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IEEE Pacific Rim Conference on Communications, Computers and Signal Processing

作者： S. Kai K. Katagishi K. Toraichi Y. Mitamura H. Murakami A. Koike T. Asami Doctoral Program Graduate School of Systems and Information Engineering University of Tsukuba Tsukuba Ibaraki Japan Institute of Informalion Sciences and Electronics University of Tsukuba Tsukuba Ibaraki Japan Graduate School of Engineering Hokkaido University Sapporo Japan KDDI Corporation Tokyo Japan KDD Research and Development Laboratories Inc. Kamifukuoka Saitama Japan

To match the different resolution capabilities of devices used in the telemedicine network, we must perform resolution conversion for fundus photograph to the required resolution with the information important to ophthalmologists' diagnosing well preserved. In this paper, in order to keep important information, such as color and shapes of blood vessels, optic disk and lesion areas on the fundus photograph after resolution conversion, we adopt three kinds of fluency functions to approximate shapes and propose a shape-directed resolution conversion method by using two variables compactly supported fluency sampling functions. The method leads the fundus photograph resolution conversion with features of scalability and good information preservation.

关键词： Sampling methods Interpolation Shape control Blood vessels Lesions Medical diagnostic imaging Biomedical optical imaging Photography Diseases Telemedicine

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Changing life expectancy in European countries 1990–2021: a subanalysis of causes and risk factors from the Global Burden of Disease Study 2021

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The Lancet Public Health 2025年第3期10卷 e172-e188页

作者： Steel, Nicholas Bauer-Staeb, Clarissa Maria Mercedes Ford, John A. Abbafati, Cristiana Abdalla, Mohammed Altigani Abdelkader, Atef Abdi, Parsa Zuñiga, Roberto Ariel Abeldaño Abiodun, Olugbenga Olusola Abolhassani, Hassan Abu-Gharbieh, Eman Abukhadijah, Hana J. Abu-Zaid, Ahmed Addo, Isaac Yeboah Addolorato, Giovanni Adekanmbi, Victor Adetunji, Juliana Bunmi Adeyeoluwa, Temitayo Esther Agardh, Emilie E. Agyemang-Duah, Williams Ahmad, Danish Ahmed, Ayman Ahmed, Syed Anees Akinosoglou, Karolina Akkaif, Mohammed Ahmed Al Awaidy, Salah Al Hasan, Syed Mahfuz Al Zaabi, Omar Ali Mohammed Aldridge, Robert W. Algammal, Abdelazeem M. Al-Gheethi, Adel Ali Saeed Ali, Abid Ali, Mohammed Usman Ali, Syed Shujait Ali, Waad Alicandro, Gianfranco Alif, Sheikh Mohammad Al-Jumaily, Adel Allebeck, Peter Alrawashdeh, Ahmad Al-Rifai, Rami H. Alsabri, Mohammed A. Alshahrani, Najim Z. Aluh, Deborah Oyine Al-Wardat, Mohammad Al-Zyoud, Walid A. Amiri, Sohrab Anderlini, Deanna Andrei, Catalina Liliana Anil, Abhishek Anvari, Saeid Anyasodor, Anayochukwu Edward Appiah, Seth Christopher Yaw Aquilano, Michele Arabloo, Jalal Arafat, Mosab Areda, Demelash Aremu, Abdulfatai Armani, Keivan Armocida, Benedetta Ärnlöv, Johan Asaduzzaman, Muhammad Astell-Burt, Thomas Aujayeb, Avinash Ausloos, Marcel Azadnajafabad, Sina Aziz, Shahkaar Azzam, Ahmed Y. Babu, Giridhara Rathnaiah Badache, Andreea Corina Badiye, Ashish D. Bahramian, Saeed Baig, Atif Amin Baker, Jennifer L. Bansal, Hansi Bärnighausen, Till Winfried Barone, Mark Thomaz Ugliara Barrow, Amadou Barteit, Sandra Bashir, Shahid Bashiru, Hameed Akande Basso, João Diogo Bastan, Mohammad-Mahdi Basu, Sanjay Batra, Kavita Bauckneht, Matteo Baune, Bernhard T. Beghi, Massimiliano Beiranvand, Maryam Béjot, Yannick Bell, Michelle L. Bello, Olorunjuwon Omolaja Belo, Luis Beloukas, Apostolos Beneke, Alice A. Bettencourt, Paulo J.G. Bhagavathula, Akshaya Srikanth Bhala, Neeraj Bhaskar, Sonu Bisulli, Francesca Bjørge, Tone Bodunrin, Aadam Olalekan Carvajal, Alejandro Botero Bouaoud, Souad Brayne, Carol Brenner, H Department of Primary Care and Public Health University of East Anglia Norwich United Kingdom Office for Health Improvement and Disparities Department of Health and Social Care London United Kingdom Department of Legal and Economic Studies La Sapienza University Rome Italy Rome Italy Hull York Medical School University of Hull Hull United Kingdom Department of Mathematics and Sciences Ajman University Ajman United Arab Emirates Department of Medicine Memorial University St. John's NL Canada Postgraduate Department University of Sierra Sur Miahuatlan de Porfirio Diaz Mexico Yhteiskuntadatatieteen keskus (Centre for Social Data Science) Helsinki Finland Department of Internal Medicine Federal Medical Centre Abuja Nigeria Research Center for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Department of Medical Biochemistry and Biophysics Karolinska Institutet (Karolinska Institute) Stockholm Sweden Department of Clinical Sciences University of Sharjah Sharjah United Arab Emirates Department of Biopharmaceutics and Clinical Pharmacy University of Jordan Amman Jordan Medical Research Center Hamad Medical Corporation Doha Qatar Department of Biochemistry and Molecular Medicine Alfaisal University Riyadh Saudi Arabia College of Graduate Health Sciences University of Tennessee Memphis TN United States School of Medicine Sydney NSW Australia Centre for Social Research in Health University of New South Wales Sydney NSW Australia Internal Medicine and Alcohol Related Disease Unit Fondazione Policlinico Universitario A. Gemelli IRCCS Rome Italy Department of Medical and Surgical Sciences Università Cattolica di Roma (Catholic University of Rome) Rome Italy Department of Obstetrics and Gynecology University of Texas Medical Branch Galveston TX United States Department of Biochemistry Osun State University Osogbo Nigeria Department of Pharmacology and Therapeutics University of Medical Sciences Ondo Ondo Nigeria Department of Veteri

Background: Decades of steady improvements in life expectancy in Europe slowed down from around 2011, well before the COVID-19 pandemic, for reasons which remain disputed. We aimed to assess how changes in risk factors and cause-specific death rates in different European countries related to changes in life expectancy in those countries before and during the COVID-19 pandemic. Methods: We used data and methods from the Global Burden of Diseases, Injuries, and Risk Factors Study 2021 to compare changes in life expectancy at birth, causes of death, and population exposure to risk factors in 16 European Economic Area countries (Austria, Belgium, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Luxembourg, the Netherlands, Norway, Portugal, Spain, and Sweden) and the four UK nations (England, Northern Ireland, Scotland, and Wales) for three time periods: 1990–2011, 2011–19, and 2019–21. Changes in life expectancy and causes of death were estimated with an established life expectancy cause-specific decomposition method, and compared with summary exposure values of risk factors for the major causes of death influencing life expectancy. Findings: All countries showed mean annual improvements in life expectancy in both 1990–2011 (overall mean 0·23 years [95% uncertainty interval [UI] 0·23 to 0·24]) and 2011–19 (overall mean 0·15 years [0·13 to 0·16]). The rate of improvement was lower in 2011–19 than in 1990–2011 in all countries except for Norway, where the mean annual increase in life expectancy rose from 0·21 years (95% UI 0·20 to 0·22) in 1990–2011 to 0·23 years (0·21 to 0·26) in 2011–19 (difference of 0·03 years). In other countries, the difference in mean annual improvement between these periods ranged from –0·01 years in Iceland (0·19 years [95% UI 0·16 to 0·21] vs 0·18 years [0·09 to 0·26]), to –0·18 years in England (0·25 years [0·24 to 0·25] vs 0·07 years [0·06 to 0·08]). In 2019–21, there was an overall decrease in mean annual life expectancy a

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A Novel Blockchain-Based Strategy for Energy Conservation in Cognitive Wireless Sensor Networks

A Novel Blockchain-Based Strategy for Energy Conservation in...

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Blockchain Technology and information Security (ICBCTIS), International Conference on

作者： Khemraj Sharma Rohit Goyal Sukesh Kumar Bhagat Sheenu Agarwal Girish Singh Bisht Mohemmed Hussien Department of Business Administration Doctoral Program in Management Sciences Nanhua University Dalin Taiwan School of Engineering and Computing Dev Bhoomi Uttarakhand University Dehradun India Department of Computer Science & Engineering Uttaranchal University Dehradun India Department of Electronics and Communication Engineering Uttarakhand Technical University Dehradun India Department of Computer Science and Engineering Tula's Institute Dehradun India Dehradun India The Islamic university Najaf Iraq

ISBN: (数字)9798331528324

ISBN: (纸本)9798331528331

Internet of Things (IoT) industrial applications have increased in internet-connected devices. As a result, accessibility, scalability, connectivity, and adaptability have become major challenges. It is possible to create connections between loT devices over the wireless medium, but efficiently utilizing scarce spectrum is the biggest concern. The paper proposes a novel method for selecting sensing nodes with the goal of reducing energy usage and increasing sensing efficiency. In order to ensure that node trust values conform to each other without any ambiguity, cryptography encrypts all information that relates to a node in a central node (CN). Nodes participating in CSS are selected based on their performance. Based on a comparison of various algorithms using varying numbers of nodes, the proposed algorithm achieves a 10% sensing efficiency.

关键词： Wireless communication Wireless sensor networks Adaptation models Machine learning algorithms Heuristic algorithms Scalability Sensors Blockchains Internet of Things Reliability

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Predicting online item-choice behavior: A shape-restricted regression approach

arXiv

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arXiv 2020年

作者： Nishimura, Naoki Sukegawa, Noriyoshi Takano, Yuichi Iwanaga, Jiro Graduate School of Systems and Information Engineering University of Tsukuba Ibaraki305-8577 Japan Recruit Lifestyle Co. Ltd. Tokyo100-6640 Japan Department of Information and Computer Technology Tokyo University of Science Tokyo125-8585 Faculty of Engineering Information and Systems University of Tsukuba Ibaraki305-8577 Doctoral Program in Policy and Planning Sciences University of Tsukuba Ibaraki305-8577 Erdos Inc. Kanagawa222-0033

This paper examines the relationship between user pageview (PV) histories and their item-choice behavior on an e-commerce website. We focus on PV sequences, which represent time series of the number of PVs for each user-item pair. We propose a shape-restricted optimization model that accurately estimates item-choice probabilities for all possible PV sequences. This model imposes monotonicity constraints on item-choice probabilities by exploiting partial orders for PV sequences, according to the recency and frequency of a user's previous PVs. To improve the computational efficiency of our optimization model, we devise efficient algorithms for eliminating all redundant constraints according to the transitivity of the partial orders. Experimental results using real-world clickstream data demonstrate that our method achieves higher prediction performance than that of a state-of-the-art optimization model and common machine learning methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Graph theory

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Non-invasive biopsy diagnosis of diabetic kidney disease via deep learning applied to retinal images: a population-based study

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The Lancet Digital Health 2025年第5期7卷 100868页

作者： Meng, Ziyao Guan, Zhouyu Yu, Shujie Wu, Yilan Zhao, Yaoning Shen, Jie Lim, Cynthia Ciwei Chen, Tingli Yang, Dawei Ran, An Ran He, Feng Hamzah, Haslina Singh, Sarkaaj Abd Raof, Anis Syazwani Lee-Boey, Jian Wen Samuel Lim, Soo-Kun Sun, Xufang Ge, Shuwang Xu, Gang Su, Hua Cheng, Yang Lu, Feng Liao, Xiaofei Jin, Hai Deng, Chenxin Ruan, Lei Zhang, Cuntai Wu, Chan Dai, Rongping Jin, Yixiao Wang, Wenxiao Li, Tingyao Liu, Ruhan Li, Jiajia Shu, Jia Lu, Yuwei Wang, Xiangning Wu, Qiang Qin, Yiming Tang, Jin Sheng, Xiaohua Jiao, Qiong Yang, Xiaokang Guo, Minyi McKay, Gareth J Hogg, Ruth E Liew, Gerald Chee, Evelyn Yi Lyn Hsu, Wynne Lee, Mong Li Szeto, Simon Luk, Andrea O Y Chan, Juliana C N Cheung, Carol Y Tan, Gavin Siew Wei Tham, Yih-Chung Cheng, Ching-Yu Sabanayagam, Charumathi Lim, Lee-Ling Jia, Weiping Li, Huating Sheng, Bin Wong, Tien Yin Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Institute for Proactive Healthcare Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Centre for Diabetes Shanghai Key Laboratory of Diabetes Mellitus Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Beijing Tsinghua Changgung Hospital Eye Center School of Clinical Medicine Tsinghua Medicine Tsinghua University Beijing China Medical Records and Statistics Office Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Renal Medicine Singapore General Hospital SingHealth-Duke Academic Medical Centre Singapore Singapore Department of Ophthalmology Shanghai Health and Medical Center Wuxi China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong Special Administrative Region China Singapore Eye Research Institute Singapore National Eye Centre Singapore Department of Medicine Faculty of Medicine Universiti Malaya Kuala Lumpur Malaysia Department of Ophthalmology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Hubei Wuhan China Department of Nephrology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Nephrology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Ophthalmology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China National Engineering Research Centre for Big Dat

Background: Improving the accessibility of screening diabetic kidney disease (DKD) and differentiating isolated diabetic nephropathy from non-diabetic kidney disease (NDKD) are two major challenges in the field of diabetes care. We aimed to develop and validate an artificial intelligence (AI) deep learning system to detect DKD and isolated diabetic nephropathy from retinal fundus images. Methods: In this population-based study, we developed a retinal image-based AI-deep learning system, DeepDKD, pretrained using 734 084 retinal fundus images. First, for DKD detection, we used 486 312 retinal images from 121 578 participants in the Shanghai Integrated Diabetes Prevention and Care System for development and internal validation, and ten multi-ethnic datasets from China, Singapore, Malaysia, Australia, and the UK (65 406 participants) for external validation. Second, to differentiate isolated diabetic nephropathy from NDKD, we used 1068 retinal images from 267 participants for development and internal validation, and three multi-ethnic datasets from China, Malaysia, and the UK (244 participants) for external validation. Finally, we conducted two proof-of-concept studies: a prospective real-world study with 3 months' follow-up to evaluate the effectiveness of DeepDKD in screening DKD;and a longitudinal analysis of the effectiveness of DeepDKD in differentiating isolated diabetic nephropathy from NDKD on renal function changes with 4·6 years' follow-up. Findings: For detecting DKD, DeepDKD achieved an area under the receiver operating characteristic curve (AUC) of 0·842 (95% CI 0·838–0·846) on the internal validation dataset and AUCs of 0·791–0·826 across external validation datasets. For differentiating isolated diabetic nephropathy from NDKD, DeepDKD achieved an AUC of 0·906 (0·825–0·966) on the internal validation dataset and AUCs of 0·733–0·844 across external validation datasets. In the prospective study, compared with the metadata model, DeepDKD could detect DKD wit

关键词： Deep learning

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Double-SP Method to Enhance Privacy and Security of Smart City Applications

Double-SP Method to Enhance Privacy and Security of Smart Ci...

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International Conference on computing for Sustainable Global Development (INDIACom)

作者： Nour Mahmoud Bahbouh Abdulelah Alhussain Hassan Hakami Yaser Mohammed Aljohani Adnan Ahmed Abi Sen Ahmad Alkhodre Aeshah Alsughayyir Department of Information and Communication Sciences Granada University Granada Spain Saudi Electronic University Al-Madinah Saudi Arabia YasRef Program Yanbu Saudi Arabia Computing and Information Technology Islamic University Al-Madinah Saudi Arabia Faculty of Computer and Information Systems Islamic University Al-Madinah Saudi Arabia College of Computer Science and Engineering Taibah University Al-Madinah Saudi Arabia

Smart cities promise a lot of well-being to their users in all areas of life through millions of applications and services. Smart services rely heavily on collecting data and the preferences of users. But on the dark side, the users' information is posed to threat and penetration during transmission or stored far in the clouds. Depending on encryption only is not sufficient if the attacker has strong resources or if the attacker is the service provider (SP) itself. In addition, changing data before sending is not a practical solution in many systems because of the adverse impact on the quality of a main service. This research presented a new idea to address the issue of protection. The proposed method enhanced the privacy and security of users' data without affecting the accuracy of service. The core of the suggested solution relies on a knowledge base of services that are managed by experts. Also, the solution depends on fog nodes to measure the level of security and privacy of users' queries without delay. Moreover, the fog nodes manage contact with SPs. Finally, the proposed method divided the SP into two, one for user queries and the other for user data. The simulation and analytical discussion on a practical case in smart cities demonstrated the superiority of the proposed approach over previous methods in the level of protection by maintaining the quality of services, and the resistance to attacks.

关键词： Resistance Privacy Data privacy Machine learning algorithms Smart cities Knowledge based systems Quality of service

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Data-Driven Approaches to Detecting Misdeliveries in Truck Logistics using GPS Data

Data-Driven Approaches to Detecting Misdeliveries in Truck L...

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IEEE International Conference on Big Data

作者： Ayumu Hidaka Ryota Shin Atsushi Tsuchiya Norihiko Nakabayashi Yukihiko Okada Yohei Shida Master’s Program in Service Engineering University of Tsukuba Tsukuba Japan Doctoral Programs in Policy and Planning Sciences University of Tsukuba Tsukuba Japan Yamato Transport Co. Ltd. Minato Japan Yamato Transport Co. Ltd. University of Tsukuba Tsukuba Japan Institute of Systems and Information Engineering Center for Artificial Intelligence Research University of Tsukuba Tsukuba Japan Institute of Systems and Information Engineering University of Tsukuba Tsukuba Japan

Misdelivery in logistic services leads to increased costs and degradation of packages. For deliveries using trucks, erroneous deliveries are prevented by checking identification numbers on packages as the packages pass through delivery points. In recent years, it has become possible to monitor real-time location by attaching GPS receivers to packages, but few concrete efforts have been made to detect anomalies using truck transport data. This study introduces a basic framework for the detection of misdeliveries and a summary of the problems in actual truck misdelivery using special medical supply delivery data provided by a major Japanese logistics company. It is shown that the system can detect erroneous deliveries with high accuracy, even for actual delivery data with coarse resolution owing to the cost of installing the equipment. This study has the potential not only to improve logistics and reduce costs, but also to solve various social problems such as driver shortages.

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Enhanced Keypoint-Based Approach for Identifying Copy-Move Forgery in Digital Images

Enhanced Keypoint-Based Approach for Identifying Copy-Move F...

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International Conference on Contemporary computing and Informatics (IC3I)

作者： Girish Singh Bisht Arpit Jain Vandna Bansla Khemraj Sharma Rudramani Bhutia Vikas Kumar Department of Computer Science & Engineering Tula’s Institute Dehradun India Department of Computer Science & Engineering Koneru Lakshamaiah Education Foundation Vadessahwaram A.P. India Shivalik Engineering College Dehradun India Department of Business Administration Doctoral Program in Management Sciences Nanhua University Dalin Chiayi Taiwan R.O.C. Department of Computer Science & Engineering Koneru Lakshmaiah Education Foundation A.P. India School Engineering and Computing Dev Bhoomi Uttrakhand University Dehradun Uttrakhand India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

Copy-move forgeries often exploit homogeneous regions in images with large-scale attacks to either highlight or conceal target objects. These manipulations are simple to execute but challenging to notice. Forgery detection techniques like Copy-Move Forgery Detection (CMFD) cannot detect these forged documents because they are unable to identify a sufficient number of effective keypoints in homogeneous areas, leading to inaccurate and inefficient results. SURF stands for Speeded-Up Robust Features and is used in this paper along with A-KAZE and Scale-Invariant Feature Transforms. According to our experiment, A-KAZE offers superior detection performance under diverse attacks, especially when it comes to large-scale attacks targeting homogeneous regions. A-KAZE is found to be more accurate than SIFT, SURF, and A-KAZE when applied to the NB-CASIA dataset, achieving detection accuracies of $\mathbf{8 9. 2 \%}, \mathbf{9 3. 9 \%}$ and $\mathbf{9 8. 9 8 \%}$.)

关键词： Accuracy Digital images Forensics Transforms Forgery Computational efficiency Reliability Informatics Software development management

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Byzantine fault tolerance for collaborative editing with commutative operations

Byzantine fault tolerance for collaborative editing with com...

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2016 IEEE International Conference on Electro information Technology, EIT 2016

作者： Zhao, Wenbing Babi, Mamdouh William, Yang Luo, Xiong Zhu, Yueqin Jack, Yang Chaomin, Luo Yang, Mary Department of Electrical Engineering and Computer Science Cleveland State University ClevelandOH44115 United States Texas Advanced Computing Center University of Texas at Austin 10100 Burnet Road AustinTX78758 United States School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China Development Research Center of China Geological Survey Ministry of Land and Resources Beijing100037 China Division of Biostatistics and Biomathematics Massachusetts General Hospital and Harvard Medical School BostonMA United States Department of Electrical and Computer Engineering University of Detroit Mercy DetroitMI48221 United States Department of Information Science George Washington Donaghey College of Engineering and Information Technology Joint Bioinformatics Program of University of Arkansas at Little Rock University of Arkansas for Medical Sciences 2801 S. University Avenue Little RockAR72204 United States

ISBN: (纸本)9781467399852

In this paper, we present a study on how to achieve Byzantine fault tolerance for collaborative editing systems with commutative operations. Recent research suggests that Conflict-free Replicated Data Types (CRDTs) can be used to construct collaborative editing systems where concurrent update operations are commutative. This new approach is shown to avoid the complex issue of conflict resolution for concurrent updates to a shared document. The shared document is often modeled as a linear text buffer where each basic element is assigned a globally unique and totally ordered identifier. The linear text buffer constructed this way would constitute as a CRDT, which would make concurrent update operations issued by different users commutative. State convergence at all users can be achieved automatically as long as the users could receive the same set of operations irrespective of their relative ordering. However, it is not straightforward to guarantee state convergence in the presence of malicious users and external adversaries. In this paper, we carefully analyze the threats towards this type of systems, and propose a lightweight solution to achieve Byzantine fault tolerance with low runtime overhead. We define a set of correctness properties for such systems and prove that the proposed Byzantine fault tolerance mechanisms guarantee these properties. © 2016 IEEE.

关键词： Fault tolerance

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Classification Based on Texture Analysis for Synthetic Hazy Image from Mount Kelud Haze Image Density

Classification Based on Texture Analysis for Synthetic Hazy ...

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International Conference on Electrical, Electronics and information engineering (ICEEIE)

作者： Fresy Nugroho Puspa Miladin Safitri A. Basid Yunifa Miftachul Arif Norizan Mat Diah Firma Sahrul Bahtiar Hani Nurhayati Fachrul Kurniawan Nurizal Dwi Priandani Dodik Arwin Dermawan Informatics Engineering Faculty of Science and Technology Universitas Islam Negeri Maulana Malik Ibrahim Malang Indonesia School of Computing Sciences College of Computing Informatics and Media Universiti Teknologi MARA Shah Alam Malaysia Faculty of Science and Technology Library and Information Science Universitas Islam Negeri Maulana Malik Ibrahim Malang Indonesia Manajemen Informatika Program Vokasi Universitas Negeri Surabaya Surabaya Indonesia

This paper classifies the simulations of homogeneous synthetic images, heterogeneous synthetic hazy images, and original hazy images taken from CCTV (Close Circuit Television) of Mt. Kelud crater using the GLCM (Gray Level Co-Occurrence Matrix) method. The average feature values obtained using the GLCM (Gray Level Co-Occurrence Matrix) method are used to compare the similarity of gray feature values of the three and then classify thin, medium, and thick images. The results for classifying thin haze, medium haze, and thick haze on the homogeneous synthetic hazy image test data obtained an accuracy value of 50%, a precision value of 46%, and a sensitivity value of 65%. As for the classification of thin, medium, and thick fog on heterogeneous synthetic hazy images, test data obtained an accuracy value of 42%, a precision value of 32%, and a sensitivity value of 48%.

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