Information Technology Kolkata India University of Engineering and Management Department of Computer Science and Engineering Kolkata India

ISBN: (纸本)9798350373110

The world population relies on agricultural plants for food, and illnesses reduce yield, but proper plant disease monitoring can help to resolve such issues. computer vision and machine learning methods can detect plant illnesses early, reducing their negative consequences and overcoming the drawbacks of continual human monitoring. After pre-processing of input images, the target region needs to be isolated. The proposed models analyzes images for detection and classification to identify edges, colors, and textures. This research has agricultural applications for early disease identification. This study examined ResNet, MobileNet, DenseNet, InceptionV3 and modified CNN model on the PlantVillage dataset to classify tomato illnesses. The F1 score, accuracy, sensitivity, and specificity can evaluate models. This paper evaluates these models using a diverse collection of tomato leaf pictures, contributing to automated disease detection systems and may be helpful for improving farm management and sustainability. © 2024 IEEE.

关键词： Fruits

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Revealing Security Flaws in Cross-Modal Retrieval Models through Video Poisoning

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IEEE Transactions on Circuits and Systems for Video Technology 2025年第6期35卷 6184-6194页

作者： Jin, Ming Hu, Wenbo Hong, Richang Zhu, Lei Hefei University of Technology School of Computer and Information Hefei China Data Space Research Institute Hefei Comprehensive National Science Center China Tongji University School of Electronic and Information Engineering Shanghai China

Video-text cross-modal retrieval is widely studied to improve retrieval accuracy. However, the security of video-text cross-modal retrieval models receives little attention. If attackers exploit the security vulnerabilities in these models, it poses a significant threat to the retrieval models. Thus, identifying security flaws in video-text cross-modal retrieval models becomes the focus of our research. We are the first to design a video poisoning model to uncover security vulnerabilities in retrieval models. Existing poisoning models have certain limitations when it comes to exploiting vulnerabilities in retrieval models. These include failing to comprehensively embed malicious information into the original video and being unable to maintain visual consistency between the original and poisoned videos. These limitations can result in unsuccessful attacks on retrieval models and an inability to effectively identify security flaws within them. To address these shortcomings, we design an efficient poisoning model that embeds malicious information thoroughly into the original clean data to attack video-text cross-modal retrieval models. We are the first to use a poisoning model to attack retrieval models, thereby uncovering their security vulnerabilities. Second, we introduce a bi-level poisoning module to ensure that malicious information is thoroughly embedded into the original video, thereby enhancing the attack capability of the poisoning model. Finally, we design an adversarial module to improve visual consistency between the original and poisoned videos, thus enhancing the concealment of malicious information within the training data of retrieval models. Our poisoning model can identify security flaws in video-text cross-modal retrieval models, providing insights into improving the security of retrieval models. The effectiveness of our model is validated on the MSR-VTT, LSMDC, and MSVD datasets. © 1991-2012 IEEE.

关键词： Content based retrieval

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CaFe DBSCAN: A Density-based Clustering Algorithm for Causal Feature Learning 10

CaFe DBSCAN: A Density-based Clustering Algorithm for Causal...

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10th IEEE International Conference on data science and Advanced Analytics, DSAA 2023

作者： Weber, Pascal Miklautz, Lukas Kumar, Akshey Grosse-Wentrup, Moritz Plant, Claudia Doctoral School of the University of Vienna Vienna Austria University of Vienna Data Science@Uni Vienna Faculty of Computer Science Vienna Austria

ISBN: (纸本)9798350345032

Causal Feature Learning (CFL) infers macro-level causes (e.g., an aggregation of pixels in a traffic light image) from micro-level data (e.g., pixels of the image) by clustering the predicted probabilities of effect states (e.g., state of the traffic light). The current method for CFL uses a two-step procedure. First, a classifier for the effect states is trained, and afterwards, the predicted effect state probabilities are clustered. With CaFe DBSCAN, we present a novel density-based clustering method that conducts CFL directly by estimating conditional probabilities during clustering. To this end, we introduce the notion of clustering regions with similar conditional probabilities of the effect states given their micro-level data points. Our single-step approach has the following benefits: (1) CaFe DBSCAN introduces a comprehensive approach to Causal Feature Learning. Unlike existing methods, CaFe DBSCAN uses a probabilistic framework and does not require separate classification and clustering steps implemented by different algorithms relying on various assumptions, parameter settings, and optimization goals. (2) We do not need to train and tune a classifier first, hence the algorithm is more runtime-efficient than the current approach. (3) Due to the properties of density-based clustering algorithms, CaFe DBSCAN is robust against noise and outliers, which leads to purer clusters. (4) Our algorithm automatically infers a reasonable number of clusters, i.e., macro-level causes. We demonstrate the benefits of CaFe DBSCAN on synthetic and real-world data. © 2023 IEEE.

关键词： Clustering algorithms

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Machine Learning for Mental Health: A Systematic Study of Seven Approaches for Detecting Mental Disorders

Machine Learning for Mental Health: A Systematic Study of Se...

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2023 International Technical Conference on Circuits/Systems, computers, and Communications, ITC-CSCC 2023

作者： Nadeem, Muhammad Rashid, Junaid Moon, Hyeonjoon Dosset, Arailym Sejong Uviversity Department of Computer Science and Engineering Seoul Korea Republic of Sejong University Department of Data Science Seoul Korea Republic of

ISBN: (纸本)9798350326413

Mental disorders are a prevalent issue among teenagers. The widespread use of smartphones and social media has revolutionized the way individuals communicate and exchange information with millions of people using these technologies every day. As a result, vast amounts of data are generated, which can be harnessed to improve mental health detection. The increasing prevalence of mental health issues and the demand for quality healthcare services have led to research exploring the potential of machine learning (ML) to address these challenges. This paper provides a systematic study of seven ML approaches used in previous studies to detect mental disorders. The study examines the datasets employed, the accuracy achieved, and the limitations of each ML approach. The seven ML approaches studied in this paper are Support Vector Machine (SVM), Least Absolute Shrinkage and Selection Operator (LASSO), Long Short-Term Memory (LSTM), Random Forest (RF), Logistic Regression (LR), Artificial Neural Networks (ANN), and eXtreme Gradient Boosting (XGBoost). These approaches have been utilized in various studies to detect mental disorders and this paper aims to provide a comprehensive understanding of their effectiveness. The findings indicate that machine learning approaches have demonstrated significant potential for the detection of mental disorders, with promising implications for enhancing healthcare services. Additionally, the paper discusses the open research challenges and future directions for mental health. © 2023 IEEE.

关键词： Support vector machines

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Machine Learning Diagnosis of Lumpy Skin Disease in Cattle Herds 3

Machine Learning Diagnosis of Lumpy Skin Disease in Cattle H...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Krishna, Nimmagadda Kumar Benjamin, Ruth Moly Tejaswi, Nimmagadda Institute of Technology and Sciences Division of Computer Science and Engineering Karunya Coimbatore India Shri Vishnu Engineering College for Women Division of Artificial Intelligence and Data Science Bhimavaram India

ISBN: (纸本)9798350375190

In order to diagnose lumpy skin disease in cattle herds, machine learning techniques such as Support Vector Machine (SVM), Gradient Boosting, and Random Forest algorithms were used in this research work. The objective of the study is to create a diagnostic tool that would be both accurate and effective to identify and treat this infectious disease early on. The algorithms were trained to classify cases with high accuracy using a dataset that included different clinical and demographic characteristics related with the presence or absence of Lumpy Skin Disease. SVM is used because of its capacity to manage intricate and non-linear correlations in the data, and Random Forest and Gradient Boosting algorithms were chosen due to their predictive performance. © 2024 IEEE.

关键词： Support vector machines

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Special Topic on Reinforcement Learning and Intelligent Decision

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ZTE Communications 2023年第3期21卷 1-2页

作者： GAO Yang Department of Computer Science and Technology Nanjing University National Institute of Healthcare Big Data at Nanjing University

Over the past few years,deep reinforcement learning(RL)has made remarkable progress in a range of applications,including Go games,vision-based control,and generative dialogue *** error-and-trial mechanisms,deep RL enables data-driven optimization and sequential decision-making in uncertain *** to traditional programming or heuristic optimization methods,deep RL can elegantly balance exploration and exploitation and handle environmental *** a result,this learning paradigm has attracted increasing attention from both academia and industry and is paving a new path for largescale complex decision-making applications.

关键词： handle optimization programming

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A multi-agents visual federated learning framework for underground coal mines 24

A multi-agents visual federated learning framework for under...

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7th International Conference on Signal Processing and Machine Learning, SPML 2024

作者： Ji, Yucheng Yuan, Hongyong Liu, Yongqiang Fang, Yudong Chen, Wei Ministry of Emergency Management Big Data Center China School of Safety Science Tsinghua University China School of Computer Science and Technology China University of Mining and Technology China

ISBN: (纸本)9798400717192

The video safety monitoring and analysis is a critical problem in underground coal mines. Due to the complex environment in the underground coal mine and the requirement of perception and decision-making abilities from the coal mine robots, single intelligent agent(robot) is difficult to complete complicated tasks. This paper proposes a framework of federated learning for multiple robots(agents) in underground coal mines to achieve video monitor and analysis tasks. Three federated learning models are raised include asynchronous federation optimization algorithm;visual federation method for heterogeneous image data and coal mine video equipment federated model aggregation. The visual federated learning framework can significantly improve the safety monitoring, accelerate the development of automation in the coal mine production field, and constructing and improving smart mines. © 2024 ACM.

关键词： Risk management

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Trace-based Multi-Dimensional Root Cause Localization of Performance Issues in Microservice Systems 24

Trace-based Multi-Dimensional Root Cause Localization of Per...

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44th ACM/IEEE International Conference on Software Engineering, ICSE 2024

作者： Zhang, Chenxi Dong, Zhen Peng, Xin Zhang, Bicheng Chen, Miao Fudan University China Shanghai Collaborative Innovation Center of Intelligent Visual Computing China School of Computer Science and Shanghai Key Laboratory of Data Science Fudan University China

ISBN: (纸本)9798400702174

Modern microservice systems have become increasingly complicated due to the dynamic and complex interactions and runtime environment. It leads to the system vulnerable to performance issues caused by a variety of reasons, such as the runtime environments, communications, coordinations, or implementations of services. Traces record the detailed execution process of a request through the system and have been widely used in performance issues diagnosis in microservice systems. By identifying the execution processes and attribute value combinations that are common in anomalous traces but rare in normal traces, engineers may localize the root cause of a performance issue into a smaller scope. However, due to the complex structure of traces and the large number of attribute combinations, it is challenging to find the root cause from the huge search space. In this paper, we propose TraceContrast, a trace-based multidimensional root cause localization approach. TraceContrast uses a sequence representation to describe the complex structure of a trace with attributes of each span. Based on the representation, it combines contrast sequential pattern mining and spectrum analysis to localize multidimensional root causes efficiently. Experimental studies on a widely used microservice benchmark show that TraceContrast outperforms existing approaches in both multidimensional and instance-dimensional root cause localization with significant accuracy advantages. Moreover, Trace-Contrast is efficient and its efficiency can be further improved by parallel execution. © 2024 ACM.

关键词： Spectrum analysis

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Exploring Recurrent Neural Network Models for Depression Detection Through Facial Expressions: A Systematic Literature Review 6

Exploring Recurrent Neural Network Models for Depression Det...

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6th International Conference on computer and Informatics Engineering, IC2IE 2023

作者： Rumahorbo, Brilyan Nathanael Pardamean, Bens Elwirehardja, Gregorius Natanael Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia

ISBN: (纸本)9798350345162

Major Depressive Disorder (MDD) is a prevalent mental disorder, affecting a significant number of individuals, with estimates reaching 300 million cases worldwide. Currently, the diagnosis of this condition relies heavily on subjective assessments based on the experience of medical professionals. Therefore, researchers have turned to deep learning models to explore the detection of depression. The objective of this review is to gather information on detecting depression based on facial expressions in videos using deep learning techniques. Overall, this research found that RNN models achieved 7.22 MAE for AVEC2014. LSTM models produced 4.83 MAE for DAIC-WOZ, while GRU models achieved an accuracy of 89.77% for DAIC-WOZ. Features like Facial Action Units (FAU), eye gaze, and landmarks show great potential and need to be further analyzed to improve results. Analysis can include applying feature engineering techniques. Aggregation methods, such as mean calculation, are recommended as effective approaches for data processing. This Systematic Literature Review found that facial expressions do have relevant patterns related to MDD. © 2023 IEEE.

关键词： deep learning facial expression GRU LSTM major depressive disorder

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