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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Abdirahim Addawe Musaria K. Mahmood Sulaiman M. Karim Ahmad M. Nezam dept. of Energy Systems Engineering Ankara Yildirim Beyazit University Ankara Turkiye dept. of Medical Instr. Tech. Eng. Jannat Iraq College Ramadi Iraq dept. of Computer Technology Engineering Al-Hadba'a University College Mosul Iraq dept. of Computer Engineering Karabuk Üniversitesi Karabuk Turkey

ISBN: (数字)9798350374131

ISBN: (纸本)9798350374148

Improving the efficiency of photovoltaic (PV) sys-tems has emerged as a critical priority. Achieving optimal power production from solar modules requires operating at maximum power point (MPP) under various climatic conditions. This paper describes the operation of the Perturb and Observe (P&O) as an efficient Maximum Power-Point Tracking (MPPT) algorithm. Simulations of various operating scenarios are performed using MATLAB to assess the algorithm efficiency under both Standard Test Conditions (STC) and Partial Shading Conditions (PSC). This study considers factors such as output power efficiency, time response, and steady-state power oscillation. The simulation results show a fast-tracking, lower oscillation in the case of STC but fail to track the MPP in the case of multi-panel under PSC.

关键词： Photovoltaic systems Solar irradiance Simulation Production Software Steady-state Time factors

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EVALUATING MULTIPLE MODELS USING LABELED AND UNLABELED DATA

arXiv

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arXiv 2025年

作者： Shanmugam, Divya Sadhuka, Shuvom Raghavan, Manish Guttag, John Berger, Bonnie Pierson, Emma CSAIL Massachusetts Institute of Technology United States Dept. of Computer Science Cornell Tech United States Sloan School of Management Massachusetts Institute of Technology United States Dept. of Mathematics Massachusetts Institute of Technology United States Dept. of EECS UC Berkeley United States

It remains difficult to evaluate machine learning classifiers in the absence of a large, labeled dataset. While labeled data can be prohibitively expensive or impossible to obtain, unlabeled data is plentiful. Here, we introduce Semi-Supervised Model Evaluation (SSME), a method that uses both labeled and unlabeled data to evaluate machine learning classifiers. SSME is the first evaluation method to take advantage of the fact that: (i) there are frequently multiple classifiers for the same task, (ii) continuous classifier scores are often available for all classes, and (iii) unlabeled data is often far more plentiful than labeled data. The key idea is to use a semi-supervised mixture model to estimate the joint distribution of ground truth labels and classifier predictions. We can then use this model to estimate any metric that is a function of classifier scores and ground truth labels (e.g., accuracy or expected calibration error). We present experiments in four domains where obtaining large labeled datasets is often impractical: (1) healthcare, (2) content moderation, (3) molecular property prediction, and (4) image annotation. Our results demonstrate that SSME estimates performance more accurately than do competing methods, reducing error by 5.1× relative to using labeled data alone and 2.4× relative to the next best competing method. SSME also improves accuracy when evaluating performance across subsets of the test distribution (e.g., specific demographic subgroups) and when evaluating the performance of language models. © 2025, CC BY.

关键词： Adversarial machine learning

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U-Net Based Image Segmentation Techniques for Development of Non-Biocidal Fouling-Resistant Ultra-Thin Two-dimensional (2D) Coatings

U-Net Based Image Segmentation Techniques for Development of...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Gurung, Bichar Dip Shrestha Devadig, Ramesh Do, Tuyen Gadhamshetty, Venkataramana Gnimpieba, Etienne Z. University of South Dakota Dept. of Computer Science Vermillion United States South Dakota School of Mines and Technology Dept. of Civil and Environmental Engineering Rapid City United States University of South Dakota Dept. of Biomedical Engineering Vermillion United States

ISBN: (纸本)9781665468190

Bacterial adhesion to the metallic surfaces creates a complex biofilm network, resulting in many problems like corrosion and fouling. Precise quantitative analysis of the surface coverage of cells can be vital in decoding biofilm-related issues. We present a deep learning-based approach to automate the microbes' segmentation from the Scanning Electron Microscope (SEM) images of biofilm developed on coated multilayer graphene nickel samples. We collected SEM images from multilayer graphene nickel exposed to Oleidesulfovibrioalaskensis (OA-G20) for 30 days. Then we manually annotated the microbes with the help of subject expertise and trained a deep-learning U-Net architecture. In order to deal with larger image sizes, we perform patched-based image training and techniques for predicting segmentation masks over the larger image. Intersection over Union (IOU) was calculated for the evaluation of the performance of the model. After training the image for multiple epochs and extracting the optimal model parameters from the learning curve, we were able to get 70.64% mean IOU score. The patched-based technique for image training and image inference showed promising output during the segmentation. © 2022 IEEE.

关键词： Biofilms

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Experimental Thermal Model-Based SOC Estimation Algorithm for Lithium-Ion Phosphate Batteries Considering Internal Temperature

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Transactions of the Korean Institute of Electrical Engineers 2024年第12期73卷 2289-2296页

作者： Kim, Dong Hwan Ko, Jeong Soo Kim, Do Hyeon Lim, Jong-Hun Lim, Je Yeong Lee, Jaein Lee, Byoung Kuk Dept. of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of Advanced Defense Science & Technology Research Institute Agency for Defense Development Korea Republic of

This paper proposes a method for estimating the State of Charge (SOC) of lithium iron phosphate (LFP) batteries based on an experimental thermal model that considers internal battery temperatures. The proposed algorithm involves selecting a thermal model and suggesting methods for extracting parameters from the chosen model. Using the proposed thermal model, the algorithm compensates for temperature differences in cold and hot environments, thereby correcting the internal temperature to estimate SOC accurately. The effectiveness of the proposed algorithm is verified by estimating the SOC of electric vehicle LFP batteries and comparing the accuracy before and after correction. Copyright © The Korean Institute of Electrical Engineers.

关键词： State of charge

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Spintronic Device Potential in Neuromorphic Computing from a Device-Circuit Modeling Perspective

Spintronic Device Potential in Neuromorphic Computing from a...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Suman, Bashetty Deepika, N.M. Rajalakshm, B. Singh, Jay Nijhawan, Ginni Riyadh, Mazin Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Institute of Aeronautical Engineering Hyderabad Dundigal India New Horizon College of Engineering Department of Computer Science Bangalore India Gl Bajaj Institute of Technology & Management Dept. of Eee U.P. Greater Noida India Lovely Professional University Phagwara India AL-Hameed College of Engineering Technology National University of Science and Technology Dhi Qar Iraq

ISBN: (纸本)9798350357769

Recent years have seen tremendous progress in the area of neuromorphic computing, which seeks to mimic the parallel processing power and energy efficiency of the human brain. From the standpoint of device-circuit modelling, this work explores how spintronic devices might improve the scalability and performance of neuromorphic systems. Utilizing the inherent spin of the electron and the magnetic moment that goes along with it, spintronics has special benefits in terms of non-volatility, low power consumption, and high integration density. We provide a thorough model that accurately depicts the complex behaviour of spintronic components and how they are incorporated into neuromorphic circuits. Our results suggest that spintronic devices may improve the overall efficiency of neuromorphic systems by reducing the power-delay product substantially. Furthermore, it is possible to realize synaptic capabilities and plasticity processes with better accuracy by using the intrinsic features of spintronic devices, such as magnetoresistance and spin-transfer torque. Additionally, the research identifies problems with device variability and suggests possible fixes. In summary, spintronic devices have great potential to transform the field of neuromorphic computing if they are properly modelled and integrated. © 2023 IEEE.

关键词： Device-Circuit Modeling Magnetoresistance. Top of Form Neuromorphic Computing Spintronics Synaptic Plasticity

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Importance of Machine Learning and Deep Learning Algorithms in Earthquake Prediction: A Review

Importance of Machine Learning and Deep Learning Algorithms ...

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International Symposium on Digital Forensic and Security (ISDFS)

作者： Güneş Gürsoy Asaf Varol Ahad Nasab Dept. of Computer Engineering College of Engineering and Natural Sciences Maltepe University İstanbul Turkey Dept. of Engineering Management and Technology College of Engineering and Computer Science The University of Chattanooga Chattanooga TN US

Earthquakes are the leading natural disasters that have caused loss of life and property since the formation of the world. Machine learning and deep learning are frequently used in studies for earthquake prediction. This article consists of a compilation of studies using machine learning and deep learning algorithms. In the article, studies on topics such as earthquake magnitude estimation, signal discrimination, electron density estimations in the ionosphere, examination of radon gas anomalies using machine learning and deep learning algorithms are included. The studies in this paper show that Deep Learning algorithms are used more frequently in earthquake forecasting. It is expected that Deep Learning will provide more successful results in future studies due to its ability to work with larger data sets compared to Machine Learning and its ability to improve itself from errors.

关键词： Deep learning Machine learning algorithms Earthquakes Radon Neural networks Estimation Prediction methods

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Enhancing Explainability and Transparency in Deep Learning Models for Healthcare Applications

Enhancing Explainability and Transparency in Deep Learning M...

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Data Science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： V. Indhumathi Veeramalai Sankaradass V. K Manindra Manish K. Jagadeesh Dept. of Computer Science and Engineering Chennai Institute of Technology Chennai India Bachelor of Medicine and Bachelor of Science Dhanalakshmi Srinivasan Institute of Medical Sciences and Hospital Perambalur India Dept. of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Recently, it has been observed that deep learning models can be very useful in handling healthcare facilities mostly medical diagnosis and management. However, they are not interpretable and transparent enough to be accepted and used in the clinical environments where the matching reasoning is important. This paper proposes a new strategy to improve explainability of deep learning approaches when used in healthcare data set. We introduce an IDNN design with explainable layers and postdoc approaches like SHAP and LIME for visualization and explanation of model predictions. Our framework is applied to multiple healthcare datasets such as MIMICIII and ChestXray14 where we will compare the traditional method with the proposed method by training accuracy and explainability evaluation metrics. Results indicate that this causes minimal compromise in accuracy on the other hand, other explainability measures of model fidelity, and comprehensibility reveal significant gains, signifying the potential of this method in advancing the use of AI solutions in healthcare contexts.

关键词： Deep learning Training Hands Accuracy Explainable AI Statistical analysis MIMICs Medical services Predictive models Medical diagnosis

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Data Security Enhancement in Cloud Computing by Proposing A DKE Encryption Protocol 23

Data Security Enhancement in Cloud Computing by Proposing A ...

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Proceedings of the 2023 International Conference on Advances in Artificial Intelligence and Applications

作者： Sahar Ebadinezhad Halmat Ayub Abdulmajed Department of Computer Information Systems Near East University North Cyprus and Computer Information Systems Research and Technology Center (CISRT) Near East University North Cyprus Dept. Engineering and Computer Systems Lebanese French University (LFU) Iraq

ISBN: (纸本)9798400708268

This study uses cryptography to tackle the important problem of data security in cloud computing. Two keys are used in the Dual Key Encryption (DKE) method for both encryption and decryption. DKE employs a public key for the first encryption round before cloud upload and a user-only private key for the second round of encryption. Decryption operates oppositely. When tested and simulated with different file sizes on a CloudAnalyst simulator, DKE operates faster and more efficiently than cryptographic methods such as Triple DES (3DES), AES, RSA, and DES. Thanks to developments in information technology, cloud computing has made several services available online. Data security assurance is still very difficult to achieve, nevertheless. In this context, cryptography is essential and has led to the creation of the novel DKE technology. This technology is not only far more efficient than well-known cryptographic techniques such as 3DES, but it also improves data security.

关键词： Cloud Services Cryptography Data Integrity Data Security Dual Key Encryption

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An Enhancemnt of Low-Light Images Using Hierarchical Pyramid Attentive Network and Hierarchical Feature Fusion 2

An Enhancemnt of Low-Light Images Using Hierarchical Pyramid...

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2nd International Conference on Data Science and Information System, ICDSIS 2024

作者： Suma, K.G. Nagendar, Y. Habelalmateen, Mohammed I. Revanasiddappa Halvi, Srinivas Vit University School Of Computer Science And Engineering Dept. Of Ai & Ml Andhra Pradesh India Sr University School Of Computer Science And Engineering Telangana Warangal India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute Of Technology Department Of Aiml Bengaluru India Dayananda Sagar College Of Engineering Department Of Medical Electronics Engineering Bengaluru India

ISBN: (纸本)9798350381665

The development of the low-light images assists efficiently in high visioned tasks which often fails in the poor illumination conditions. Most of the old methods, applied has enhanced the low-lighted degrade images which resulted in the undesirable enhancement including the intensive noise, blurred details and slanted color. This paper is used for finding alignment to an end-to-end image along with enhancement of the perceptual information by pixel wise. To reduce the differences in style, Hierarchical Pyramid Attentive Network (HPANet) was used, which results in the betterment of the illuminations and the reveal exposed information's. After aligned images, a hierarchy pyramid enhancements subnetwork is used for optimizing the quality of the image, which helps for removing the noises, results in enhancement low light images. A Hierarchical Feature Fusion (HFF) module is proposed which results in the high resolution of the images in perceptual qualities. Extensive experiments have also performed that effectiveness of HPANet-HFF methods on various datasets have outdone the existing methods in reproduction of the color. According to the results, the proposed HPANet-HFF obtained MAE and RMSE of 106.5 and 181.5 which is superior over other existing architectures. © 2024 IEEE.

关键词： Cell proliferation

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Customer Segmentation Using Hierarchical Clustering

Customer Segmentation Using Hierarchical Clustering

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International Conference for Convergence of technology (I2CT)

作者： Areeba Afzal Laiba Khan Muhammad Zunnurain Hussain Muhammad Zulkifl Hasan Muzzamil Mustafa Aqsa Khalid Rimsha Awan Farhan Ashraf Zohaib Ahmed Khan Arslan Javaid Department of Computer Engineering Information Technology University Lahore Punjab Pakistan Dept. of Computer Science Bahria University Lahore Campus Department of Computer Science Faculty of Information Technology University of Central Punjab Lahore Pakistan Department of Computer Science National College of Business Administration and Economics Lahore Pakistan Information Technology University Lahore Pakistan Department of Computer Science National College of Business Administration & Economics Lahore Pakistan

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

In the dynamic landscape of retail, understanding customer behavior is paramount for businesses seeking to optimize marketing strategies and enhance the shopping experience. This study explores the utilization of hierarchical clustering techniques for mall customer segmentation, with a focus on the paper titled ’MALL CUSTOMER SEGMENTATION USING MACHINE LEARNING’ as the benchmark. Our dataset encompasses a diverse range of mall customers, spanning demographics and behavioral attributes. Hierarchical clustering systematically groups customers into clusters, revealing distinct segments within the mall’s customer base. A comprehensive analysis of these clusters unveils profound insights into customer tendencies, preferences, and purchasing habits. These insights form a solid foundation for tailored marketing campaigns, personalized recommendations, and resource allocation within the mall. The study contributes significantly to customer analytics, providing retailers with a powerful tool to gain a competitive edge in the retail sector. By leveraging hierarchical clustering for mall customer segmentation, businesses can enhance customer satisfaction, drive sales, and foster lasting customer relationships. This paper underscores the importance of data-driven methodologies in understanding customer behavior and offers a practical framework for businesses to harness the potential of hierarchical clustering for strategic decision-making in the retail industry.

关键词： Industries Decision making Customer satisfaction Benchmark testing Solids Resource management Data mining

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