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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Pernando, Yonky Gaol, Ford Lumban Soeparno, Haryono Arifin, Yulyani Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798331539603

Shipbuilder safety and performance, structural integrity of welded joints in shipbuilding is of utmost importance. Traditional manual inspection methods to detect any potential defect can be time consuming and susceptible to human errors;alternative inspection systems have proven more cost effective and accurate solutions than manual approaches for defect identification. This paper investigates how advanced software quality assessment standards and machine learning techniques such as Convolutional Neural Networks (CNNs) and You Only Look Once (YOLO) object identification framework can enhance weld defect detection. Research undertaken for this thesis addresses three core questions. 1) Which standards provide effective software quality assessments used for weld defect detection systems?;and 2) In what ways will they impact efficiency and reliability. How will implementation of these standards increase accuracy in manufacturing processes? Integrating high-quality annotated datasets, optimizing joint preparation and parameter settings, and using cloud solutions for computing needs can enable industries to achieve more reliable, efficient, and scalable defect detection. This proactive approach not only enhances immediate product quality, but also contributes to long-term durability and performance, decreasing maintenance costs and increasing overall reliability for steel plates used for marine applications. Furthermore, evidence has revealed that adopting software quality assessment standards in weld defect detection significantly enhances structural integrity as well as operational efficiency of shipbuilding operations. © 2024 IEEE.

关键词： Convolutional neural networks

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Sign Comm: A Real-Time Indian Sign Language Recognition System Using Deep Learning for inclusive communication 2

Sign Comm: A Real-Time Indian Sign Language Recognition Syst...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Ilakkia, A. Kanchanadevi, P. Abirami, A. Rajan, R. Muthukumaran, K. Artificial Intelligence and Data Science Sri Manakula Vinayagar Engineering Puducherry India St. Joseph College of Arts and Science Department of Computer Science Cuddalore India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534950

Individuals who are deaf often utilize sign language for communication. In Sign Language, the symbols or motions are arranged grammatically. Every motion is referred to as a sign. Indian Sign Language Recognition using Deep Learning model describes the creation of SignComm, which improves communication for those with hearing loss by translating Indian Sign Language (ISL) motions into text. To identify and interpret ISL gestures in real-time. The paper describes the difficulties the deaf community faces due to the dearth of real-time sign language interpretation services. It suggests a method that uses an Automatic Sign Detection system with deep learning models. The research describes current systems and their shortcomings, such as the lack of real-time translation tools and the difficulties the deaf community faces in obtaining sign language interpretation. Next, it presents the suggested system, SignComm, which has benefits including quick sign language decoding, a user-friendly platform that is easy to use and can foster an inclusive atmosphere in public areas and educational institutions a literature review that examines several deep learning models and their applicability to the project is included in the paper. It emphasizes the potential of the ResNet-50 architecture and the significance of a unique ISL dataset. The benefits of the suggested method, including its accuracy, inclusivity, and capacity to address communication barriers, are highlighted in the paper. Additionally, it outlines the steps involved in the implementation process, such as gathering data, training the ResNet model, and deploying in real time. A user-friendly experience for converting ISL gestures into text is the main goal of the intricate user interface design. © 2024 IEEE.

关键词： Deep learning

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A Preventive Environment for Pest Over the Crops Using Simplified Network Architecture 4

A Preventive Environment for Pest Over the Crops Using Simpl...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Kuhanraja, A.R. Ramkumar, P. Balaji, S. Santhosh, R. Karpagam Academy of Higher Education Department of Artificial Intelligence and Data Science Coimbatore India Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331523923

Crop pests can significantly impact productivity, making early detection and prevention crucial for management. Machine learning approaches are being used to forecast crop diseases using a variety of data sets. Relatively easy access to the environment data has resulted in a lot of attempts at predicting diseases and pests using this data. In this research, we provide a model that uses deep learning technique known as Simplified YOLO 3.1 architecture to examine past crop growth environment data to forecast illnesses. Utilizing large public datasets on crops such as tomato, pepper, grape, strawberry, and paprika, we show that the model is capable of accurately forecasting crop pest and disease risk. With an average accuracy of 98% and precision of 96%, it showed excellent predictive power. The anticipated results can help with post-harvest processing or insect control. It is expected that this learning framework and crop disease prediction model, which is based on environmental data, will be extensively useful for the prevention of pests and diseases in various facilities and crops. © 2025 IEEE.

关键词： Plant diseases

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Automation of IoT Network Traffic Analysis and Anomaly Detection using Deep Packet Inspection with Hybrid DenseNet121-BiLSTM 1

Automation of IoT Network Traffic Analysis and Anomaly Detec...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Geo Francis, E. Sheeja, S. Antony John, E.F. Joseph, Jismy Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Sri Krishna Adithya College of Arts and Science Department of Data Science Coimbatore India Department of Mathematics Thrissur India Department of Mathematics Mala India

ISBN: (纸本)9798331529246

Network security is one of the most crucial problems facing in Internet of Things. An essential component of information systems' long-term viability and regular functioning is network intrusion detection. One potential technique that aids in the identification and defense against network threats is deep packet inspection (DPI). A novel DPI method utilizing a blend of LSTM, GRU, and Recurrent Neural Networks is presented in the paper. The proposed model enhances the accuracy and reliability of detecting outcomes for the threats included in network traffic data. The proposed system detects and identify particular threats, such as malware exploitation, DoS attacks, and unauthorized access attempts, allowing linked devices and services with an accuracy of 99.99%. The system, therefore, can be applied in real-time monitoring systems, intrusion detection and prevention systems, and forensic investigations. Through a series of quantitative trials and comparisons with the most recent formulations, the effectiveness of this approach is assessed. © 2025 IEEE.

关键词： Network intrusion

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Scheduling with Obligatory Tests 32

Scheduling with Obligatory Tests

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32nd Annual European Symposium on Algorithms, ESA 2024

作者： Dogeas, Konstantinos Erlebach, Thomas Liang, Ya-Chun Department of Computer Science Durham University United Kingdom Data Science Institute Columbia University New YorkNY United States Department of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan

ISBN: (纸本)9783959773386

Motivated by settings such as medical treatments or aircraft maintenance, we consider a scheduling problem with jobs that consist of two operations, a test and a processing part. The time required to execute the test is known in advance while the time required to execute the processing part becomes known only upon completion of the test. We use competitive analysis to study algorithms for minimizing the sum of completion times for n given jobs on a single machine. As our main result, we prove using a novel analysis technique that the natural 1-SORT algorithm has competitive ratio at most 1.861. For the special case of uniform test times, we show that a simple threshold-based algorithm has competitive ratio at most 1.585. We also prove a lower bound that shows that no deterministic algorithm can be better than √2-competitive even in the case of uniform test times. © Konstantinos Dogeas, Thomas Erlebach, and Ya-Chun Liang;licensed under Creative Commons License CC-BY 4.0.

关键词： Scheduling algorithms

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data-Efficient Radiology Report Generation via Similar Report Features Enhancement 3rd

Data-Efficient Radiology Report Generation via Similar Repo...

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3rd International Workshop on Applications of Medical Artificial Intelligence, AMAI 2024 held in conjunction with the 27th International Conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2024

作者： Li, Yanfeng Sun, Jinghan Wang, Liansheng Department of Computer Science at School of Informatics Xiamen University Xiamen China National Institute for Data Science in Health and Medicine Xiamen University Xiamen China

ISBN: (纸本)9783031820069

The utilization of Artificial Intelligence in automatically generating radiology reports presents a promising solution for enhancing the efficiency of the diagnostic process and reducing human error. However, existing methods require training on large datasets of image-report pairs, which are often scarce. Moreover, the accuracy of reports generated with limited paired data significantly diminishes. To address these challenges, this study introduces a data-efficient method that integrates the retrieval of similar reports with text fusion enhancements to tackle the scarcity of image-report pairs and generate accurate radiology reports. Our method is compared with several state-of-the-art approaches, showing advancements on the MIMIC-CXR and IU X-ray benchmarks with the same limited data pairs. It achieves near-optimal results on MIMIC-CXR and comparable results on IU-Xray, highlighting not only its effectiveness and potential to improve radiological diagnosis with fewer image reports but also its ability to generate more accurate reports. By enhancing cross-modal feature interaction and demonstrating higher diagnostic accuracy, this work contributes to the fields of clinical medicine and artificial intelligence. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Radiology

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Implementing Vision Transformer to Model Emotions Recognition from Facial Expressions 4

Implementing Vision Transformer to Model Emotions Recognitio...

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4th International Conference on Artificial Intelligence and data sciences, AiDAS 2023

作者： Chowanda, Andry Nadia Diana School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Cyber Security Program Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350318432

Emotions are essential to social interaction between interlocutors. It provides essential meaning to social interaction. It is important to recognise emotions along with verbal cues to fully understand the true meaning of a conversation in a social interaction setting. Several techniques can be applied to recognise emotions in the social interaction setting automatically. Emotions can be recognised and interpreted from the interlocutor's facial cues (e.g., facial expression recognition task) by using a sensor such as a camera. Recognising emotions from facial cues has several problems to be solved. This research aims to model robust emotion recognition from facial cues to recognise emotions (particularly from Asian people), as there is only limited research done in this area. This research aims to propose an emotion recognition model from facial cues by implementing Vision Transformers architecture. The dataset implemented in this research was explicitly performed by local (i.e., Indonesian) actors (The Indonesian Mixed Emotion dataset-IMED). The results show that the proposed model can achieve the best testing accuracy of 100% with the best training loss of 0.0001, with 0.53 hours of training times for 50 epochs. © 2023 IEEE.

关键词： Emotion Recognition

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Deep ensemble optimal classifier-based software defect prediction for early detection and quality assurance

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Journal of Umm Al-Qura University for Engineering and Architecture 2025年 1-12页

作者： Srinivas, Kalyanapu Janapati, Krishna Chaithanya Sai Kiran, Mungalched Sudheer Reddy, Davu Vinay, Thota Vamshi Yashwanth, Choula Department of Computer Science and Engineering Dhanekula Institute of Engineering and Technology Andhra Pradesh Vijayawada India Department of Electronics and Communication Engineering Vardhaman College of Engineering Telangana Hyderabad India Department of Computer Science and Engineering (Data Science) Malla Reddy Engineering College and Management Sciences Medchal Telangana Kistapur India

Software Defect Prediction (SDP) is critical in identifying fault-prone modules during the software development life cycle, enhancing software quality, and reducing maintenance costs. However, existing SDP models face significant challenges, including high false positive rates and severe class imbalance between defect-prone and non-defect modules, which hinder practical model training and accurate defect detection. To overcome these challenges, this study proposes a robust and efficient SDP architecture that integrates several advanced techniques to enhance predictive accuracy and model reliability. The methodology begins with comprehensive data preprocessing, incorporating normalization to standardize feature scales and outlier detection to eliminate noise and improve data quality. To address the inherent class imbalance in the dataset, the minority oversampling by synthetic data (MOSD) technique is employed to generate synthetic samples for the minority class, thus achieving a more balanced distribution. Following this, an Adaptive sequential K-best (ASKB) feature selection strategy is implemented to identify and retain the most relevant features, effectively reducing dimensionality while preserving critical information. Finally, the weighted random forest (WRF) classifier is utilized, which assigns appropriate weights to training instances, enabling improved classification performance, especially for minority class instances. The proposed methodology achieved an accuracy of 99.119%, a precision of 99.431%, a recall of 99.122%, and an F1-score of 99.333%. © The Author(s) 2025.

关键词： Adaptive sequential K-best Class imbalance data preprocessing Feature selection Minority oversampling Predictive modeling Software defect prediction Synthetic data Weighted random forest

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Comments on "relaxed Conditions for the Input-to-State Stability of Switched Nonlinear Time-Varying Systems"

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IEEE Transactions on Automatic Control 2025年第6期70卷 4233-4236页

作者： Şahan, Gökhan Trenn, Stephan Izmir Institute of Technology Department of Mathematics and Computational Science and Engineering Program Urla Izmir Turkey University of Groningen Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence Groningen9722BA Netherlands

This study addresses the deficiencies in the assumptions of the results in Chen and Yang, 2017 [1] due to the lack of uniformity. We first show the missing hypothesis by presenting a counterexample. Then we prove why they are wrong in that form and show the errors in the proof of the main result of [1]. Next, we compare the assumptions and related results of [1] with similar works in the literature. Lastly, we give suggestions to complement the shortcomings of the hypotheses and thus correct them. © 1963-2012 IEEE.

关键词： Nonlinear simulations

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Recognition of Facial Expressions and Emotions on the Face using the YOLOv5 Algorithm

Recognition of Facial Expressions and Emotions on the Face u...

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2023 International Workshop on Artificial Intelligence and Image Processing, IWAIIP 2023

作者： Gunawan, Alexander A. S. Kusnadi, Maximilianus Raymond Joseph, James Tristan Josan, William Edbert, Ivan Sebastian Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Cyber Security Program Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350382914

This study examines the significant influence of emotional states on individuals, recognizing that people are often confronted with numerous challenges and problems in their lives. Such challenges can give rise to feelings of despair and tension, especially in those who struggle to manage them effectively. Depression, which is not just limited to adults, can also have a significant impact on children who may not fully grasp their socio-economic conditions. One of the serious consequences of unresolved emotional distress, particularly depression, is an elevated risk of suicide. Within this framework, facial recognition technology presents itself as a powerful instrument for identifying human emotions. The purpose of this study is to assess the efficacy of facial recognition systems in detecting indicators of depression. Using the YOLOv5 algorithm and comparing it with its precursors, YOLOv4 and YOLOv7, the research conducts a simulation experiment to examine the ability of the system to precisely interpret a range of emotional states, including happiness, sadness, and anger. The research findings suggest that the YOLO algorithm, particularly in its most recent versions, displays exceptional precision in identifying and classifying intricate facial expressions, thus showcasing its potential as a beneficial device in mental health evaluations. © 2023 IEEE.

关键词： Face recognition

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