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arXiv 2023年

作者： Musyaffa, Muhammad Salman Ikrar Yudistira, Novanto Rahman, Muhammad Arif Batoro, Jati Informatics Engineering Faculty of Computer Science Brawijaya University East Java Malang65145 Indonesia Depatement of Biology Faculty of Mathematocs and Natural Science Brawijaya University East Java Malang65145 Indonesia

The rich diversity of herbal plants in Indonesia holds immense potential as alternative resources for traditional healing and ethnobotanical practices. However, the dwindling recognition of herbal plants due to modernization poses a significant challenge in preserving this valuable heritage. The accurate identification of these plants is crucial for the continuity of traditional practices and the utilization of their nutritional benefits. Nevertheless, the manual identification of herbal plants remains a time-consuming task, demanding expert knowledge and meticulous examination of plant characteristics. In response, the application of computer vision emerges as a promising solution to facilitate the efficient identification of herbal plants. This research addresses the task of classifying Indonesian herbal plants through the implementation of transfer learning of Convolutional Neural Networks (CNN). To support our study, we curated an extensive dataset of herbal plant images from Indonesia with careful manual selection. Subsequently, we conducted rigorous data preprocessing, and classification utilizing transfer learning methodologies with five distinct models: ResNet, DenseNet, VGG, ConvNeXt, and Swin Transformer. Our comprehensive analysis revealed that ConvNeXt achieved the highest accuracy, standing at an impressive 92.5%. Additionally, we conducted testing using a scratch model, resulting in an accuracy of 53.9%. The experimental setup featured essential hyperparameters, including the ExponentialLR scheduler with a gamma value of 0.9, a learning rate of 0.001, the Cross-Entropy Loss function, the Adam optimizer, and a training epoch count of 50. This study’s outcomes offer valuable insights and practical implications for the automated identification of Indonesian medicinal plants, contributing not only to the preservation of ethnobotanical knowledge but also to the enhancement of agricultural practices through the cultivation of these valuable resources. The In

关键词： Convolutional neural networks

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Obstacle Identification and Ellipsoidal Decomposition for Fast Motion Planning in Unknown Dynamic Environments

arXiv

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arXiv 2022年

作者： Kaymaz, Mehmetcan Ure, Nazım Kemal The Faculty of Aeronautics and Astronautics Istanbul Technical University Istanbul Turkey The Faculty of Computer and Informatics Engineering Istanbul Technical University Istanbul Turkey

Collision avoidance in the presence of dynamic obstacles in unknown environments is one of the most critical challenges for unmanned systems. In this paper, we present a method that identifies obstacles in terms of ellipsoids to estimate linear and angular obstacle velocities. Our proposed method is based on the idea of any object can be approximately expressed by ellipsoids. To achieve this, we propose a method based on variational Bayesian estimation of Gaussian mixture model, the Kyachiyan algorithm, and a refinement algorithm. Our proposed method does not require knowledge of the number of clusters and can operate in real-time, unlike existing optimization-based methods. In addition, we define an ellipsoid-based feature vector to match obstacles given two timely close point frames. Our method can be applied to any environment with static and dynamic obstacles, including ones with rotating obstacles. We compare our algorithm with other clustering methods and show that when coupled with a trajectory planner, the overall system can efficiently traverse unknown environments in the presence of dynamic obstacles. © 2022, CC BY.

关键词： Motion planning

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Cardiovascular Diseases Risk Analysis using Distance-Based Similarity Measure of Neutrosophic Sets

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Neutrosophic Sets and Systems 2021年 47卷 26-37页

作者： Mustapha, Norzieha Alias, Suriana Yasin, Roliza Md Abdullah, Ilyani Broumi, Said Faculty of Computer and Mathematical Sciences Universiti Teknologi MARA Kelantan Kelantan Bukit Ilmu Machang18500 Malaysia Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Terengganu Kuala Nerus21030 Malaysia Laboratory of Information Processing Faculty of Science Ben M’Sik University Hassan II Casablanca Morocco

One of the highest causes of death in many countries in this modern era is cardiovascular diseases. There are a few symptoms that linked significantly to the cardiovascular diseases. The symptoms and diseases relationship can be represented by neutrosophic set values. In general, neutrosophic set gives remarkable contribution in denoising, clustering, segmentation, and classification in handling data of many real applications including in medical field. This study aims to analyse the cardiovascular disease risks by a new distance-based similarity measure motivated from intuitionistic fuzzy set theory. The proof for all the properties is presented clearly. Then, a case study is conducted by using the data on the severity level of the six symptoms found in two different patients. The neutrosophic data are analysed to determine the patients’ possibility of having any one or combination of the three types of cardiovascular diseases. A comparative study involving three common distance measures is conducted. The results show that the similarity indexes for all measures of both patients are less than 0.5. This situation can further conclude as both patients are possibly not suffering from cardiovascular diseases. © 2021, Neutrosophic Sets and Systems. All Rights Reserved.

关键词： Data handling

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A Qualitative Insight of Success Factors of Information Security Management System 7

A Qualitative Insight of Success Factors of Information Secu...

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7th International Conference on Research and Innovation in Information Systems, ICRIIS 2021

作者： Maarop, Nurazean Mohd Zam, Mohd Farid Samy, Ganthan Narayana Manaf, Azizah Abdul Munshi, Asmaa Magalingam, Pritheega Universiti Teknologi Malaysia Razak Faculty of Technology and Informatics Kuala Lumpur Malaysia College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia

ISBN: (纸本)9781665408059

Information Security Management System (ISMS) is the vehicle that ensures correctness, completeness, and improvement of security controls in information security management. In the Malaysian context, the number of government agencies certified by ISMS is still low and efforts to retain the certification have been challenging in the recent year. The goal of this study is to describe the success factors identified from the literature review regarding the implementation of ISMS in the selected government agencies in Malaysia. This study applied a qualitative approach, which includes semistructured interviews of nine key informants among Malaysian government technologists. A deductive thematic analysis on interviews transcript and qualitative scoring were used to describe the findings of the study. Of seven identified factors, top management and implementer commitment were revealed as the most critical components in the ISMS implementation in the context of the Malaysian government agency. © 2021 IEEE.

关键词： Security of data

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Medical imaging and analysis of dedifferentiated chondrosarcoma using CT, MRI and ultrasound

Medical imaging and analysis of dedifferentiated chondrosarc...

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2021 International Conference on Biomedical engineering, ICoBE 2021

作者： Rasheed, M.E.H. Youseffi, M. Jamil, M.M.A. Ibrahim, T.N.T. Parisi, L. Faculty of Engineering and Informatics Biomedical and Electronics Engineering Department University of Bradford BradfordBD7 1DP United Kingdom Johore Malaysia Johore Malaysia Faculty of Engineering and Informatics Computer Science Department University of Bradford BradfordBD7 1DP United Kingdom

Chondrosarcoma (CS) is a type of bone cancer that arises from the malignant transformation of chondrocytes and spreads metastatically to the surrounding bone tissue. CSs tend to grow and spread slowly, dedifferentiating into high-grade tumours;however, the cancer may grow rapidly too. What causes chondrosarcoma is not known, though it may arise from a benign tumour or bone conditions. Typically, CS tumours originate from the bones of the axial skeleton, but they may also occur in other parts of the body. CS patients usually experience aching pain around the tumour, especially at night or during physical activity, and it slowly deteriorates. Medical imaging tests play an important role in the diagnosis of CS;nevertheless, a tissue biopsy is required to confirm its diagnosis. The role of medical imaging is also vital in guiding and monitoring treatments. Surgical resection of the tumour is commonly the primary treatment for most types of CS due to its resistance to chemotherapy and radiation therapy. This work presents the case of a deceased middle-aged male subject with dedifferentiated CS of the right chest wall, including analysis of medical images involving both ionising (computed tomography) and non-ionising techniques (ultrasound and magnetic resonance imaging), in addition to treatments the subject received, along with their outcomes. Five generations of the subject’s family were investigated in detail and the subject was found the only cancer case in his family. Furthermore, the subject did not benefit from any treatments he underwent, and he died within two years from the diagnosis. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Tumors

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Cervical Cancer Classification Using Combined Machine Learning and Deep Learning Approach

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computers, Materials & Continua 2022年第9期72卷 5117-5134页

作者： Hiam Alquran Wan Azani Mustafa Isam Abu Qasmieh Yasmeen Mohd Yacob Mohammed Alsalatie Yazan Al-Issa Ali Mohammad Alqudah Department of Biomedical Engineering Jordan University of Science and TechnologyIrbid22110Jordan Department of Biomedical Systems and Informatics Engineering Yarmouk University 556Irbid21163Jordan Faculty of Electrical Engineering Technology Campus Pauh PutraUniversiti Malaysia Perlis02000ArauPerlisMalaysia Advanced Computing Centre of Excellence(CoE)Universiti Malaysia Perlis(UniMAP)02000ArauPerlisMalaysia The Institute of Biomedical Technology King Hussein Medical CenterRoyal Jordanian Medical ServiceAmman11855Jordan Department of Computer Engineering Yarmouk UniversityIrbid21163Jordan

Cervical cancer is screened by pap smear methodology for detection and classification *** smear images of the cervical region are employed to detect and classify the abnormality of cervical *** this paper,we proposed the first system that it ables to classify the pap smear images into a seven classes *** smear images are exploited to design a computer-aided diagnoses system to classify the abnormality in cervical images *** features that have been extracted using ResNet101 are employed to discriminate seven classes of images in Support Vector Machine(SVM)*** success of this proposed system in distinguishing between the levels of normal cases with 100%accuracy and 100%*** top of that,it can distinguish between normal and abnormal cases with an accuracy of 100%.The high level of abnormality is then studied and classified with a high *** the other hand,the low level of abnormality is studied separately and classified into two classes,mild and moderate dysplasia,with∼92%*** proposed system is a built-in cascading manner with five models of polynomial(SVM)*** overall accuracy in training for all cases is 100%,while the overall test for all seven classes is around 92%in the test phase and overall accuracy reaches 97.3%.The proposed system facilitates the process of detection and classification of cervical cells in pap smear images and leads to early diagnosis of cervical cancer,which may lead to an increase in the survival rate in women.

关键词： Classification deep learning machine learning pap smear images resnet101 support vector machines

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Feature Calibration Enhanced Parameter Synthesis for CLIP-based Class-Incremental Learning

arXiv

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arXiv 2025年

作者： Guo, Juncen Liu, Yang Zhu, Xiaogunag Sun, Lianlong Teng, Liangyu Wu, Jingyi Li, Di Zhou, Wei Song, Liang Academy for Engineering and Technology Fudan University Shanghai200433 China DataLab: Data Science and Informatics University of California DavisCA95616 United States University of Rochester New York14627 United States Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China Academy for Computer Science and Informatics Cardiff University Wales CF24 4AG United Kingdom

Class-Incremental Learning (CIL) enables models to continuously learn new class knowledge while retaining previous classes, facilitating adaptation and evolution in dynamic, real-world environments. Traditional CIL methods primarily rely on visual features, which limits their effectiveness in complex, multimodal scenarios. In contrast, VLMs show promising potential for enhancing CIL by leveraging pre-trained knowledge and integrating multi-modal semantic cues such as text and vision. However, existing approaches struggle to mitigate catastrophic forgetting while preserving the generalization strengths of VLMs across diverse modalities. To address these challenges, we propose a Feature Calibration Enhanced Parameter Synthesis (FCPS) framework. Specifically, FCPS introduces a dynamic parameter adjustment mechanism that iteratively calibrates the contribution of original visual features to the final class decision, thus preserving the model’s intrinsic generalization capability across modalities. Simultaneously, parameter integration enables effective knowledge transfer, maintaining a balance between acquiring new class representations and preserving old knowledge. Experimental results on popular benchmarks (e.g., CIFAR100 and ImageNet100) validate the superiority of the proposed method. Copyright © 2025, The Authors. All rights reserved.

关键词： Knowledge transfer

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The Scientific Progress and Prospects of Artificial Intelligence for Cancer Detection: A Bibliometric Analysis 24

The Scientific Progress and Prospects of Artificial Intellig...

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24th International Seminar on Intelligent Technology and Its Applications, ISITIA 2023

作者： Maulana, Fairuz Iqbal Adi, Puput Dani Prasetyo Lestari, Dian Ariyanto, Sandy Vikki Purnomo, Agung Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia National Research and Innovation Agency Telecommunication Research Center Jakarta11480 Indonesia Universitas Airlangga Faculty of Medicine Surabaya60132 Indonesia University of Madura Informatics Engineering Study Program Pamekasan Indonesia Bina Nusantara University BINUS Business School Undergraduate Program Entrepreneurship Department Jakarta11480 Indonesia

ISBN: (纸本)9798350313956

Using the Scopus database, this study aims to investigate the use of artificial intelligence for cancer detection in the last ten years from 2013 to 2022. The researchers used bibliometric analysis combined with VosViewer and Rstudio software quantification method for literature analysis. The results of the flushed articles show data such as publication year, journal, country, keywords, and authors, to the highest number of citations. Common keywords used by researchers are artificial intelligence, medical, and human. Researchers limited the findings through keywords such as in the last ten years, documents are journals, and English only so that 1868 articles were obtained. The results found that Harvard Medical School affiliation had the highest number of articles, with 101 articles, by subject area, Medicine had a proportion of 41.9% (n=1320). This bibliometric study will be useful for other researchers to examine the development of research on Artificial Intelligence for Cancer Detection in the last ten years. © 2023 IEEE.

关键词： Diseases

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On the Difficulty of NOT being Unique: Fingerprinting Users from Wi-Fi Data in Mobile Devices 25

On the Difficulty of NOT being Unique: Fingerprinting Users ...

引用

40th Annual ACM Symposium on Applied Computing, SAC 2025

作者： Cunha, Mariana Mendes, Ricardo De Montjoye, Yves-Alexandre Vilela, João P. CRACS/INESC TEC CISUC Department of Computer Science Faculty of Sciences University of Porto Porto Portugal CISUC and Department of Informatics Engineering University of Coimbra Coimbra Portugal Imperial College London London United Kingdom

ISBN: (纸本)9798400706295

The pervasiveness of mobile devices has fostered a multitude of services and applications, but also raised serious privacy concerns. In order to avoid users' tracking and/or users' fingerprinting, smartphones have been tightening the access to unique identifiers. Nevertheless, smartphone applications can still collect diverse data from available sensors and smartphone resources. Using real-world data from a field study we performed, this paper demonstrates the possibility of fingerprinting users from Wi-Fi data in mobile devices and the consequent privacy impact. From the performed analysis, we concluded that a single snapshot of a set of scanned Wi-Fi BSSIDs (MAC addresses) per user is enough to uniquely identify about 99% of the users. In addition, the most frequent Wi-Fi BSSID is sufficient to re-identify more than 90% of the users, a percentage that goes up to 97% of the users with the top-2 scanned BSSIDs. The Wi-Fi SSID (network name) also leads to a re-identification risk of about 83% and 97% with 1 and 2 of the strongest Wi-Fi Access Points (APs), respectively. Copyright © 2025 held by the owner/author(s).

关键词： privacy

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Implementation of MTCNN Facial Feature Extraction on Sleepiness Scale Classification Using CNN

Implementation of MTCNN Facial Feature Extraction on Sleepin...

引用

International Conference on computer engineering, Network and Intelligent Multimedia (CENIM)

作者： Adima Mahardika Putra Ahmad Zaini Eko Pramunanto Department of Computer Engineering Faculty of Intelligent Electrical and Informatics Technology Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (纸本)9781665476515

Sleepiness is a condition when the level of human consciousness decreases. Sleepiness is not easy to measure externally. If this is allowed just like that, it would be very dangerous if we were doing activities that requires full control of consciousness such as activities driving. This set of tools and methods for detecting drowsiness has been developed. However, in its implementation the intrusive method uses this tool less practical. In addition, the sleep detection method uses video images as well experiencing problems due to the potential for data loss in the form of facial features. To get this data, you must use a near infrared camera which has a low resolution and lacks detail. Therefore it is necessary an algorithm that is able to detect facial features to the maximum for classifying a person's sleepiness scale. To achieve the goal, a program will be created that functions to perform the extraction facial features and eye features. The program will function to detect number of frames containing facial features and performing condition classification eyes closed or open which will then be saved in a ‘csv’ file to be processed. Furthermore, the data will be carried out in the training process using 1D CNN architecture. The results of the training process that has been carried out The previous model is the model that will be used in performing the scale classification drowsiness. There are 6 experimental scenarios to get the best results. From all the results that have been obtained, it can be concluded that the best model results are the result of the training process with epoch values by 30 and the addition of synthetic data. Accuracy value obtained of 89% and the loss value of 34%.

关键词： Training Image resolution Sleep computer architecture Feature extraction Data models Data mining

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