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P-ROCK: A Sustainable Clustering Algorithm for Large Categorical Datasets

Intelligent Automation & Soft Computing 2023年第1期35卷 553-566页

作者： Ayman Altameem Ramesh Chandra Poonia Ankit Kumar Linesh Raja Abdul Khader Jilani Saudagar Department of Computer Science and Engineering College of Applied Studies and Community ServicesKing Saud UniversityRiyadh11533Saudi Arabia Department of Computer Science CHRIST(Deemed to be University)Bangalore560029India Department of Computer Engineering and Applications GLA UniversityMathuraUPIndia Department of Computer Application Manipal University JaipurRajasthan303007India Information Systems Department Imam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia

Data clustering is crucial when it comes to data processing and *** new clustering method overcomes the challenge of evaluating and extracting data from big *** or categorical data can be *** clustering methods favor numerical data clustering and ignore categorical data *** recently,the only way to cluster categorical data was to convert it to a numeric representation and then cluster it using current numeric clustering ***,these algorithms could not use the concept of categorical data for *** that,suggestions for expanding traditional categorical data processing methods were *** addition to expansions,several new clustering methods and extensions have been proposed in recent *** is an adaptable and straightforward algorithm for calculating the similarity between data sets to cluster *** paper aims to modify the algo-rithm by creating a parameterized version that takes specific algorithm parameters as input and outputs satisfactory cluster *** parameterized ROCK algorithm is the name given to the modified algorithm(P-ROCK).The proposed modification makes the original algorithm moreflexible by using user-defined parameters.A detailed hypothesis was developed later validated with experimental results on real-world datasets using our proposed P-ROCK algorithm.A comparison with the original ROCK algorithm is also *** results show that the proposed algorithm is on par with the original ROCK algorithm with an accuracy of 97.9%.The proposed P-ROCK algorithm has improved the runtime and is moreflexible and scalable.

关键词： ROCK K-means algorithm clustering approaches unsupervised learning K-histogram

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Deep Learning in Remote Sensing for Climate-Induced Disaster Resilience: A Comprehensive Interdisciplinary Approach

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Remote Sensing in Earth Systems sciences 2024年第1期8卷 145-160页

作者： Padmaja, S.M. Naveenkumar, R. Kumari, N. P. Lavanya Pimo, Er. S. John Bindhu, M. Konduri, Bhagavan Jangir, Pradeep Department of Electrical and Electronics Engineering Shri Vishnu Engineering College for Women Bhimavaram India Department of Computer Science and Engineering Chandigarh Colleges of Engineering Chandigarh Group of Colleges Jhanjeri Punjab Mohali 140307 India Department of Computer Science and Systems Engineering AUCE Andhra University Visakhapatnam India Department of Computer Science and Engineering St. Xavier’s Catholic College of Engineering Nagercoil India Department of ECE Sri Venkateswara College of Engineering Sriperumbudur India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan

Climate-induced disasters pose significant threats to human lives, infrastructure, and ecosystems. Deep learning techniques, combined with remote sensing, offer powerful tools for predicting, detecting, and mitigating the impacts of such disasters. This study presents a comprehensive interdisciplinary approach using a hybrid cascaded CNN-RNN model to perform spatio-temporal analysis, enhanced image segmentation with U-Net and SegNet architectures, and a hybrid CNN-LSTM-XGBoost ensemble for multi-hazard prediction. The proposed CNN-RNN model achieves an accuracy of 96.78%, significantly outperforming traditional models in predicting flood-prone areas, wildfires, and droughts. Key metrics such as Intersection over Union (IoU), precision, recall, and F1-scores are used to evaluate model performance, highlighting the model’s ability to accurately identify disaster-prone areas and improve disaster resilience. This research not only demonstrates the efficacy of deep learning for climate-induced disaster prediction but also paves the way for future integration of socio-economic data to refine disaster risk assessment. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Climate-induced disasters CNN-RNN Deep learning Disaster resilience Hybrid ensemble Multi-hazard prediction Remote sensing SegNet Spatio-temporal analysis U-Net

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A Comparison of Selected Agent-Based Modelling Frameworks 15th

A Comparison of Selected Agent-Based Modelling Frameworks

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15th International Conference on Parallel Processing and applied mathematics, PPAM 2024

作者： Gacek, Paulina Bocheńczyk, Dominika Krajewski, Maciej Kruczek, Bartosz Lazarz, Krzysztof Magnuszewski, Pawel Zięciak, Antoni Kamiński, Filip Wąs, Jaroslaw Department of Applied Computer Science Faculty of Electrical Engineering Automatics IT and Biomedical Engineering AGH University of Krakow Mickiewicza 30 Krakow30-059 Poland

ISBN: (纸本)9783031857027

In recent years, a variety of new frameworks streamlining the process of agent-based modeling has emerged. These frameworks serve different purposes and each offers a unique set of features. In this practical comparative study, we evaluate the performance of various ABM frameworks through a series of benchmark simulations. By comparing the distinct functionalities offered by these tools, we aim to assist in the selection of an appropriate ABM toolkit for developing system models. This review presents a concise overview of seven popular agent-based modeling tools aiming to inspire further exploration and investigation into this subject. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Digital elevation model

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Optimizing Virtual Machine Downtime in Live Migration using the Least Frequently Used Service-based Pre-copy Algorithm

Optimizing Virtual Machine Downtime in Live Migration using ...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Shabariram, C.P. Shanthi, N. Ponnusamy, P. Priya Prema, R. Sowmiya Priya, R.S. PSG Institute of Technology and Applied Research Department of Computer Science and Engg Coimbatore India Kongu Engineering College Department of Computer Science and Engg Erode India

ISBN: (纸本)9798350348217

Cloud computing has been considered the most reliable and safe technology among many cloud users. Virtual machine migration is a crucial component of cloud computing technology. A Virtual machine is transferred from one host machine to another during the migration process, which is accomplished by repeatedly copying the memory pages available in the virtual disk. The time required for the movement of secondary storage, main memory, process states, and network connectivity, as well as the maintenance of their accuracy, must be considered for the efficient migration of virtual machines from one physical host to another in the cloud service provider. The most robust mechanism for system maintenance, resilience system, balancing the load of the service provider, and power-saving is the live migration of virtual machines between the nodes in the data center. So many services are running in the virtual machines during live migration;thus, it needs to take place with the least amount of downtime possible to prevent service degradation. The memory pages in the virtual storage must be copied to the other host using the least frequently used service-based pre-copy algorithm to optimize the downtime. The effectiveness of the suggested solution was evaluated in comparison to OpenNebula and OpenStack, two cloud computing platforms. The least frequently used service-based pre-copy algorithm reduces the amount of virtual machine downtime during live migration. © 2023 IEEE.

关键词： Virtual machine

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Highly Efficient Three-Level AC-AC Converter with Identical In-Phase and Antiphase Buck-Boost Operations

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IEEE Transactions on Power Electronics 2024年第8期39卷 9929-9942页

作者： Ahmed, Hafiz Furqan Khan, Ashraf Ali Al Zaabi, Omar Mousavi, Seyyed Mohammad Javad Babaei, Ebrahim National Sun Yat-Sen University Department of Electrical Engineering Kaohsiung804 Taiwan Memorial University of Newfoundland Department of Electrical and Computer Engineering Faculty of Engineering and Applied Science St. John'sNLA1C 5S7 Canada Khalifa University Advanced Power and Energy Center Department of Electrical Engineering and Computer Science Abu Dhabi127788 United Arab Emirates University of Tabriz Faculty of Electrical and Computer Engineering Tabriz51666 Iran Near East University Engineering Faculty Nicosia99138 Turkey

In this article, a new coupled-inductors based three-level bipolar buck-boost ac-ac converter is proposed. The proposed converter can produce highly efficient and symmetric in-phase and antiphase buck and boost modes of operation with much lower component voltage and current stresses. This reduces the operation and design complexity, and helps to achieve a high-efficiency operation. The high-frequency modulating switches of the proposed converter are implemented with coupled-inductor-based dual-buck phase leg, producing three-level input and output voltages, and providing inherent protection from voltage-source short-circuit issue during switch transitions. The other salient features of the proposed converter are no-commutation issue, no need for RC snubbers or dedicated safe-commutation algorithms, provision of high-quality and continuous input/output currents, and support for nonresistive loads. An in-depth circuit analysis of the proposed converter is provided based on the proposed switch modulation strategies. The guidelines for component design/selection are discussed, followed by the comparisons with state-of-the-art three-level ac-ac converters. Finally, practical circuit verifications are performed on a laboratory-assembled prototype. © 1986-2012 IEEE.

关键词： Buck-Boost converter

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Integrating AI, IoT, and Drones for Sustainable Apple Orchard Monitoring in Society 5.0 2nd

Integrating AI, IoT, and Drones for Sustainable Apple Orch...

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2nd International Conference on applied Algorithms, ICAA 2025

作者： Datta, Ankana Paul, Sukalpa Pal, Anidipta Biswas, Sounav Bag, Anil Kumar Sengupta, Diganta Department of Computer Science and Engineering - AIML Heritage Institute of Technology Chowbaga Road Kolkata700107 India Department of Applied Electronics and Instrumentation Engineering Heritage Institute of Technology Chowbaga Road Kolkata700107 India Department of Computer Science and Engineering Heritage Institute of Technology Chowbaga Road Kolkata700107 India

ISBN: (纸本)9783031845420

Sustainable smart agriculture forms one of the focal points in Society 5.0. We propose an AI-IOT enabled framework capable of processing multi-modal data for apple orchard monitoring. Real-time data acquisition is done from ground sensors as well as drone fitted cameras. The ground sensors monitor soil moisture, pH levels, nutrient composition (nitrogen, phosphorous, and potassium), ambient temperature, and humidity, while ESP32-CAMs fitted on drones capture images of apples, leaves, and trees. IOT enabled Unmanned Aerial Vehicle (UAV) as well as the ground sensor framework feed the data to the cloud. YOLOv8 and ResNet152 have been used to process the images for classifying the health of the apple plants. Machine learning models predict the farm yield using the ground sensor data reflecting the soil conditions. Our framework fares better than prior art in terms of accuracy. Although existing literature exhibits processing of soil data for prediction of health and yield, our study takes into consideration three further nutrient components - nitrogen, phosphorous, and potassium. Our study shows accuracy of 98.19% (apple counting) 53.66% (apple classification) 96.00% (leaf classification). To the entirety of our understanding, this study is the first to use multi-modal data inclusive of extended soil nutrients. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Orchards

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A Neuro-Fuzzy Approach to MOPP Open Time Prediction for Chemical Threats in Low-Intensity Conflicts

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International Journal of Fuzzy System Applications 2022年第1期11卷 1-17页

作者： Kumar, Vijay Tyagi, Lakshya Department of Applied Mathematics Amity Institute of Applied Sciences Amity University Noida India Department of Computer Science and Engineering ASET Amity University Noida India

CBRN (chemical, biological, radiological, and nuclear) weapons systems hold the potential to cause great damage to a fighting unit. Chemical warfare agents are cheaper and easier to produce and deploy. Mission-oriented protective posture (MOPP) equipment is used to isolate soldiers from the toxic environment in case of a chemical attack. Sustained wearing of MOPP overgarments can put soldiers at the risk of dehydration and heat stress, while reducing their capability to carry out labour-intensive tasks efficiently. A hierarchal adaptive neuro-fuzzy inference system (HANFIS) model is proposed to predict worst case MOPP open times for surfaces covered with chemical agent resistant coating (CARC). The model can provide decision support for commanders when determining MOPP open or unmasking time. From the HANFIS simulation results, it was concluded that the model can accurately predict MOPP open times according to data present in military field manuals in a manner that is computationally efficient and easy to interpret based on linguistic rules, and most importantly, easy to customize according to the needs of in-theatre commander based on intelligence on the enemy's arsenal of CW agents and weather conditions of their unit's location. This work adds to the body of evidence that soft computing techniques can be effectively applied to the design of decision support systems for defence sciences. Copyright © 2022, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

关键词： Chemical warfare

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High-Dynamic Range ADC for Finite-Rate-of-Innovation Signals 48

High-Dynamic Range ADC for Finite-Rate-of-Innovation Signals

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Mulleti, Satish Eldar, Yonina C. Bombay Department of Electrical Engineering India Weizmann Institute of Science Faculty of Math and Computer Science Israel

ISBN: (纸本)9781728163277

Modulo folding can be used to sample high-dynamic range signals without increasing the dynamic range of the sampler. Specifically, folding is used prior to sampling and then the folded signal is sampled. After sampling, unfolding algorithms are used to compute the true samples (up to a constant factor) from the folded ones. In this work, we consider a modulo sampling framework for finite rate of innovation (FRI) signals which are used to model signals in time of flight imaging. We suggest compactly supported sum-of-sincs filter as a sampling kernel prior to modulo folding and sampling. We derive conditions on the sampling rate and filter coefficients which lead to unfolding of the samples up to an unknown constant. We then propose a modified annihilating filter approach that can uniquely determine the FRI parameters from the unfolded samples with a constant unknown offset. We show that the proposed framework outperforms existing techniques in the presence of noise. © 2023 IEEE.

关键词： Signal sampling

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Assessment of processing time and measurement accuracy of different phase compensation methods in Quantitative Phase Imaging via Digital Holographic Microscopy applied to Biological Specimens Holographic Microscopy

Assessment of processing time and measurement accuracy of di...

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3D Image Acquisition and Display: Technology, Perception and Applications, 3D 2024 - Part of Optica Imaging Congress

作者： Obando-Vásquez, Sofía Doblas, Ana Trujillo, Carlos Applied optics group School of Applied Science and Engineering Universidad EAFIT Medellín Colombia Department of Electrical and Computer Engineering University of Massachusetts DartmouthMA United States

Six different methods for phase compensation in Digital Holographic Microscopy are compared using a calibrated test target and a Toxocara canis larva sample regarding processing time, measurement accuracy, and usefuln...

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An EfficientNet to Classify Monkeypox-Comparable Skin Lesions Using Transfer Learning

An EfficientNet to Classify Monkeypox-Comparable Skin Lesion...

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2023 IEEE International Conference on Omni-Layer Intelligent Systems, COINS 2023

作者： Hossen, Md. Rifat Alfaz, Nazia Sami, Adnan Tanim, Sharia Arfin Bin Sarwar, Talha Islam, Md. Kamrul Department of Computer Science and Engineering Dhaka Bangladesh Department of Computer Science Dhaka Bangladesh College of Computing and Applied Science Universiti Malaysia Pahang Faculty of Computing Pekan Malaysia

ISBN: (纸本)9798350346473

Monkeypox is an infectious illness caused by the DNA-based monkeypox virus, which has raised public health concerns due to its rapid transmission to over 50 countries. Direct physical interaction with infected humans or infected animals is the main reason behind the spread of this virus. The appearance of skin problems such as smallpox and rashes are the most frequently reported symptoms of this virus. Since cases of monkeypox are increasing rapidly around the world, stopping the spread of this zoonosis by providing early diagnosis and treatment is crucial before the emergence of a pandemic similar to COVID-19. In this study, we aim to propose a transfer learning-based approach using the EfficientNet-B0 architecture to identify monkeypox subjects by using skin lesion image samples. However, distinguishing monkeypox from other comparable infectious skin illnesses like chickenpox and measles is challenging. Therefore, additionally, this study identifies other diseases that also cause blisters and rashes on the skin, like chickenpox, and measles. During the data distribution phase, 5-fold cross-validation is used to validate the work's reliability by assuring that every sample is utilized for training and validation. For the evaluation of the model's classification performance, accuracy and loss are recorded for each training epoch. Moreover, precision, recall, F1-score, and confusion matrix are generated upon completion of the model training. This proposed approach is experimented on a public dataset and has shown remarkable performance by providing an overall 96.53% classification accuracy, 96.57% precision, 96.53% recall, and 96.52% F1-score. © 2023 IEEE.

关键词： Viruses

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