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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Akila, D. Raja, S.R. Jeyalaksshmi, J. Revathi, M. Ashfaq, Farzeen Khan, A.A. Saveetha College of Liberal Arts & Sciences SIMATS Deemed University Department of Computer Applications Chennai India Centre for Open and Digital Education Hindustan Institute of Tech. & Science Chennai India Vels Institute of Science Technology and Advanced Studies Department of IT Chennai India Meas Institute of IT Department of Computer Science Chennai India School of Computer Science Taylor's University Subang Jaya Malaysia Negara Brunei Darussalam FIT Brunei

ISBN: (纸本)9798350353266

Text mining programs often offer numerous options for processing textual data. The inclusion of extensive meta-data-such as links to other websites, titles, authors, and publication dates-can enrich text analysis and aid in text grouping. However, this side information, or 'metadata,' can also introduce noise if not handled correctly. When used indiscriminately for clustering, this noise can degrade the quality of the resulting clusters. To address this challenge, we propose a methodology that leverages a Feature Selection procedure to identify and utilize metadata that enhances clustering effectiveness. The methodology, known as Co-Clustering with Side Information (C-CSI), is designed to maximize the benefits of relevant metadata while mitigating the effects of irrelevant or noisy data. C-CSI is implemented using datamining technologies, specifically focusing on Co-Clustering, which involves clustering both rows and columns of a matrix. In our study, we employed the canopy and k-means algorithms to perform the clustering tasks. This approach ensures that the metadata used in clustering is both relevant and beneficial, improving the overall quality of the clustering results © 2024 IEEE.

关键词： Data assimilation

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A model of service-oriented architecture of e-certification system to support boat registration and site visit inspection to support maritime safety and crew health inspection

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Marine Systems and Ocean Technology 2023年第1-2期18卷 45-63页

作者： Saputra, Diaz Gaol, Ford Lumban Abdurachman, Edi Sensuse, Dana Indra Matsuo, Tokuro Department of Computer Science BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Department of Computer Science Binus Graduate Program - Master in Information System Bina Nusantara University Jakarta11480 Indonesia Department of Research in Management Bina Nusantara University Jakarta Indonesia Faculty of Computer Science University of Indonesia Jakarta Indonesia Advanced Institute of Industrial Technology Tokyo Japan City University of Macau China Asia University Taichung Taiwan

This article describes the architecture of software services that provide registration of small boat data to build maritime safety. The proposed system architecture which so-called SIMKAPEL aims to support small boat registration for maritime safety. It is developed from a conventional service-oriented architecture (SOA) with an open standard for data interoperability between elements. The goal of this research is to implement and evaluate the proposed architecture to support small boat registration and e-certification. Several parts of the architecture have been implemented as a platform that provides core services and data sources to be used by Ministry of Transportation, Directorate General of Sea Transportation, and other government agencies. Finally, the proposed SIMKAPEL platform with the SOA architecture is evaluated for its benefits to users through the expert opinion survey to measure its efficiency. The analysis results showed that SOA-based SIMKAPEL is feasible to use as a boat e-certification system. Furthermore, experiment results provide three parameters (e.g., maritime safety, namely, cargo/passengers, boat safety facilities, and maritime safety systems) with efficiency values of 42%, 46%, and 42%, respectively. The parameter with the highest efficiency shows a big effect on the level of safety of the boat and passengers. Thus, the parameters that need to be prioritized and need boat owners’ attention are cargo/passenger, boat safety equipment, and maritime safety system. © 2023, The Author(s).

关键词： Boat e-certification Boat online certification Cloud-based service Integrated system SOA architecture

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Catalyzing EEG signal analysis: unveiling the potential of machine learning-enabled smart K nearest neighbor outlier detection

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International Journal of Information Technology (Singapore) 2024年 1-12页

作者： Aymen, Abid Khediri, Salim El Thaljaoui, Adel Miladi, Moahmed Kachouri, Abdennaceur Computer & amp Embedded Systems Lab National Engineering School of Sfax University of Sfax Sfax Tunisia Department of Information Technology College of Computer Qassim University Buraydah Saudi Arabia Department of Computer Science Faculty of Science of Gafsa University of Gafsa Gafsa Tunisia Preparatory Institute for Engineering Studies of Gafsa Gafsa Tunisia Department of Computer Science and Information College of Science at Zulfi Majmaah University Al-Majmaah 11952 Saudi Arabia Higher Institute of Computer Science and Multimedia of Sfax University of Sfax Sfax Tunisia Advanced Fluid Dynamics Energetics and Environment Lab National Engineering School of Sfax University of Sfax Sfax Tunisia

Electroencephalogram (EEG) data are susceptible to artifacts, such as lapses in concentration or poor imagination, which can significantly impact the accuracy of disease diagnosis in e-health applications. To mitigate this issue, the use of machine learning (ML) and potentially artificial intelligence (AI) solutions to accurately identify outliers becomes crucial. Unlike many AI methods that incorporate unnecessary or redundant input variables, our study focuses on detecting anomalous values in EEG data through the K nearest neighbor (KNN) process and Euclidean distance metric. Our proposed unsupervised non-parametric algorithm, known as the smart KNN outlier detector (SKOD), eliminates the need for initial parameter configurations such as the number of neighbors (K), while achieving high performance. Evaluation of SKOD using real EEG data from 140 trials demonstrated sensitivity and specificity exceeding 60%, with nearly perfect accuracy in detecting outliers reaching close to 100%. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Data analysis EEG signal Healthcare Knowledge-based systems Machine learning Outlier detection

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Loan Prediction Using Machine Learning

Loan Prediction Using Machine Learning

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2023 International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2023

作者： Sarisa, Hemanth Kumar Khurana, Varun Koti, Venkat Chandu Garg, Neha Manav Rachna International Institute of Research and Studies Department of Computer Science & Engineering India

ISBN: (纸本)9798350344387

Loan prediction is a significant problem in the banking industry. Using Technology, will change and make some improvements in Loan prediction by bank employees manually. By using "Machine learning "in loan prediction. The machine learning system will check the data for a loan of the applicant and the data will be checked so fast and without any human error. So, it will help to improve technology in Bank sectors. By this, processing the data will be checked quickly, making the data output in less time. It helps both the applicant and the bank *** learning algorithms can be used to analyze historical data and make accurate predictions about whether a loan application will be approved or not. In this study, we propose a machine learning-based loan prediction model that utilizes various features such as credit score, income, loan amount, and loan term. We explore different classification algorithms, including logistic regression, decision tree, and random forest, to build the loan prediction model. Our experimental results show that the random forest algorithm outperforms other models with an accuracy rate of 83.45%. The proposed model can help banks and financial institutions streamline their loan approval process, reduce manual effort, and minimize the risk of bad loans. © 2023 IEEE.

关键词： Data Pre-Processing Decision Tree K-Neighbours Classifier Machine Learning Python Random Forest Classifier

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Exploring the Intersection of IoT and Blockchain: An Analysis of Security and Privacy Risks

Exploring the Intersection of IoT and Blockchain: An Analysi...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Ashok, Harishma Ganesan, Swathi Somasiri, Nalinda PSG Institute of Advanced Studies Data Analytics Coimbatore India York St John University Department of Computer Science London United Kingdom

ISBN: (纸本)9798350348217

Internet of things (IoT) is one of the leading technologies of transforming the internet into the next level. Security of IoT is becoming a major challenge and its measures of stopping these insecurities have been developing. Improving IoT security is important since IoT provides a great environment for systems in the industrial world and other several applications. The challenges need to be addressed so that consumer's data can be safe. Measures available are to improve the protection of access by unauthorized persons, providing a safe way to transfer data, and ensuring synchronization of the system. Blockchain has also provided a safe way for IoT devices, each device is given the public key, IoT devices scramble and send the message dependent on the public key of the objective device so just the objective device can unscramble the message. © 2023 IEEE.

关键词： Internet of things

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A Hybrid Multi-Objective Optimization Algorithm for Scheduling Heterogeneous Workloads in Cloud 2nd

A Hybrid Multi-Objective Optimization Algorithm for Scheduli...

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Emergent Converging Technologies and Biomedical Systems Select Proceedings of the 2nd International Conference, ETBS 2022

作者： Dar, Iqbal Gani Shrivastava, Vivek Department of Computer Science International Institute of Professional Studies DAVV Indore452001 India

ISBN: (纸本)9789819922703

Cloud computing provides a platform for hosting and providing services to users in an on-demand manner over the Internet. Numerous challenges are faced due to the dynamic nature of the cloud environment. One such key challenge involves the scheduling of tasks submitted by various users at the same time on virtual machines (VMs). Task scheduling involves the optimized resource allocation to various tasks while considering several performance metrics such as the turnaround time and the throughput. To improve these performance measures, a variety of single-criteria and multi-criteria decision-making (MCDM) methods have been suggested. In this study, we have proposed a hybrid algorithm BWM_ELECTRE (Best Worst Method EliminationEt Choice Translating REeality). The performance of this algorithm has been analyzed and evaluated in terms of the turnaround time and the throughput performance measures. The efficiency of BWM_ELECTRE has been compared with the best-known MCDM method BWM_TOPSIS (Best Worst Method Technique for Order Preference by Similarity to Ideal Solution). A dataset of 2400 cloudlets has been considered to compare the performance of these algorithms. Experiments have been carried out in an open-source simulation tool CloudSim 3.0.1. The results validate that BWM_ELECTRE outperforms the BWM_TOPSIS algorithm by 1.27%, and 2.08% in terms of the average turnaround time and the throughput, respectively. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cloud computing

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Supportive Negatives Spectral Augmentation for Source-Free Cross-Domain Segmentation 39

Supportive Negatives Spectral Augmentation for Source-Free C...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zheng, Kexin Xia, Haifeng Xia, Siyu Shao, Ming Ding, Zhengming Advanced Ocean Institute Southeast University Nantong China School of Automation Southeast University Nanjing China Department of Computer and Information Science University of Massachusetts Dartmouth United States Department of Computer Science Tulane University United States

ISBN: (纸本)157735897X

Source-free domain adaptation (SFDA) aims to transfer knowledge from the well-trained source model and optimize it to adapt target data distribution. SFDA methods are suitable for medical image segmentation tasks due to its data-privacy protection and achieve promising performances. However, cross-domain distribution shift makes it difficult for the adapted model to provide accurate decisions on several hard instances and negatively affects model generalization. To overcome this limitation, a novel method 'supportive negatives spectral augmentation' (SNSA) is presented in this work. Concretely, SNSA includes the instance selection mechanism to automatically discover a few hard samples for which the source model produces incorrect predictions. And an active learning strategy is adopted to re-calibrate their predictive masks. Moreover, SNSA deploys the spectral augmentation between hard instances and others to encourage the source model to gradually capture and adapt the attributions of the target distribution. Considerable experimental studies demonstrate that annotating merely 4%∼5% of negative instances from the target domain significantly improves the segmentation performance over previous methods. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Image segmentation

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Robust Domain Generalization for Multi-modal Object Recognition 5

Robust Domain Generalization for Multi-modal Object Recognit...

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5th International Conference on Artificial Intelligence and Electromechanical Automation, AIEA 2024

作者： Qiao, Yuxin Li, Keqin Lin, Junhong Wei, Rong Jiang, Chufeng Luo, Yang Yang, Haoyu Northern Arizona University Department of Computer Information Technology FlagstaffAZ United States Ama University Department of Computer Science Quezon Philippines Massachusetts Institute of Technology Electrical Engineering & Computer Science Department CambridgeMA United States Peking University Academy for Advanced Interdisciplinary Studies Beijing China The University of Texas at Austin Department of Computer Science AustinTX United States University of Southern California Department of Computer Science Los AngelesCA United States Georgia Institute of Technology College of Computing AtlantaGA United States

ISBN: (纸本)9798350366174

In multi-label classification, machine learning encounters the challenge of domain generalization when handling tasks with distributions differing from the training data. Existing approaches primarily focus on vision object recognition and neglect the integration of natural language. Recent advancements in vision-language pre-training leverages supervision from extensive visual-language pairs. This allows learning across diverse domains and enhances recognition in multi-modal scenarios, showcasing superior transfer learning capabilities in methods like CLIPood. However, CLIPood has several limitations: differences in the utilized loss, loss of generality in evaluating only a single backbone, and neglect of class-aware visual *** address these, we propose this paper that infers the actual loss based on the implementation, broadens evaluations to larger vision-language backbones, and introduces Mixup-CLIPood with a novel mix-up loss for enhanced class-aware visual fusion. © 2024 IEEE.

关键词： Visual languages

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Room-temperature waveguide-integrated photodetector using bolometric effect for mid-infrared spectroscopy applications

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光：科学与应用（英文版） 2025年第5期14卷 1300-1311页

作者： Joonsup Shim Jinha Lim Inki Kim Jaeyong Jeong Bong Ho Kim Seong Kwang Kim Dae-Myeong Geum SangHyeon Kim School of Electrical Engineering Korea Advanced Institute of Science and Technology(KAIST)291 Daehak-RoYuseong-GuDaejeon 34141Republic of Korea Department of Electrical & Computer Engineering Inha University100Inha-roMichuhol-guIncheon 22212Republic of Korea

Waveguide-integrated mid-infrared(MIR)photodetectors are pivotal components for the development of molecular spectroscopy applications,leveraging mature photonic integrated circuit(PIC)*** various strategies,critical challenges still remain in achieving broadband photoresponse,cooling-free operation,and large-scale complementary-metal-oxide-semiconductor(CMOS)-compatible *** leap beyond these limitations,the bolometric effect-a thermal detection mechanism-is introduced into the waveguide *** importantly,we pursue a free-carrier absorption(FCA)process in germanium(Ge)to create an efficient light-absorbing medium,providing a pragmatic solution for full coverage of the MIR spectrum without incorporating exotic materials into ***,we present an uncooled waveguide-integrated photodetector based on a Ge-on-insulator(Ge-OI)PIC architecture,which exploits the bolometric effect combined with ***,our device exhibits a broadband responsivity of 28.35％/mW across 4030-4360 nm(and potentially beyond),challenging the state of the art,while achieving a noise-equivalent power of 4.03×10-7W/Hz0.5 at 4180 *** further demonstrate label-free sensing of gaseous carbon dioxide(CO2)using our integrated photodetector and sensing waveguide on a single *** approach to room-temperature waveguide-integrated MIR photodetection,harnessing bolometry with FCA in Ge,not only facilitates the realization of fully integrated lab-on-a-chip systems with wavelength flexibility but also provides a blueprint for MIR PICs with CMOS-foundry-compatibility.

关键词： applications temperature integrated room bolometric effect mid-infrared photodetector spectroscopy using

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Comparative study of DNN Models for Skin Cancer Detection 4

Comparative study of DNN Models for Skin Cancer Detection

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4th International Conference on Smart Systems and Inventive Technology, ICSSIT 2022

作者： Kavitha, P. Jayalakshmi, V. Department of Computer Science Vels Institute of Science Technology and Advanced Studies School of Computing Sciences Pallavaram Chennai India Department of Computer Applications Vels Institute of Science Technology and Advanced Studies School of Computing Sciences Pallavaram Chennai India

ISBN: (纸本)9781665401180

Malignant melanoma, the most dangerous form of skin cancer, has become more common in recent years. The persistent global increase in this cancer, as well as the high medical costs and death rate, have made early detection of this malignancy a priority. Melanoma survival and cure are directly proportional to its thickness;if it can be found early, the survival rate will be enhanced. Despite the fact that significant progress has been achieved in the identification of skin malignancies, there are still significant issues. In comparison to the naked eye, computer-based detection systems can enhance the diagnosis rate of melanoma by 5-30 percent. Because visual perception frequently contains flaws, the need for a second viewpoint with greater precision and consistency is underlined. On the other side, it minimizes the tasks and responsibilities that physicians are responsible for. Many studies have been conducted in the area of automated melanoma detection. The potential benefits of such investigations are enormous and unquantifiable. Furthermore, the challenges are numerous, and fresh contributions in this field are greatly valued. However, it is widely known that more reliable and consistent detection methods necessitate a higher level of accuracy. The goal of this paper is to provide a skin cancer diagnosis system that can automatically identify lesions as malignant or benign. Preprocessing, segmentation, feature extraction and selection, and classification are some of the methods used in an automated skin cancer diagnosis. © 2022 IEEE

关键词： Deep neural networks

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