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arXiv 2025年

作者： Beer, Anna Krieger, Lena Weber, Pascal Ritzert, Martin Assent, Ira Plant, Claudia Faculty of Computer Science University of Vienna Vienna Austria IAS-8: Data Analytics and Machine Learning Forschungszentrum Jülich Jülich Germany UniVie Doctoral School Computer Science University of Vienna Vienna Austria Data Science @ Uni Vienna University of Vienna Vienna Austria Institute of Computer Science and Campus Institute Data Science University of Göttingen Göttingen Germany Department of Computer Science Aarhus University Aarhus Denmark

In density-based clustering, clusters are areas of high object density separated by lower object density areas. This notion supports arbitrarily shaped clusters and automatic detection of noise points that do not belong to any cluster. However, it is challenging to adequately evaluate the quality of density-based clustering results. Even though some existing cluster validity indices (CVIs) target arbitrarily shaped clusters, none of them captures the quality of the labeled noise. In this paper, we propose DISCO, a Density-based Internal Score for Clustering Outcomes, which is the first CVI that also evaluates the quality of noise labels. DISCO reliably evaluates density-based clusters of arbitrary shape by assessing compactness and separation. It also introduces a direct assessment of noise labels for any given clustering. Our experiments show that DISCO evaluates density-based clusterings more consistently than its competitors. It is additionally the first method to evaluate the complete labeling of density-based clustering methods, including noise labels. © 2025, CC BY.

关键词： Cluster analysis

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Parameter Based Literature Survey of COVID-19 Mortality Dynamics Using Machine Learning Techniques 6

Parameter Based Literature Survey of COVID-19 Mortality Dyna...

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6th International Conference on Recent Trends on Electronics, Information, Communication and Technology, RTEICT 2021

作者： Sewariya, Manish Katarya, Rahul Delhi Technical University Big Data Analytics and Web Intelligence Laboratory Department of Computer Science New Delhi India

ISBN: (纸本)9781665435598

The pandemic COVID-19 (SARS-CoV-2) known as the coronavirus is spreading at a very alarming rate in the world which is quite a threatening situation. In terms of disease presentation, the global epidemic COVID-19 can be traced to the ancestral influenza viruses. COVID-19 outbreak is a very perturbing situation for the high rate of mortality as reported in recent studies. The mortality and morbidity rates have always been a concerning issue in the determination of the scale of epidemics. These mortality rate predictions subsume an interesting factor within them i.e. variance of mortality rates across regions. The advancement in machine learning-based tools is a vital component corresponding to addressing the 'heterogeneity in fatality rates'. This review envelopes the recent developments in mortality rate predictions for COVID-19. The review helps in gaining an insight into Spatio-temporal mortality dynamics which will be fruitful with reference to future control-strategy implementation. By observing the Spatio-temporal pattern of mortality dynamics, the inter-dependence of various factors can be explored, and hence, mortality rates can be reduced in the upcoming time. © 2021 IEEE.

关键词： COVID-19

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Automated Machine Learning (AutoML): Transforming data science Workflows in Big data analytics

Automated Machine Learning (AutoML): Transforming Data Scien...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Mohammed I. Nofal Banan Mohammad Alfalah Fayiz Momani Muaadh Mukred Omar Zughoul Fathye Mohammed Department of Business Intelligence & Data Analytics Department University of Petra Amman Jordan Amman Jordan Department of Banking and Financial Science University of Petra Amman Jordan Amman Jordan Department of Business Intelligence & Data Analytics University of Petra Amman Jordan Amman Jordan Department of Business Analytics Sunway Business School- Sunway University Petaling Jaya Bandar Sunway Selangor Malaysia Department of Computer information system and computer science Ahmad bin Mohammad Military College (ABMMC) Shahaniya Qatar Department of Business Analytics Sunway Business School University Petaling Jaya Bandar Sunway Selangor Malaysia

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

The introduction of Automated Machine Learning (AutoML) can be considered a game-changing development in the field of data science and more specifically, in the area of big data analytics. AutoML brings an expert-level approach to non-experts, allowing them to conduct intricate data analysis because it simplifies complicated tasks such as data preprocessing, feature engineering, model testing, hyper parameter adjustment of the models, and finally deploying the trained models. This advancement lessens reliance on qualified personnel, improves efficiency and helps cut down any associated lag in time from when an organization begins to utilize large amounts of data. This paper illustrates AutoML facilitates progress in multiple components for modern times: evolution, accessibility, integration into numerous sectors, and decision making. Respecting AutoML's various roles, fostering innovation while promoting inclusivity comes with certain challenges such as ethical considerations and algorithmic transparency. To summarize, AutoML will most likely disrupt analytical maturity and strengthen competitive advantages deemed sustainable today.

关键词： Analytical models Technological innovation Decision making Big data data science Automated machine learning Market research data models Tuning Testing

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The Role of Machine Learning Algorithms in Developing Android App and to do Malware Detection 3

The Role of Machine Learning Algorithms in Developing Androi...

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3rd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Sudhamsu, Gadug Bhatt, Rahul Jakka, Geethamanikanta Jweeg, Muhsin J. Alfurhood, Badria Sulaiman Tripathi, Vikas Department of Computer Science and Engineering Bangalore India Dev Bhoomi Uttarakhand University Department of CSE Uttarakhand Dehradun India University of the Cumberlands Data Analytics Information Technology United States Al-Farahidi University College of Technical Engineering Baghdad Iraq Princess Νourah Hint Ahdulrahman University College of Computer and Information Sciences Department of Computer Sciences Saudi Arabia Graphic Era Deemed to Be University Department CSE India

ISBN: (纸本)9798350399264

Because there are so many issues that have had suitable solutions, cybersecurity is now the subject that worries computer scientists and system administrators the most. The rapid development of information technology and its inherent integration into every aspect of our lives have made it possible to analyze and identify a broad variety of harmful software packages and the targets of these programmes. Malware ***, Android malware has gotten some notice. It seems to attract a lot of attention on the internet. Android is one of the most well-liked operating systems that dominates the operating system market. In 2020, 85% of smartphones will run on the Android operating system, according to statistics from the system. Mobile virus attacks have increased in frequency over time. Known to infect a phone system with applications that seem harmless but really contain malware is one of the most popular approaches. Malware on cellphones has already been detected using machine learning. Deep learning and supervised learning techniques have been used to achieve this. Drawing on consumer rights, this study evaluates the effectiveness of multiple categorization approaches to find virus in Android apps. © 2023 IEEE.

关键词： Android (operating system)

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Robust Perceptual Hashing for Fingerprint Template Protection 1

Robust Perceptual Hashing for Fingerprint Template Protectio...

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1st International Conference on computer Communications and Intelligent Systems, I3CIS 2022

作者： Birouk, Wafa Lahoulou, Atidel Melit, Ali Bouridane, Ahmed University of Bejaia Faculty of Exact Sciences Computer Science Department Bejaia06000 Algeria University Mohammed Seddik Benyahia Computer Science Department Jijel18000 Algeria LaRIA Laboratory University Mohammed Seddik Benyahia Computer Science Department Jijel18000 Algeria University of Sharjah Cybersecurity and Data Analytics Research Center Sharjah United Arab Emirates

ISBN: (纸本)9781665461887

Perceptual hashing has proven its robustness in securing biometric templates, where several methods have been proposed in order to have a robust and discriminating hash code against acceptable attacks. This paper proposes a robust perceptual hashing scheme for biometric template protection that based on SIFT and Harris functions for the feature ex-traction/selection phase and on DWT and SVD functions for the hash code generation phase. The experimental results in this paper discussed the effect of distance value and image blocks size parameters on hash performances (robustness and discrimination), which are used in the feature selection phase and the hash generation phase of our method respectively. The results show that we have could to choose the optimal parameters values in the generation of our hash code to give a best tradeoff between discrimination and robustness against acceptable image manipulations. © 2022 IEEE.

关键词： Signal reconstruction

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Estimating Smart Grid Stability with Hybrid RNN+LSTM Deep Learning Approach 12

Estimating Smart Grid Stability with Hybrid RNN+LSTM Deep Le...

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12th International Conference on Smart Grid, icSmartGrid 2024

作者： Oyucu, Saadin Sagiroglu, Seref Aksoz, Ahmet Bicer, Emre Faculty of Engineering Department of Computer Engineering Adiyaman Turkey Gazi University Artificial Intelligence and Big Data Analytics Security R&d Center Ankara Turkey Sivas Cumhuriyet University Mobilers Team Sivas Turkey Sivas University of Science and Technology Faculty of Engineering and Natural Sciences Battery Research Laboratory Sivas Turkey

ISBN: (纸本)9798350361612

Smart grids are faced with a range of challenges, such as the development of communication infrastructure, cybersecurity threats, data privacy, and the protection of user information, due to their complex structure. Another key challenge faced by smart grids is the stability issues arising from variable energy sources and consumption patterns. In these complex grid systems where energy demand and supply need to be balanced instantly, stability predictions play a significant role in foreseeing potential disruptions and optimizing energy flow. Therefore, within the scope of this study, a hybrid structure utilizing Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks is employed for stability classification to predict grid stability. This hybrid model combines the ability of RNN to recognize relationships between consecutive data points with LSTM's capability to preserve long-term dependencies. The results obtained indicate that the model exhibited stable performance with accuracy rates of 98.06% and 98.02% at 50 and 100 epochs, respectively. The findings of this study contribute valuable insights to research on the management and stability of smart grids, enabling energy systems to be operated more reliably and efficiently. © 2024 IEEE.

关键词： Smart power grids

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Real-Time Energy Efficient Communication for Large-Scale Industrial Internet of Things 2nd

Real-Time Energy Efficient Communication for Large-Scale Ind...

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2nd International Conference on Sustainability: Developments and Innovations, ICSDI 2024

作者： Khan, Musfira Khan, Muazzam A. Farman, Haleem Malik, Asad W. Ramzan, Muhammad Sher Islamabad Pakistan Department of Computer Science Quaid-I-Azam University Islamabad Pakistan ICESCO Chair for Big Data Analytics and Edge Computing Quaid-I-Azam University Islamabad Pakistan Smart Systems Engineering Lab College of Engineering Prince Sultan University Riyadh Saudi Arabia King Abdulaziz University Jeddah Saudi Arabia

ISBN: (纸本)9789819783441

Industrial Internet of Things (IIoTs) comprises large-scale sensors deployed to monitor and control the system. Deploying IoT devices presents challenges due to frequent disconnections and sudden power drainage in intermediate devices responsible for routing data. Consequently, the deployed network becomes sporadic, hindering connectivity with the industrial controller system, especially critical during emergencies. Furthermore, IoT node failures lead to a short network lifetime, which is intolerable in real-time industrial applications. This paper introduces a multi-hop clustering framework designed for reliable communication in industrial environments. The framework dynamically forms clusters, employing cluster heads and backup nodes to stabilize communication in the absence of a cluster head. Each cluster selects a backup node during the initial cluster head selection and later through data piggybacking. The proposed framework provides efficient communication across the IIoT network with the help of a backup and multi-hop clustering mechanism. The proposed framework is compared with two existing protocols i.e. cluster-based coordination routing (CRC) and energy-efficient quality of service routing (EEQSRP) protocols. The results show a considerable decrease in sensor energy consumption and packet delay. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Energy utilization

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Enhancing IoT Security and Efficiency Using Machine Learning

Enhancing IoT Security and Efficiency Using Machine Learning

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Lara Al–Rbabah Ahmad Al–Qerem Mohammad A Al Khaldy Hussain Mohammed Turki Computer Science Department Zarqa University Zarqa Jordan Business Intelligence Data Analytics University of Petra Amman Jordan University Headquarter University Of Anbar Ramadi Anbar Iraq

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

The Internet of Things (IoT) revolutionized modern infrastructure by enabling interconnected devices to improve operational efficiency and provide innovative applications across various fields. However, expanding adoption of IoT systems introduces significant challenges, particularly in cybersecurity. This study investigates the role of Artificial Intelligence (AI) and Machine Learning (ML) in addressing this challenge, focusing on anomaly detection and intrusion prevention within IoT environments. Leveraging the TON_IoT dataset, we evaluated five machine learning models-Decision Tree, Random Forest, K-Nearest Neighbors, Logistic Regression, and Naive Bayes-using both binary and multi-class classification tasks. The findings reveal that Random Forest consistently achieved the best results across all performance metrics (accuracy, precision, recall, and F1-score), making it a robust choice for IoT anomaly detection. This paper highlights the critical role of AI and ML in optimizing IoT system performance and securing networks, offering valuable insights for deploying reliable and efficient IoT solutions.

关键词： Logistic regression Accuracy Prevention and mitigation Computational modeling Nearest neighbor methods Classification algorithms Bayes methods Internet of Things computer security Random forests

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FUSegNet: A Deep Convolutional Neural Network for Foot Ulcer Segmentation

arXiv

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arXiv 2023年

作者： Dhar, Mrinal Kanti Zhang, Taiyu Patel, Yash Gopalakrishnan, Sandeep Yu, Zeyun Big Data Analytics and Visualization Laboratory Department of Computer Science University of Wisconsin-Milwaukee MilwaukeeWI53201 United States

This paper presents FUSegNet, a new model for foot ulcer segmentation in diabetes patients, which uses the pretrained EfficientNet-b7 as a backbone to address the issue of limited training samples. A modified spatial and channel squeeze-and-excitation (scSE) module called parallel scSE or P-scSE is proposed that combines additive and max-out scSE. A new arrangement is introduced for the module by fusing it in the middle of each decoder stage. As the top decoder stage carries a limited number of feature maps, max-out scSE is bypassed there to form a shorted P-scSE. A set of augmentations, comprising geometric, morphological, and intensity-based augmentations, is applied before feeding the data into the network. The proposed model is first evaluated on a publicly available chronic wound dataset where it achieves a data-based dice score of 92.70%, which is the highest score among the reported approaches. The model outperforms other scSE-based UNet models in terms of Pratt's figure of merits (PFOM) scores in most categories, which evaluates the accuracy of edge localization. The model is then tested in the MICCAI 2021 FUSeg challenge, where a variation of FUSegNet called x-FUSegNet is submitted. The x-FUSegNet model, which takes the average of outputs obtained by FUSegNet using 5-fold cross-validation, achieves a dice score of 89.23%, placing it at the top of the FUSeg Challenge leaderboard. The source code for the model is available on GitHub. © 2023, CC BY-NC-SA.

关键词： Image segmentation

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Comparative Evaluation of Attribute-Enabled Supervised Classification in Predicting the Air Quality

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Spatial Information Research 2023年第4期31卷 399-407页

作者： Subbulakshmi, P. Vimal, S. Robinson, Y. Harold Verma, Amit Nayak, Janmenjoy School of computer science and Engineering VIT Chennai campus Tamilnadu India Data Analytics Lab Department of Artificial Intelligence and Data Science Ramco Institute of Technology Rajapalayam India Department of Computer Science and Engineering Francis Xavier Engineering College Tirunelveli India Department of Computer Science & Engineering University Centre for Research & Development Chandigarh University Gharuan Mohali India Department of Computer Science Maharaja Sriram Chandra Bhanja Deo University Odisha India

Air pollution demonstrates the appearance of toxins into the air which is blocking human prosperity and the earth. It will portray as potentially the riskiest threats that humanity anytime faced. It makes hurt animals, harvests to thwart these issues in transportation territories need to expect air quality from pollutions utilizing AI systems and IoT. Along these lines, air quality evaluation and assumption has become a huge target for human health factors and also affect internal organs related to respiratory. The accuracy of Air Pollution prediction has been involved with the machine learning techniques and the best accuracy model is identified. The air quality prediction dataset is used for identifying the meteorology air pollution data while the predicted model is involved the decision tree computation for predicting the toxin contents in the region, the Air quality indicator is used to assess the pollution level and monitoring the air quality. The performance analysis shows that the decision tree technique has produced the better results in the performance metrics of Accuracy, precision, recall, and F1-score with the minimized error values while the comparative evaluation of Attribute-enabled classification has identified the best technique for predicting the air quality.

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