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Journal articles on the topic 'ISIC archive'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Bajwa, Muhammad Naseer, Kaoru Muta, Muhammad Imran Malik, et al. "Computer-Aided Diagnosis of Skin Diseases Using Deep Neural Networks." Applied Sciences 10, no. 7 (2020): 2488. http://dx.doi.org/10.3390/app10072488.

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Propensity of skin diseases to manifest in a variety of forms, lack and maldistribution of qualified dermatologists, and exigency of timely and accurate diagnosis call for automated Computer-Aided Diagnosis (CAD). This study aims at extending previous works on CAD for dermatology by exploring the potential of Deep Learning to classify hundreds of skin diseases, improving classification performance, and utilizing disease taxonomy. We trained state-of-the-art Deep Neural Networks on two of the largest publicly available skin image datasets, namely DermNet and ISIC Archive, and also leveraged dis

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S., Nandhini, Abdul Sofiyan Mohammed, Kumar Sushant, and Afridi Adnan. "Skin Cancer Classification using Random Forest." International Journal of Management and Humanities (IJMH) 4, no. 3 (2019): 39–42. https://doi.org/10.35940/ijmh.C0434.114319.

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Skin cancer is a very big health issue in today’s fastgrowing population not only for old age people but for all age groups. We are classifying skin cancer of a person according to dermatoscopic images into seven different types. We handle this issue utilizing the HAM10000 (Human-Against-Machine with 10000 training images) data-set. The finalized dataset includes 10001 dermatoscopic pictures which are released as a readiness set for academic machine learning purposes and are openly available through the ISIC archive. We are classifying skin cancer of a person according to dermatoscopic i

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Veronese, Federica, Francesco Branciforti, Elisa Zavattaro, et al. "The Role in Teledermoscopy of an Inexpensive and Easy-to-Use Smartphone Device for the Classification of Three Types of Skin Lesions Using Convolutional Neural Networks." Diagnostics 11, no. 3 (2021): 451. http://dx.doi.org/10.3390/diagnostics11030451.

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Background. The use of teledermatology has spread over the last years, especially during the recent SARS-Cov-2 pandemic. Teledermoscopy, an extension of teledermatology, consists of consulting dermoscopic images, also transmitted through smartphones, to remotely diagnose skin tumors or other dermatological diseases. The purpose of this work was to verify the diagnostic validity of images acquired with an inexpensive smartphone microscope (NurugoTM), employing convolutional neural networks (CNN) to classify malignant melanoma (MM), melanocytic nevus (MN), and seborrheic keratosis (SK). Methods.

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Cano, Elia, José Mendoza-Avilés, Mariana Areiza, Noemi Guerra, José Longino Mendoza-Valdés, and Carlos A. Rovetto. "Multi skin lesions classification using fine-tuning and data-augmentation applying NASNet." PeerJ Computer Science 7 (June 3, 2021): e371. http://dx.doi.org/10.7717/peerj-cs.371.

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Skin lesions are one of the typical symptoms of many diseases in humans and indicative of many types of cancer worldwide. Increased risks caused by the effects of climate change and a high cost of treatment, highlight the importance of skin cancer prevention efforts like this. The methods used to detect these diseases vary from a visual inspection performed by dermatologists to computational methods, and the latter has widely used automatic image classification applying Convolutional Neural Networks (CNNs) in medical image analysis in the last few years. This article presents an approach that

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Lyakhova, U. A., P. A. Lyakhov, R. I. Abdulkadirov, G. A. Efimenko, S. A. Romanov, and D. I. Kaplun. "System for neural network recognition of malignant pigmented skin neoplasms with image pre-processing." Journal of Physics: Conference Series 2052, no. 1 (2021): 012023. http://dx.doi.org/10.1088/1742-6596/2052/1/012023.

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Abstract The article presents a system for the recognition of malignant pigmented skin neoplasms with a preliminary processing stage. Image pre-processing consists of removing hair structures from images, as well as resizing images and their further augmentation. Augmentation made it possible to increase the variety of training data, balance the number of images in different categories, and avoid retraining the neural network. The modeling was carried out using the MatLab R2020b software package for solving technical calculations on clinical dermatoscopic images from the international open arc

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Wang, Ying, Jie Su, Qiuyu Xu, and Yixin Zhong. "A Collaborative Learning Model for Skin Lesion Segmentation and Classification." Diagnostics 13, no. 5 (2023): 912. http://dx.doi.org/10.3390/diagnostics13050912.

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The automatic segmentation and classification of skin lesions are two essential tasks in computer-aided skin cancer diagnosis. Segmentation aims to detect the location and boundary of the skin lesion area, while classification is used to evaluate the type of skin lesion. The location and contour information of lesions provided by segmentation is essential for the classification of skin lesions, while the skin disease classification helps generate target localization maps to assist the segmentation task. Although the segmentation and classification are studied independently in most cases, we fi

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Alqudah, Ali Mohammad, Hiam Alquraan, and Isam Abu Qasmieh. "Segmented and Non-Segmented Skin Lesions Classification Using Transfer Learning and Adaptive Moment Learning Rate Technique Using Pretrained Convolutional Neural Network." Journal of Biomimetics, Biomaterials and Biomedical Engineering 42 (July 2019): 67–78. http://dx.doi.org/10.4028/www.scientific.net/jbbbe.42.67.

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A skin lesion is a very severe problem, especially in coastal countries. Early detection by a highly reliable classification of skin lesion causes a great reduction in the mortality rate. Recognition of melanoma is a complicated issue due to the high degree of visual similarities between melanoma and non-melanoma lesions. Various studies are carried out to overcome this problem and to obtain accurate screening of skin lesion, where the most recent method for segmenting and classifying the lesion is based on a deep learning algorithm. In this paper, (GoogleNet) and (AlexNet) are employed with t

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Lyakhov, P. A., and U. A. Lyakhova. "Neural network classification system for pigmented skin neoplasms with preliminary hair removal in photographs." Computer Optics 5, no. 45 (2021): 728–35. http://dx.doi.org/10.18287/2412-6179-co-863.

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The article proposes a neural network classification system for pigmented skin neoplasms with a preliminary processing stage to remove hair from the images. The main difference of the proposed system is the use of the stage of preliminary image processing to identify the location of the hair and their further removal. This stage allows you to prepare dermatoscopic images for further analysis in order to carry out automated classification and diagnosis of pigmented skin lesions. Modeling was carried out using the MatLAB R2020b software package on clinical dermatoscopic images from the internati

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Lyakhov, P. A., and U. A. Lyakhova. "Neural network classification system for pigmented skin neoplasms with preliminary hair removal in photographs." Computer Optics 5, no. 45 (2021): 728–35. http://dx.doi.org/10.18287/2412-6179-co-863.

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The article proposes a neural network classification system for pigmented skin neoplasms with a preliminary processing stage to remove hair from the images. The main difference of the proposed system is the use of the stage of preliminary image processing to identify the location of the hair and their further removal. This stage allows you to prepare dermatoscopic images for further analysis in order to carry out automated classification and diagnosis of pigmented skin lesions. Modeling was carried out using the MatLAB R2020b software package on clinical dermatoscopic images from the internati

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Wamane, Niharika, Aishwarya Yadav, Jidnyasa Bhoir, Deep Shelke, and Deepali Kadam. "A Comparative Study of Melanoma Images Using CNN And Resnet 50." Journal of Innovative Image Processing 5, no. 1 (2023): 20–35. http://dx.doi.org/10.36548/jiip.2023.1.002.

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Melanoma is a specific type of skin cancer that can be lethal if not diagnosed and treated early. This paper presents a deep-learning approach for the automatic identification of melanoma on dermoscopic images from the ISIC Archive dataset and non-dermoscopic images from the MED-NODE dataset. The method involves the development of Convolutional Neural Network (CNN) and ResNet50 models, along with various pre-processing techniques. The CNN and ResNet50 models detect melanoma from dermoscopic images with 98.07% and 99.83% accuracy respectively, using hair removal and augmentation techniques. For

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Khan, Shamsur Rahman. "CAD – Prediction Model Using Artificial Intelligence." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47696.

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Abstract Heart disease is one of the most rapidly growing diseases worldwide. Detection of artery disease in the early stages is very crucial for providing the right treatment at the right time to escape from any kind of bigger risks, this is where there has been a need of an advanced system that can detect the artery disease to get a control over it at the right time. This paper proposes a system for the medical professionals, a web application platform developed with MERN Stack for preprocessing the medical data with techniques like Regression, Random Forest, and Gradient Boosting to enhance

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Molina-Molina, Edgar Omar, Selene Solorza-Calderón, and Josué Álvarez-Borrego. "Classification of Dermoscopy Skin Lesion Color-Images Using Fractal-Deep Learning Features." Applied Sciences 10, no. 17 (2020): 5954. http://dx.doi.org/10.3390/app10175954.

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The detection of skin diseases is becoming one of the priority tasks worldwide due to the increasing amount of skin cancer. Computer-aided diagnosis is a helpful tool to help dermatologists in the detection of these kinds of illnesses. This work proposes a computer-aided diagnosis based on 1D fractal signatures of texture-based features combining with deep-learning features using transferred learning based in Densenet-201. This proposal works with three 1D fractal signatures built per color-image. The energy, variance, and entropy of the fractal signatures are used combined with 100 features e

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Lucius, Maximiliano, Jorge De All, José Antonio De All, et al. "Deep Neural Frameworks Improve the Accuracy of General Practitioners in the Classification of Pigmented Skin Lesions." Diagnostics 10, no. 11 (2020): 969. http://dx.doi.org/10.3390/diagnostics10110969.

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This study evaluated whether deep learning frameworks trained in large datasets can help non-dermatologist physicians improve their accuracy in categorizing the seven most common pigmented skin lesions. Open-source skin images were downloaded from the International Skin Imaging Collaboration (ISIC) archive. Different deep neural networks (DNNs) (n = 8) were trained based on a random dataset constituted of 8015 images. A test set of 2003 images was used to assess the classifiers’ performance at low (300 × 224 RGB) and high (600 × 450 RGB) image resolution and aggregated data (age, sex and lesio

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Manole, Ionela, Alexandra-Irina Butacu, Raluca Nicoleta Bejan, and George-Sorin Tiplica. "Enhancing Dermatological Diagnostics with EfficientNet: A Deep Learning Approach." Bioengineering 11, no. 8 (2024): 810. http://dx.doi.org/10.3390/bioengineering11080810.

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Background: Despite recent advancements, medical technology has not yet reached its peak. Precision medicine is growing rapidly, thanks to machine learning breakthroughs powered by increased computational capabilities. This article explores a deep learning application for computer-aided diagnosis in dermatology. Methods: Using a custom model based on EfficientNetB3 and deep learning, we propose an approach for skin lesion classification that offers superior results with smaller, cheaper, and faster inference times compared to other models. The skin images dataset used for this research include

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Khalkhali, Vahid, Hayan Lee, Joseph Nguyen, et al. "MST-AI: Skin Color Estimation in Skin Cancer Datasets." Journal of Imaging 11, no. 7 (2025): 235. https://doi.org/10.3390/jimaging11070235.

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The absence of skin color information in skin cancer datasets poses a significant challenge for accurate diagnosis using artificial intelligence models, particularly for non-white populations. In this paper, based on the Monk Skin Tone (MST) scale, which is less biased than the Fitzpatrick scale, we propose MST-AI, a novel method for detecting skin color in images of large datasets, such as the International Skin Imaging Collaboration (ISIC) archive. The approach includes automatic frame, lesion removal, and lesion segmentation using convolutional neural networks, and modeling normal skin tone

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Maher Ahmed, Hasan, and Manar Younis Kashmola. "A proposed architecture for convolutional neural networks to detect skin cancers." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 2 (2022): 485. http://dx.doi.org/10.11591/ijai.v11.i2.pp485-493.

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The goal of the research paper is to design and development of a computer-based system for the segmentation and classification of malignant skin diseases and a comparison between the accuracy of their detection, as two malignant diseases of skin diseases were detected. Namely, basal cell carcinoma and melanoma separately with images of nevus, and the images were collected from the ISIC 2020 archive group, as the total, The images used: 17,846 images include 3,008 images of basal cell carcinoma (BCC), 5,272 images of melanoma, and 9,566 images of a nevus, and validation data contains 20% of the

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Hasan, Maher Ahmed, and Younis Kashmola Manar. "A proposed architecture for convolutional neural networks to detect skin cancers." International Journal of Artificial Intelligence (IJ-AI) 11, no. 2 (2022): 485–93. https://doi.org/10.11591/ijai.v11.i2.pp485-493.

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The goal of the research paper is to design and development of a computer-based system for the segmentation and classification of malignant skin diseases and a comparison between the accuracy of their detection, as two malignant diseases of skin diseases were detected. Namely, basal cell carcinoma and melanoma separately with images of nevus, and the images were collected from the ISIC 2020 archive group, as the total, The images used: 17,846 images include 3,008 images of basal cell carcinoma (BCC), 5,272 images of melanoma, and 9,566 images of a nevus, and validation data contains 20% of the

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Nasiruddin, Md, Mohammad Abir Hider, Rabeya Akter, et al. "OPTIMIZING SKIN CANCER DETECTION IN THE USA HEALTHCARE SYSTEM USING DEEP LEARNING AND CNNS." American Journal of Medical Sciences and Pharmaceutical Research 06, no. 12 (2024): 92–112. https://doi.org/10.37547/tajmspr/volume06issue12-10.

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Skin cancer is among the most prevalent cancers in the USA, with millions of new cases reported each year. The two main types of skin cancer include aggressive, life-threatening melanoma and less lethal, though potentially very morbid if left unattended, non-melanoma types: basal cell carcinoma and squamous cell carcinoma. The chief aim of this research project is to devise, curate, and propose a deep-learning CNN methodology for skin cancer detection in the USA. The dataset for the current research project was retrieved from the Kaggle website, particularly, The ISIC 2016 Skin Cancer Dataset

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Qureshi, Mohammad Naved, Mohammad Sarosh Umar, and Sana Shahab. "A Transfer-Learning-Based Novel Convolution Neural Network for Melanoma Classification." Computers 11, no. 5 (2022): 64. http://dx.doi.org/10.3390/computers11050064.

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Skin cancer is one of the most common human malignancies, which is generally diagnosed by screening and dermoscopic analysis followed by histopathological assessment and biopsy. Deep-learning-based methods have been proposed for skin lesion classification in the last few years. The major drawback of all methods is that they require a considerable amount of training data, which poses a challenge for classifying medical images as limited datasets are available. The problem can be tackled through transfer learning, in which a model pre-trained on a huge dataset is utilized and fine-tuned as per t

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Qureshi, Mohammad Naved, Mohammad Sarosh Umar, and Sana Shahab. "A Transfer-Learning-Based Novel Convolution Neural Network for Melanoma Classification." Computers 11, no. 5 (2022): 64. http://dx.doi.org/10.3390/computers11050064.

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Skin cancer is one of the most common human malignancies, which is generally diagnosed by screening and dermoscopic analysis followed by histopathological assessment and biopsy. Deep-learning-based methods have been proposed for skin lesion classification in the last few years. The major drawback of all methods is that they require a considerable amount of training data, which poses a challenge for classifying medical images as limited datasets are available. The problem can be tackled through transfer learning, in which a model pre-trained on a huge dataset is utilized and fine-tuned as per t

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Qureshi, Mohammad Naved, Mohammad Sarosh Umar, and Sana Shahab. "A Transfer-Learning-Based Novel Convolution Neural Network for Melanoma Classification." Computers 11, no. 5 (2022): 64. http://dx.doi.org/10.3390/computers11050064.

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Skin cancer is one of the most common human malignancies, which is generally diagnosed by screening and dermoscopic analysis followed by histopathological assessment and biopsy. Deep-learning-based methods have been proposed for skin lesion classification in the last few years. The major drawback of all methods is that they require a considerable amount of training data, which poses a challenge for classifying medical images as limited datasets are available. The problem can be tackled through transfer learning, in which a model pre-trained on a huge dataset is utilized and fine-tuned as per t

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Fadiah, Septi Ainun, Marsofiyati, and Ervina Maulida. "Analysis of the Implementation of Standard Operating Procedures in Archives Management at PT Pelabuhan Indonesia (Persero)." International Student Conference on Business, Education, Economics, Accounting, and Management (ISC-BEAM) 2, no. 1 (2024): 1758–76. http://dx.doi.org/10.21009/isc-beam.012.118.

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This research was motivated by the application of Standard Operating Procedures (SOP) for archive management which was not yet effective when the researcher carried out practical field work activities carried out by employees. The aim of the research is to find out how the records management system works, how the records management SOP is implemented and how the effects of the records management SOP are implemented. This research uses a qualitative approach using observation, interviews and documentation methods. The data used in this research is primary data, namely data collected directly by

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Al Huda, Md Sadi, Md Asraf Ali, Ajran Hossain, et al. "Enhancing Early Detection of Melanoma: A Deep Learning Approach for Skin Cancer Prediction." JOIV : International Journal on Informatics Visualization 8, no. 3-2 (2024): 1772. https://doi.org/10.62527/joiv.8.3-2.3081.

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Melanoma, a form of skin cancer, is a substantial global public health threat due to its rising prevalence and the potential for severe outcomes if not promptly identified and managed. Detecting skin cancer lesions in their first stages enhances patient outcomes and decreases mortality rates. The core issue investigated in this research paper is the enduring problem of early skin cancer prediction. In the past, individuals often lacked awareness of their skin cancer condition until it had reached late stages. Consequently, this resulted in delayed diagnoses, which restricted the available trea

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Montaha, Sidratul, Sami Azam, A. K. M. Rakibul Haque Rafid, Sayma Islam, Pronab Ghosh, and Mirjam Jonkman. "A shallow deep learning approach to classify skin cancer using down-scaling method to minimize time and space complexity." PLOS ONE 17, no. 8 (2022): e0269826. http://dx.doi.org/10.1371/journal.pone.0269826.

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The complex feature characteristics and low contrast of cancer lesions, a high degree of inter-class resemblance between malignant and benign lesions, and the presence of various artifacts including hairs make automated melanoma recognition in dermoscopy images quite challenging. To date, various computer-aided solutions have been proposed to identify and classify skin cancer. In this paper, a deep learning model with a shallow architecture is proposed to classify the lesions into benign and malignant. To achieve effective training while limiting overfitting problems due to limited training da

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Khalizah Alfi Fadhliyah, Christian Wiradendi Wolor, and Marsofiyati. "Analysis of Archival Transformation at X Institution in Bekasi City." International Student Conference on Business, Education, Economics, Accounting, and Management (ISC-BEAM) 3, no. 1 (2025): 498–516. https://doi.org/10.21009/isc-beam.013.31.

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This research is motivated by the importance of archival transformation as an effort to increase the efficiency and effectiveness of archive management at Institution X Bekasi City. This research uses a qualitative descriptive method by describing and analyzing problems based on primary and secondary data collected through observation, interviews, documentation, as well as studies from books, previous research and related articles. This research aims to find out more deeply about the transformation of archives at Institution X Bekasi City which faces challenges in the manual archive management

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Febrila, Zahra Aisha, Christian Wiradendi Wolor, and Marsofiyati. "The Analysis of Dynamic Archive Handling System at Pt. X." International Student Conference on Business, Education, Economics, Accounting, and Management (ISC-BEAM) 3, no. 1 (2025): 881–96. https://doi.org/10.21009/isc-beam.013.61.

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This research uses a descriptive qualitative approach with primary data collection obtained through interviews, observation and documentation to analyze the problems found. As well as using secondary data collection derived from articles, research journals and research books. The purpose of this research is to find out the dynamic archive handling system in storing and managing archives at PT. X starting from creation, maintenance, use, and depreciation whether it has been implemented properly. The results obtained from this study are that the dynamic archive handling system at PT. X has not b

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Dinda Mandasari, Christian Wiradendi Wolor, and Maulana Amirul Adha. "Archives Management System Analysis Of Sales Operations Division PT United Tractors." International Student Conference on Business, Education, Economics, Accounting, and Management (ISC-BEAM) 2, no. 1 (2024): 1595–618. http://dx.doi.org/10.21009/isc-beam.012.107.

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The aim of this research is to find out how to manage archives starting from creation, utilization, storage, rediscovery, transfer, depreciation and destruction of archives. This research uses qualitative research and 5 informants as research sources. The data collection techniques used are interview techniques, documentation and observation. Data analysis is carried out by collecting data, reducing data, presenting data, and drawing conclusions. The results of research regarding the archive management system at the Sales Operation Division of PT United Tractors include creation, storage and r

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Abir, Shake Ibna, Shaharina Shoha, Md Miraj Hossain, et al. "Deep Learning-Based Classification of Skin Lesions: Enhancing Melanoma Detection through Automated Preprocessing and Data Augmentation." Journal of Computer Science and Technology Studies 6, no. 5 (2024): 152–67. https://doi.org/10.32996/jcsts.2024.6.5.13.

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Skin cancer of the most dangerous type, melanoma, requires an early and accurate diagnosis for its treatment to reduce mortality and increase the number of positive outcomes. Even with the availability of better imaging and diagnostic techniques, it is still difficult to differentiate between benign lesions and malignant melanoma because of overlapping features, noisy images and images with artefacts such as hair and glare. To overcome these challenges, this research adopts deep learning models to classify skin lesions based on images from the ISIC Archive dataset. The study establishes a stro

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Priyanka Pramila, R., and R. Subhashini. "Optimized compact fortified weight-prioritized convolutional network for swift skin lesion identification using dermoscopic images." Edelweiss Applied Science and Technology 8, no. 5 (2024): 478–97. http://dx.doi.org/10.55214/25768484.v8i5.1711.

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Skin cancer is a leading cause of cancer-related mortality, posing a significant global health challenge. Early detection and treatment are crucial for survival rates. While dermoscopy is a valuable non-invasive imaging tool for diagnosing skin lesions, its reliance on the expertise of dermatologists introduces variability, affecting diagnostic reliability. Existing deep learning models for skin lesion analysis often prioritize accuracy over computational efficiency, limiting their practical application in clinical settings where both rapidity and precision are crucial. To address these limita

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Khaled Khalifa Said, Chibani Belgacem Rhaimi. "Developing an Artificial Intelligence Model to Analyze Skin Images and Detect Skin Cancer in Its Early Stages." Journal of Information Systems Engineering and Management 10, no. 3 (2025): 21–36. https://doi.org/10.52783/jisem.v10i3.3586.

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Skin cancer, mainly melanoma, is one of the most competitive styles of most cancers, and early detection is vital for improving patient outcomes. This have a look at aimed to develop a convolutional neural network (CNN)-based model for the early detection of pores and skin most cancers the usage of dermatoscopic snap shots. The version was trained on a dataset from the International Skin Imaging Collaboration (ISIC) archive, which contained labeled pics of both benign and malignant pores and skin lesions. Transfer getting to know strategies, consisting of the usage of pre-trained fashions incl

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Andini Najwa Putri. "Analysis Of Digital Archive Management At Institution X." International Student Conference on Business, Education, Economics, Accounting, and Management (ISC-BEAM) 3, no. 1 (2025): 452–61. https://doi.org/10.21009/isc-beam.013.28.

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This research was conducted with the aim of finding out the archive management system, the positive impact of the implementation of digital archives, and the challenges faced in managing digital archives in Institution X. The design of this study uses qualitative research with a case study approach to provide a comprehensive picture. The research sample selection technique uses a non-probability sampling method with a purposive sampling method. The data collection technique is carried out using several methods, namely observation, interviews and documentation. Meanwhile, the data validity tech

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Sanders, Willemien, and Mariana Salgado. "Re-using the archive in video posters: A win–win for users and archives." Interactions: Studies in Communication & Culture 8, no. 1 (2017): 63–78. http://dx.doi.org/10.1386/iscc.8.1.63_1.

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Jundira, Christian Wiradendi Wolor, and Marsofiyati. "The Analysis of Letter Archive Management in the Administrative System of PT Lemo Utama." International Student Conference on Business, Education, Economics, Accounting, and Management (ISC-BEAM) 3, no. 1 (2025): 606–21. https://doi.org/10.21009/isc-beam.013.40.

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This study focuses on the management of correspondence archives at PT Lemo Utama, aiming to identify current practices and challenges faced in the archiving process. Using a qualitative research approach, the study was conducted through case studies involving direct observations and in-depth interviews with employees responsible for managing and archiving documents. The findings indicate that, while an archive management system is in place, significant issues such as disorganization and inefficiencies are affecting the retrieval and processing of important documents. These challenges can lead

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Monica Jean, Marsofiyati, and Adnan Kasofi. "Analysis Of The Implementation Of Electronic Archive Data Transformation At The Coordinating Ministry For Humanitarian Development And Culture." International Student Conference on Business, Education, Economics, Accounting, and Management (ISC-BEAM) 2, no. 1 (2024): 2052–59. http://dx.doi.org/10.21009/isc-beam.012.147.

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This research was conducted at the Coordinating Ministry for Human Development and Culture or better known as the Kemenko PMK. The results of this research aim to find out whether institutions in Indonesia still use conventional archives or already use electronic archive management. This research uses qualitative methods with 4 resource persons for data collection. The data collection process in research uses observation, interviews and documentation. The analysis in this research was prepared using descriptive analysis methods. The analysis techniques used are data reduction, data presentatio

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Astuty Rihanna, Prof. Dr. Henry Eryanto, M.M., and Dr. Terrylina Arvinta Monoarfa, SE., MM. "Analysis of the Implementation of Digitization at the National Archives of the Republic of Indonesia." International Student Conference on Business, Education, Economics, Accounting, and Management (ISC-BEAM) 2, no. 1 (2024): 2549–56. http://dx.doi.org/10.21009/isc-beam.012.194.

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The aim of this research is to examine the obstacles that influence the archive digitization process at the National Archives of the Republic of Indonesia. Researchers collect data by visiting the research location directly. Research data was obtained through literature studies such as journals, books, direct observation by going into the field, and interviews with informants related to the topic studied. The research method used is descriptive analysis which aims to explain or describe a situation or phenomenon that occurs. This method allows researchers to export aspects more widely. The res

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Oumoulylte, Mariame, Ali Omari Alaoui, Yousef Farhaoui, Ahmad El Allaoui, and Abdelkhalek Bahri. "Convolutional neural network-based skin cancer classification with transfer learning models." Radioelectronic and Computer Systems, no. 4 (December 6, 2023): 75–87. http://dx.doi.org/10.32620/reks.2023.4.07.

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Skin cancer is a medical condition characterized by abnormal growth of skin cells. This occurs when the DNA within these skin cells becomes damaged. In addition, it is a prevalent form of cancer that can result in fatalities if not identified in its early stages. A skin biopsy is a necessary step in determining the presence of skin cancer. However, this procedure requires time and expertise. In recent times, artificial intelligence and deep learning algorithms have exhibited superior performance compared with humans in visual tasks. This result can be attributed to improved processing capabili

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Ayu Suwarni, Shofa, Christian Wiradendi Wolor, and Marsofiyati. "Analysis Of Maintenance Procedures For Archives Facilities And Infrastructure Division Of Human And General Resources Of The National Library Of The Republic Of Indonesia." International Student Conference on Business, Education, Economics, Accounting, and Management (ISC-BEAM) 3, no. 1 (2025): 1549–56. https://doi.org/10.21009/isc-beam.013.118.

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This research uses a qualitative descriptive approach, namely by describing and examining the problems found. This research data was obtained through observation, interviews and documentation, and previous research articles. The aim of this research is to find out about the procedures for maintaining archival facilities and infrastructure in the human and general resources division of the library. National Republic of Indonesia which includes the availability of good facilities and infrastructure for archival activities as well as obstacles and solutions in maintaining archival facilities and

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Rotermund, Hermann, and Christian Herzog. "Archives of the digital." Interactions: Studies in Communication & Culture 8, no. 1 (2017): 3–7. http://dx.doi.org/10.1386/iscc.8.1.3_2.

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Thabita Salsabila Putri, Christian Wiradendi Wolor, and Marsofiyati. "Analysis of Archive Room Layout at XYZCompany." International Student Conference on Business, Education, Economics, Accounting, and Management (ISC-BEAM) 3, no. 1 (2025): 684–99. https://doi.org/10.21009/isc-beam.013.45.

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This research uses a qualitative descriptive approach by describing and analyzing the layout of the archive room at XYZ Company. The data in this research consists of two types, namely primary data and secondary data. Primary data was obtained through field observations, in-depth interviews with employees involved in archive management, as well as internal company documentation. Meanwhile, secondary data comes from relevant literature, such as books, scientific journals, and previous research related to the research. The aim of this research is to understand the conditions of the archive room

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Heise, Andreas M. "ISIL − Ein internationales Kennzeichen für Archive, Bibliotheken und Museen." Bibliotheksdienst 46, no. 10 (2012): 912–16. http://dx.doi.org/10.1515/bd.2012.46.10.912.

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Heise, Andreas M. "ISIL − Ein internationales Kennzeichen für Archive, Bibliotheken und Museen." Bibliotheksdienst 46, no. 11 (2012): 912–16. http://dx.doi.org/10.1515/bd.2012.46.11.912.

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Casquete de Prado Sagrera, Nuria, and Isabel González Ferrín. "Inventario de los expedientes de limpieza de sangre de la Capilla Real de Sevilla." Isidorianum 9, no. 17 (2000): 185–225. http://dx.doi.org/10.46543/isid.0017.1007.

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Los expedientes de la Capilla Real de Sevilla acaban de ser depositados en el Archivo de la Institución Colombina de la Catedral para su inventario y para que puedan estar al servicio de los investigadores, junto con otros fondos bibliográficos y documentales de la Catedral. Tras una primera aproximación a este rico y desconocido archivo, el trabajo que se presenta en este trabajo es el inventario de una de sus series documentales más más interesantes para la investigación actual, por su contenido genealógico. También tenemos prevista la elaboración y publicación del inventario completo de est

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Michel, P. M. "METHODOLOGY AND MEANING OF THE 3D MODELLING OF THE LOST BAALSHAMIN TEMPLE IN PALMYRA." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-M-2-2023 (June 26, 2023): 1067–72. http://dx.doi.org/10.5194/isprs-archives-xlviii-m-2-2023-1067-2023.

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Abstract. The Baalshamin temple in Palmyra was completely destroyed by ISIL in 2015. To address this issue, the “Collart-Palmyre Project” at the University of Lausanne (UNIL) digitally published the scientific archive of Paul Collart who was in charge of the excavation of the temple in the 1950’. Since 2017, the Project makes the archives accessible on an online open access database (tiresias.unil.ch). A 3D reconstruction of the temple has been realized by the UNIL team in collaboration with ICONEM. The 3D models (including handily drawn elements) are now being integrated onto a PoTree platfor

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Casquete de Prado Sagrera, Nuria. "Inventario del Archivo General del Seminario de Sevilla." Isidorianum 6, no. 12 (1997): 515–46. http://dx.doi.org/10.46543/isid.9712.1061.

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La incorporación de todos los documentos y expedientes del Seminario de la Diócesis de Sevilla al Archivo General de la Archidiócesis ha sido la ocasión de completar su inventario, de modo que ahora son fácilmente accesibles a los investigadores. Se trata de una colección moderna de expedientes (siglos XIX y XX) sobre los alumnos del Seminario esencialmente

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Sobola, Gabriel O., Samuel Daramola, and Emmanuel Adetiba. "https://iieta.org/Journals/ISI/Archive/Vol-30-No-3-2025." Ingénierie des systèmes d information 30, no. 3 (2025): 779–95. https://doi.org/10.18280/isi.300322.

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Deshmukh, Priyanka V., Aniket K. Shahade, Makarand R. Shahade, Disha S. Wankhede, and Pritam H. Gohatre. "https://iieta.org/Journals/ISI/Archive/Vol-30-No-3-2025." Ingénierie des systèmes d information 30, no. 3 (2025): 565–76. https://doi.org/10.18280/isi.300301.

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Timcke, Scott. "The materials of memory: Tracing archives in communication studies." Interactions: Studies in Communication & Culture 8, no. 1 (2017): 9–20. http://dx.doi.org/10.1386/iscc.8.1.9_1.

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Alfitah Carellina Ramadhan, Henry Eryanto, and Maulana Amirul Adha. "Analysis of Static Archive Storage and Discovery Procedures at ANRI Institution." International Student Conference on Business, Education, Economics, Accounting, and Management (ISC-BEAM) 2, no. 1 (2024): 2044–62. http://dx.doi.org/10.21009/isc-beam.012.140.

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This research was conducted at the National Archives of the Republic of Indonesia (ANRI) with the aim of knowing the procedures for storing and finding static archives properly and correctly. In addition, this research is expected to be a benchmark for other institutions to be more concerned and aware of the importance of managing archives effectively and efficiently so that it will make it easier for employees to find archives. The type of research used is qualitative research using descriptive methods where the results of the research are in the form of sentences or narratives that explain e

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Jayakanth, F., K. Maly, M. Zubair, and L. Aswath. "Approaches to make CDS/ISIS databases interoperable with OAI‐compliant digital libraries." Program 39, no. 3 (2005): 269–78. http://dx.doi.org/10.1108/00330330510610609.

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PurposeTo make CDS/ISIS databases OAI‐compliant.Design/methodology/approachOne of the biggest obstacles for information dissemination to a user community is that many digital libraries or bibliographic databases use heterogeneous technologies that inhibit interoperability. The Open Archives Initiative (OAI) addresses interoperability by using a framework to facilitate the discovery of content stored in distributed archives or bibliographic databases through the use of the OAI Protocol for Metadata Harvesting (OAI‐PMH). Though the OAI‐PMH is becoming the de facto standard, many of the legacy da

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Danisworo, Herdina, Christian Wiradendi Wolor, and Marsofiyati. "Analysis Of Archive Management In Administration Division At Pt Pln Pulogadung." International Student Conference on Business, Education, Economics, Accounting, and Management (ISC-BEAM) 3, no. 1 (2025): 1158–76. https://doi.org/10.21009/isc-beam.013.85.

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This study examines of the archive management system at PT PLN Pulogadung in optimizing archival processes. The research aims to evaluate how the system supports activities such as registration, handling, storage, maintenance, security, lending, retrieval, and destruction of documents, while identifying its strengths and limitations. A qualitative approach was employed, utilizing interviews with personnel involved in archival management. The data for this research was obtained through several methods, namely observation, interviews, and documentation. The results emphasize the importance of ad

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