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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Barmpas, Petros Tasoulis, Sotiris K. Georaakonoulos, Spiros V. Plagianakos, Vassilis P. University of Thessaly Department of Computer Science and Biomedical Informatics Greece University of Thessaly Department of Mathematics Greece

ISBN: (纸本)9798350308365

Single-cell RNA sequencing (scRNA-seq) represents a paradigm shift in understanding the complexities of cellular functions and states at an individual level. Despite its transformative potential in revealing cellular heterogeneity and mechanisms, scRNA-seq data poses significant analytical challenges due to its high dimensionality, noise, and sparsity. This paper introduces Hyperdimensional Computing (HDC) as an alternative classification approach, suited for addressing these challenges. HDC, characterized by its robustness to noise and efficiency in high-dimensional spaces, accommodates the sparsity and variability inherent in scRNA-seq for the classification and analysis of scRNA-seq data offerring a promising pathway. This study aims to explore the application of HDC in scRNA-seq data classification, benchmarking its performance against traditional methods, and discussing its potential to enhance single-cell transcriptomic analysis. This work not only contributes to advancing the computational methodologies available for scRNA-seq analysis but also establishes a foundation for future research into scalable and robust data-driven approaches in genomics. © 2024 IEEE.

关键词： Gene encoding

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Conjugation of Hypersoft Set and Triangular Neutrosophic to Evaluate cognitive Digital Twins' Contribution to Smart Manufacturing

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Neutrosophic Sets and Systems 2025年 80卷 632-644页

作者： Mohamed, Mona Alaa, Nurhan Higher Technological Institute 10th of Ramadan City44629 Egypt Department of Computer Science Faculty of Computers and Informatics Zagazig University Zagazig44519 Egypt

The latest innovation in digital twin technology is called cognitive Digital Twins (CDT). The sophisticated and autonomous activities made possible by this technology have the potential to revolutionize manufacturing. An overview of CDT in manufacturing is given in this paper, along with an examination of their main features, components, and possible uses. CDT can learn from data, reason about difficult circumstances, and make well-informed judgments by combining artificial intelligence, machine learning, and knowledge representation approaches. The advantages of CDT in smart manufacturing are covered in the paper. Hence, the objective of this paper is evaluating the smart manufacturing that adopts CDT in its operations and practices to be smart. Multi-Criteria Decision Making (MCDM) as CRiteria Importance Through Intercriteria Correlation and multi-objective optimization based on simple ratio analysis (MOOSRA) are leveraged to construct soft decision models. This model can trat with vague and incomplete information through harnessing uncertainty theory especially Triangular Neurosophic Number (TriNN). Also, Hypersoft set is utilized with MOOSRA to rank alternatives of smart manufacturing. © 2025, University of New Mexico. All rights reserved.

关键词： Smart manufacturing

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A machine learning approach to formation of earthquake categories using hierarchies of magnitude and consequence to guide emergency management

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Data science and Management 2023年第4期6卷 208-213页

作者： Donald Douglas Atsa’am Terlumun Gbaden Ruth Wario Department of Computer Science and Informatics Faculty of Natural and Agricultural SciencesUniversity of the Free StatePhuthaditjhaba9866South Africa Department of Computer Science College of Physical SciencesJoseph Sarwuan Tarka UniversityMakurdi97001Nigeria

This study deployed k-means clustering to formulate earthquake categories based on magnitude and consequence,using global earthquake data spanning from 1900 to *** on patterns within the historical data,numeric boundaries were extracted to categorize the magnitude,deaths,injuries,and damage caused by earthquakes into low,medium,and high *** a future earthquake incident,the classification scheme can be utilized to assign earthquakes into appropriate categories by inputting the magnitude,number of fatalities and injuries,and monetary estimates of total *** resulting taxonomy provides a means of classifying future earthquake incidents,thereby guiding the allocation and deployment of disaster management resources in proportion to the specific characteristics of each ***,the scheme can serve as a reference tool for auditing the utilization of earthquake management resources.

关键词： Earthquake categories Magnitude and consequence Earthquake classification Resource allocation K-means clustering

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Evaluation of Deep Convolutional Neural Network-Based Tattoo Detection 7

Evaluation of Deep Convolutional Neural Network-Based Tattoo...

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7th International Congress on Ambient Intelligence, Software Engineering, and e-Health and Mobile Health, AmITIC 2024

作者： Jiménez-Delgado, E. Quesada-López, C. Méndez-Porras, A. Lara-Petitdemange, A. Alfaro-Velasco, J. Graduate Program in Computer Science and Informatics University of Costa Rica Costa Rica Technological Institute of Costa Rica Computer Engineering Department Costa Rica School of Computer Science and Informatics University of Costa Rica Costa Rica

ISBN: (纸本)9798350364538

In this study, two deep learning models for automatic tattoo detection were analyzed;a modified Convolutional Neural Network (CNN) and pre-trained ResNet-50 model. In order to achieve this, ResNet-50 uses transfer learning with fine-tuning. The purpose of this study was to evaluate the accuracy, precision, recall, F1-score, and computational efficiency of the system being considered. To augment the dataset included 1000 photos that were equally divided between those showing tattoos and those that did not show tattoos. A k-fold cross-validation approach was employed in training and testing the models. Although custom CNNs are effective, utilizing pre-trained ones like ResNet-50 can offer even better outcomes. Specifically, ResNet-50 attained a higher accuracy (0.86 compared to 0.79), precision (0.85 versus 0.78), recall (0.91 against 0.86), and F1-score (0.91 vis-a-vis 0.86) as compared to custom CNNs. In selecting these models for examination, two main motivations were considered. The first motivation is to see whether transfer learning with a pre-trained ResNet-50 model does well when compared with a customized CNN designed specifically for tattoo detection. Secondly,the intent of this study is to know what advantages can be derived from each approach and their demerits too. Furthermore, it seeks to determine if transfer learning can provide an alternative in contrast to the common CNN techniques with regards to precision and computational efficiency. In this research, two models will be evaluated in order to answer the question of what is better for tattoo detection: transfer learning or designing custom architectures. © 2024 IEEE.

关键词： Convolution

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Autonomous Unbiased Study Group Formation Algorithm for Rapid Knowledge Propagation

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computer Systems science & Engineering 2022年第7期42卷 15-31页

作者： Monday Eze Charles Okunbor Solomon Esomu Nneka Richard-Nnabu Kayode Oladapo Oghenetega Avwokuruaye Abisola Olayiwola Akpovi Ominike Godwin Odulaja Oluwatobi Akinmerese Department of Computer Science Babcock UniversityOgunNigeria Department of Computer Science Admiralty UniversityDeltaNigeria Department of Computer Science National Open UniversityNigeria Department of Computer Science and Informatics Alex-Ekwueme Federal UniversityEbonyiNigeria Department of Cyber Security Admiralty UniversityDeltaNigeria Department of Computer Science Caleb UniversityImotaLagosNigeria Department of Computer Science and Information Technology Petroleum Training InstituteDeltaNigeria Department of Computer Science Tai Solarin University of EducationOgunNigeria

Knowledge propagation is a necessity,both in academics and in the *** focus of this work is on how to achieve rapid knowledge propaga-tion using collaborative study *** practice of knowledge sharing in study groupsﬁnds relevance in conferences,workshops,and class ***-nately,there appears to be only few researches on empirical best practices and techniques on study groups formation,especially for achieving rapid knowledge *** work bridges this gap by presenting a workﬂow driven compu-tational algorithm for autonomous and unbiased formation of study *** system workﬂow consists of a chronology of stages,each made of distinct *** of the most important steps,subsumed within the algorithmic stage,are the algorithms that resolve the decisional problem of number of study groups to be formed,as well as the most effective permutation of the study group participants to form collaborative *** work contributes a number of new algorithmic concepts,such as autonomous and unbiased matching,exhaustive multiplication technique,twisted round-robin transversal,equilibrium summation,among *** concept of autonomous and unbiased matching is centered on the constitution of study groups and pairs purely based on the participants’performances in an examination,rather than through any external *** part of practical demon-stration of this work,study group formation as well as unbiased pairing were fully demonstrated for a collaborative learning size of forty(40)participants,and partially for study groups of 50,60 and 80 *** quantitative proof of this work was done through the technique called equilibrium summation,as well as the calculation of inter-study group Pearson Correlation Coefﬁcients,which resulted in values higher than 0.9 in all *** life experimentation was carried out while teaching Object-Oriented Programming to forty(40)under-graduates between February and May *** result showed

关键词： Knowledge propagation collaborative study group unbiased matching twisted round robin exhaustive multiplication

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Lightweight Connective Detection Using Gradient Boosting 20

Lightweight Connective Detection Using Gradient Boosting

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20th Joint ACL - ISO Workshop on Interoperable Semantic Annotation, ISA 2024

作者： Erolcan Er, Mustafa Kurfalı, Murathan Zeyrek, Deniz Cognitive Science Dept. Graduate School of Informatics Middle East Technical University Turkey Sensory-Cognitive Interaction Lab. Department of Psychology Stockholm University Sweden

ISBN: (纸本)9782493814326

In this work, we introduce a lightweight discourse connective detection system. Employing gradient boosting trained on straightforward, low-complexity features, this proposed approach sidesteps the computational demands of the current approaches that rely on deep neural networks. Considering its simplicity, our approach achieves competitive results while offering significant gains in terms of time even on CPU. Furthermore, the stable performance across two unrelated languages suggests the robustness of our system in the multilingual scenario. The model is designed to support the annotation of discourse relations, particularly in scenarios with limited resources, while minimizing performance loss. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Deep neural networks

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Optimizing Feature Extraction for Website Visuals: A Comparative Study of AlexNet and Inception V3 12

Optimizing Feature Extraction for Website Visuals: A Compara...

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12th International Conference on Cyber and IT Service Management, CITSM 2024

作者： Wahid, Arif Mu'amar Hariguna, Taqwa Karyono, Giat Magister of Computer Science Universitas Amikom Purwokerto Purwokerto Indonesia Universitas Amikom Purwokerto Magister of Computer Science and Information System Department Purwokerto Indonesia Universitas Amikom Purwokerto Magister of Computer Science and Informatics Department Purwokerto Indonesia

ISBN: (纸本)9798331528799

This study investigates the feature extraction capabilities of two prominent convolutional neural network (CNN) architectures, Inception V3 and AlexNet, in the context of website visuals. With the growing need for automated visual data analysis on the web, effective feature extraction is critical for tasks such as image classification, object detection, and visual content analysis. Inception V3 and AlexNet were selected due to their distinct architectural approaches and widespread use in various computer vision applications. The research employed a controlled experimental design, utilizing a dataset of website visuals collected from platforms like Themeforest. Data preprocessing included image resizing, normalization, and augmentation techniques to enhance model robustness. Both models were fine-tuned on the specific dataset to adapt their pretrained weights from ImageNet to the unique characteristics of website visuals. Performance metrics were used to evaluate the models' effectiveness, including precision, recall, F1 score, Mean Average Precision (MAP), and Normalized Discounted Cumulative Gain (NDCG). Results indicated that AlexNet outperformed Inception V3 in precision (0.7062 vs. 0.6860) and recall (0.5914 vs. 0.0506), highlighting its efficiency and suitability for more straightforward feature extraction tasks with lower computational costs. In contrast, Inception V3 exhibited superior potential in capturing complex visual patterns but struggled with computational efficiency, as evidenced by its significantly longer inference time (222.9552 seconds compared to 63.2978 seconds for AlexNet). The findings underscore the importance of model selection based on specific application requirements, such as prioritizing speed or depth of feature extraction. Recommendations for practitioners include leveraging AlexNet for resource-constrained environments and opting for Inception V3 when handling complex, multi-scale visual data. Future research could explore additional

关键词： Convolutional neural networks

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EXPLORING INFLUENTIAL FACTORS IN KNOWLEDGE SHARING WITHIN INDONESIAN PRISONS 22

EXPLORING INFLUENTIAL FACTORS IN KNOWLEDGE SHARING WITHIN IN...

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22nd International Conference on e-Society 2024, ES 2024 and 20th International Conference on Mobile Learning 2024, ML 2024

作者： Imandeka, Ejo Hidayanto, Achmad Nizar Suhartanto, Heru Pidanic, Jan Department of Computer Science Faculty of Computer Science University of Indonesia Depok Indonesia Department of Electrical Engineering Faculty of Electrical Engineering and Informatics Pardubice University Pardubice Czech Republic

The exchange of knowledge is widely recognized as a crucial aspect of effective knowledge management. When it comes to sharing knowledge within Prison settings, things get complicated due to various challenges such as limited resources, social stigma, isolation, communication barriers, and security concerns. Additionally, there's a scarcity of resources dedicated to promoting knowledge sharing in secure environments. This study aims to dig deep into the key factors that influence how knowledge is shared in Indonesian prisons. To navigate this intricate terrain, the study uses questionnaires distributed among Prison officers to understand the complex relationship between the identified critical factors and knowledge sharing in Prison. Leadership support, organizational culture, organizational structure, incentives, and individual beliefs emerge as pivotal elements in knowledge sharing. It's also noted that the connection between these critical factors and the intention to share knowledge is significantly influenced by organizational structure. Recognizing and addressing critical factors in knowledge sharing within prisons not only improves rehabilitation within prison walls but also transforms the broader criminal justice system. This approach fosters evidence-based practices, better communication, and a comprehensive understanding of factors leading to successful rehabilitation and reduced recidivism, ultimately creating a more just and effective system. © 2024 International Conferences e-Society 2024 and Mobile Learning 2024. All rights reserved.

关键词： Critical Factors Indonesia Knowledge Knowledge Sharing Prison

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Leveraging Machine Learning to Detect and Predict Diabetes in Polycystic Ovary Syndrome Patients: A Review 7

Leveraging Machine Learning to Detect and Predict Diabetes i...

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7th International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems, icABCD 2024

作者： Ndjene, Julia Gamundani, Attlee M. Kanyama, Maria N. Namibia University of Science and Technology Department of Informatics Windhoek Namibia Security Namibia University of Science and Technology Department of Cyber Windhoek Namibia Namibia University of Science and Technology Department of Computer Science Windhoek Namibia

ISBN: (纸本)9798350387902

Polycystic ovary syndrome (PCOS) is a prevailing hormonal disorder affecting women in their childbearing years, often associated with hormonal imbalances and metabolic disturbances. PCOS is closely linked to various health complications, including Type 2 Diabetes Mellitus (T2DM). Early detection and prediction of T2DM among PCDS patients are essential steps in providing comprehensive care to mitigate the potential adverse effects that PCOS has on its sufferers, such as infertility. Extensive research has been conducted to leverage advanced tools, particularly Machine Learning (ML) algorithms, for detecting and predicting PCOS and diabetes. This review explores the efforts to address the intersection of PCOS and diabetes, specifically focusing on ML-based approaches. Through a thorough examination of recent literature, this paper reveals all ML algorithms, methodologies and datasets that apply to detecting and predicting T2DM among PCOS patients. This paper further aims to discover and discuss possible challenges and limitations in leveraging ML in addressing the intersection of PCOS and T2DM and the future directions of ML as far as personalised healthcare interventions targeting PCOS patients at risk of T2DM are concerned. © 2024 IEEE.

关键词： Diseases

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Detecting the Abuse of Cloud Services for C&C Infrastructure Through Dynamic Analysis and Machine Learning

Detecting the Abuse of Cloud Services for C&C Infrastructure...

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2024 International Symposium on Networks, computers and Communications, ISNCC 2024

作者： Al Lelah, Turki Theodorakopoulos, George Javed, Amir Anthi, Eirini Schaol of Computer Science and Informatics Cardiff University Cardiff United Kingdom Jubail Industrial College Department of Computer Science and Information Technology Jubail Saudi Arabia

ISBN: (纸本)9798350364910

Cybercriminals increasingly abuse cloud and legitimate services (CLS) as covert command and control (C&C) infrastructure to orchestrate malicious operations and evade detection. This paper addresses the critical challenge of detecting such abuse of cloud platforms. We introduce a detection system that integrates dynamic analysis with Machine Learning (ML) to accurately distinguish between benign and malicious interactions with cloud services. By utilising a comprehensive data set from VirusTotal, the system uses advanced feature extraction techniques from both host behaviour and network traffic, using Cuckoo and Triage sandboxes to extract behaviors, to develop a detection model. The results demonstrate that the model achieves nearly 98% accuracy in identifying cloud service abuse, substantially outperforming previous efforts. Furthermore, we evaluate the model's robustness against adversarial attacks that aim to decrease accuracy by manipulating the feature values. Comparative evaluations show that our method maintains a higher detection accuracy under attack compared to related systems. © 2024 IEEE.

关键词： Adversarial machine learning

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