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2024 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2024

作者： Gade, Rachana Bornare, Aditya Jog, Saurabh Joshi, Amit Sawant, Suraj Department of Computer Science and Engineering School of Computational Sciences COEP Technological University Pune India

ISBN: (纸本)9798331533137

Memory prefetching plays a vital role in enhancing processor performance, with modern processors using various prefetching methods to manage data access patterns efficiently. Traditional prefetchers work well with predetermined patterns but struggle with the unpredictability of modern applications. Advanced techniques, especially those employing machine learning, aim to predict and prefetch data based on complex spatiotemporal patterns, yet they face significant challenges like the class explosion problem and labeling issues. This study proposes a Bidirectional Long Short-Term Memory (BiLSTM) deep learning model to dynamically adapt to intricate access patterns, addressing these limitations by leveraging features, localization, and semantic locality to improve prediction accuracy and adaptability. By integrating insights from traditional and machine learning-based approaches, this model seeks to optimize data prefetching, advancing memory system efficiency and ultimately enhancing overall processor performance. These contributions provide a robust framework for designing adaptive and efficient processors that can meet the demands of modern computing environments. ©2024 IEEE.

关键词： Deep learning

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A Survey on Enhancing Image Captioning with Advanced Strategies and Techniques

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computer Modeling in Engineering & sciences 2025年第3期142卷 2247-2280页

作者： Alaa Thobhani Beiji Zou Xiaoyan Kui Amr Abdussalam Muhammad Asim Sajid Shah Mohammed ELAffendi School of Computer Science and Engineering Central South UniversityChangsha410083China Electronic Engineering and Information Science Department University of Science and Technology of ChinaHefei230026China EIAS Data Science Lab College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia

Image captioning has seen significant research efforts over the last *** goal is to generate meaningful semantic sentences that describe visual content depicted in photographs and are syntactically *** real-world applications rely on image captioning,such as helping people with visual impairments to see their *** formulate a coherent and relevant textual description,computer vision techniques are utilized to comprehend the visual content within an image,followed by natural language processing *** approaches and models have been developed to deal with this multifaceted *** models prove to be stateof-the-art solutions in this *** work offers an exclusive perspective emphasizing the most critical strategies and techniques for enhancing image caption *** than reviewing all previous image captioning work,we analyze various techniques that significantly improve image caption generation and achieve significant performance improvements,including encompassing image captioning with visual attention methods,exploring semantic information types in captions,and employing multi-caption generation ***,advancements such as neural architecture search,few-shot learning,multi-phase learning,and cross-modal embedding within image caption networks are examined for their transformative *** comprehensive quantitative analysis conducted in this study identifies cutting-edgemethodologies and sheds light on their profound impact,driving forward the forefront of image captioning technology.

关键词： Image captioning semantic attention multi-caption natural language processing visual attention methods

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SDN-Enabled Content Dissemination Scheme for the Internet of Vehicles

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computers, Materials & Continua 2023年第5期75卷 2383-2396页

作者： Abida Sharif Muhammad Imran Sharif Muhammad Attique Khan Nisar Ali Abdullah Alqahtani Majed Alhaisoni Ye Jin Kim Byoungchol Chang Department of Computer Science COMSATS University IslamabadPakistan Department of Computer Science HITEC University TaxilaPakistan College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-KharjSaudi Arabia Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea Center for Computational Social Science Hanyang UniversitySeoul04763Korea

The content-centric networking(CCN)architecture allows access to the content through name,instead of the physical location where the content is stored,which makes it a more robust and flexible content-based ***,in CCN,the broadcast nature of vehicles on the Internet of Vehicles(IoV)results in latency and network *** IoVbased content distribution is an emerging concept in which all the vehicles are connected via the *** to the high mobility of vehicles,however,IoV applications have different network requirements that differ from those of many other networks,posing new *** this,a novel strategy mediator framework is presented in this paper for managing the network resources ***-defined network(SDN)controller is deployed for improving the routing flexibility and facilitating in the interinteroperability of heterogeneous devices within the *** to the limited memory of edge devices,the delectable bloom filters are used for caching and ***,the proposed scheme is compared with the existing variants for validating its effectiveness.

关键词： Internet of vehicles(IOV) content dissemination multi mediator data traffic management

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Recent Advancements of computer Vision in Healthcare: A Systematic Review

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IEIE Transactions on Smart Processing and Computing 2024年第6期13卷 563-571页

作者： Islam, Mahmudul Nayan, Nasim Mahmud Islam, Ashraful Sikder, Sankar Rashel, Masud Rana Alam, Md. Zahangir Department of Computer Science and Engineering Independent University Bangladesh Dhaka1229 Bangladesh Center for Computational & Data Sciences Independent University Bangladesh Dhaka1229 Bangladesh Department of Mathematics University of Louisiana LafayetteLA70503 United States Department of Mechatronics Engineering University of Évora Évora Portugal

The advancements in computer Vision (CV) techniques have demonstrated significant promise in healthcare applications. The adoption of CV within the healthcare sector has drawn considerable attention. The aim of this study is to systematically review and synthesize the recent advancements and applications of CV techniques in various domains of healthcare. 125 papers were selected initially, and after gradual filtering, 20 papers were selected for the final study. In this study, we have identified five medical domains such as disease detection, drug discovery, surgical procedures, human identity decoding, and remote patient monitoring where CV applications are being successfully implemented. Among these domains, the use of CV in surgical assistance is notable. It capitalizes on the precision and efficiency offered by CV. Deep Learning (DL) models show adaptability and accuracy in medical imaging. The combination of computer vision and sensors enhances real-time surgical skills assessment. This study revealed that CV applications can be utilized for predictive analytics and personalized patient treatments. Standardized performance metrics, ethics, and data governance are crucial for responsible computer vision deployment in healthcare. Copyrights © 2024 The Institute of Electronics and Information Engineers.

关键词： Deep learning

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Sequence to Graph Alignment Using Gap-Sensitive Co-linear Chaining 27th

Sequence to Graph Alignment Using Gap-Sensitive Co-linear C...

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27th International Conference on Research in computational Molecular Biology, RECOMB 2023

作者： Chandra, Ghanshyam Jain, Chirag Department of Computational and Data Sciences Indian Institute of Science KA Bangalore560012 India

ISBN: (纸本)9783031291180

Co-linear chaining is a widely used technique in sequence alignment tools that follow seed-filter-extend methodology. It is a mathematically rigorous approach to combine short exact matches. For co-linear chaining between two sequences, efficient subquadratic-time chaining algorithms are well-known for linear, concave and convex gap cost functions [Eppstein et al. JACM’92]. However, developing extensions of chaining algorithms for directed acyclic graphs (DAGs) has been challenging. Recently, a new sparse dynamic programming framework was introduced that exploits small path cover of pangenome reference DAGs, and enables efficient chaining [Makinen et al. TALG’19, RECOMB’18]. However, the underlying problem formulation did not consider gap cost which makes chaining less effective in practice. To address this, we develop novel problem formulations and optimal chaining algorithms that support a variety of gap cost functions. We demonstrate empirically the ability of our provably-good chaining implementation to align long reads more precisely in comparison to existing aligners. For mapping simulated long reads from human genome to a pangenome DAG of 95 human haplotypes, we achieve precision while leaving reads unmapped. Implementation: https://***/at-cg/minichain. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Cost functions

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Smoothed-LSTM: Advancing spatio-temporal NDVI prediction in semi-automated dataset for rice crop

Smoothed-LSTM: Advancing spatio-temporal NDVI prediction in ...

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2024 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing, MIGARS 2024

作者： Dey, Anamika Sarkar, Somrita Kumar, Debajyoti Mondal, Arijit Mitra, Pabitra Indian Institute of Technology Kharagpur Centre For Computational And Data Sciences Kharagpur India Indian Institute of Technology Department of Computer Science And Engineering Patna Bihta India Indian Institute of Technology Kharagpur Department of Computer Science And Engineering Kharagpur India

ISBN: (纸本)9798350389678

Accurate Normalized Difference Vegetation Index (NDVI) forecasting is crucial for effective agricultural planning. However, a good prediction of the same requires sufficient data, but structured data is not available in the public domain or open-source community. Also, most of the existing methods do not consider spatial information. This study presents a novel semi-automated dataset generation framework that utilizes Sentinel-2, POWER data Access Viewer, and Google Earth Engine to create a comprehensive time-series dataset. We propose a smoothed long short-term memory (LSTM) model considering time series, historical meteorological, and spatial information. The proposed Smoothed-LSTM-based model outperforms Traditional-LSTM models, demonstrating its effectiveness in NDVI prediction for agricultural applications. © 2024 IEEE.

关键词： Long short-term memory

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An IoT Environment Based Framework for Intelligent Intrusion Detection

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computers, Materials & Continua 2023年第5期75卷 2365-2381页

作者： Hamza Safwan Zeshan Iqbal Rashid Amin Muhammad Attique Khan Majed Alhaisoni Abdullah Alqahtani Ye Jin Kim Byoungchol Chang Deparment of Computer Science UET TaxilaTaxila47080Pakistan Department of Computer Science HITEC UniversityTaxila47080Pakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea Center for Computational Social Science Hanyang UniversitySeoul04763Korea

Software-defined networking(SDN)represents a paradigm shift in network traffic *** distinguishes between the data and control *** are then used to communicate between these *** controller is central to the management of an SDN network and is subject to security *** research shows how a deep learning algorithm can detect intrusions in SDN-based IoT ***,low accuracy,and efficient feature selection is all *** propose a hybrid machine learning-based approach based on Random Forest and Long Short-Term Memory(LSTM).In this study,a new dataset based specifically on Software Defined Networks is used in *** obtain the best and most relevant features,a feature selection technique is *** experiments have revealed that the proposed solution is a superior method for detecting flow-based *** performance of our proposed model is also measured in terms of accuracy,recall,and precision.F1 rating and detection time Furthermore,a lightweight model for training is proposed,which selects fewer features while maintaining the model’s *** show that the adopted methodology outperforms existing models.

关键词： Software-defined networks prediction modeling machine learning deep learning

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A Machine Learning Model for Active-Break Spell Forecasting of Indian Summer Monsoon from Outgoing Longwave Radiation data

A Machine Learning Model for Active-Break Spell Forecasting ...

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2024 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2024

作者： Singh, Apoorva Dinesh Jha, Ritesh Nanjundiah, Ravi Subramani, Deepak Indian Institute of Science Department of Computational and Data Sciences Bengaluru560094 India Indian Institute of Science Center for Atmospheric and Oceanic Sciences Bengaluru560094 India

ISBN: (纸本)9798350360325

Forecasting the active/break spell of the Indian Summer Monsoon is important for planning flood management and irrigation activities. In the present work, we develop a simple machine learning model to forecast the active/break spells at multiple lead times. The input to the model is a sequence of 15 days of daily 1°× 1° gridded Outgoing Longwave Radiation (OLR) spatial maps. The spatial extents are from 30°S to 30°N and 80°W to 150°E. The output from the model is whether the next 5-day, 10-day and 15-day periods have active, neutral or break spells. The active/neutral spells are defined as a 5-day period with cumulative rainfall more/less than one standard deviation of the long term 5-day mean. This simple model consists of a linear dimensionality reduction in space and multiple classical classification models that use the principal components in time as input. This simple model achieves an F1 score of 0.46 for the first, second and third pentad forecasts. These results will serve as a common sense baseline for the development of any model based on complex neural networks. The code can be found at https://***/Shikamaru-Nara1729/OLR-PCA-Baseline. © 2024 IEEE.

关键词： Artificial Intelligence Indian Summer Monsoon OLR data

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Early-cycle prediction of battery aging onset across chemistries

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Journal of Energy Chemistry 2025年第6期105卷 233-242页

作者： Xijun Tan Nathan Zeng Yixiang Deng Shuguo Sun Bo Rui Jun Xu Department of Mechanical Engineering University of Delaware Energy Mechanics and Sustainability Laboratory (EMSLab) University of Delaware Dover-Sherborn High School Department of Computer and Information Sciences University of Delaware Department of Biomedical Engineering University of Delaware Data Science Institute University of Delaware

Accurately predicting the knee point cycle number,marking the onset of accelerated capacity degradation,is the first critical signal for managing lithium-ion battery life cycles in electric vehicles and stationary energy storage ***,the inherent electrochemo-mechanical-thermal complexities of battery aging present significant challenges for physics-based models and machine-learning models,often leading to reduced predictive *** study developed a comprehensive dataset comprising 20 lithium nickel manganese cobalt oxide(NCM)/graphite cells(0.5-1 C) from our lab and 162 commercial lithium iron phosphate(LFP)/graphite cells(3-6 C) from the public database,with knee point observed between 100 and 1000 *** proposed a new strategy to extract novel features with strong physical context from early-cycle voltage curves,enabling precise knee point predictions across the chemistries without the need for extensive cycling *** model achieved a mean absolute percentage error(MAPE) of 7% for knee point prediction using five selected ***,the model yielded 8% MAPE with only one single feature across the initial 200 cycles,and 10% MAPE when applying five features across the initial 50 cycles,spanning different battery *** work highlights the potential of integrating multi-chemistry datasets with data-driven modeling to forecast aging patterns across diverse battery chemistries,advancing battery longevity and reliability.

关键词： Battery aging Knee point Machine learning Modeling

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A Secured and Continuously Developing Methodology for Breast Cancer Image Segmentation via U-Net Based Architecture and Distributed data Training

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computer Modeling in Engineering & sciences 2025年第3期142卷 2617-2640页

作者： Rifat Sarker Aoyon Ismail Hossain M.Abdullah-Al-Wadud Jia Uddin Department of Computer Science and Engineering Brac UniversityDhaka1000Bangladesh Department of Computer Science and Engineering George Mason UniversityFairfaxVA 22030USA Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia AI and Big Data Department Endicott CollegeWoosong UniversityDaejeon34606Republic of Korea

This research introduces a unique approach to segmenting breast cancer images using a U-Net-based ***,the computational demand for image processing is very ***,we have conducted this research to build a system that enables image segmentation training with low-power *** accomplish this,all data are divided into several segments,each being trained *** the case of prediction,the initial output is predicted from each trained model for an input,where the ultimate output is selected based on the pixel-wise majority voting of the expected outputs,which also ensures data *** addition,this kind of distributed training system allows different computers to be used *** is how the training process takes comparatively less time than typical training *** after completing the training,the proposed prediction system allows a newly trained model to be included in the ***,the prediction is consistently more *** evaluated the effectiveness of the ultimate output based on four performance matrices:average pixel accuracy,mean absolute error,average specificity,and average balanced *** experimental results show that the scores of average pixel accuracy,mean absolute error,average specificity,and average balanced accuracy are 0.9216,0.0687,0.9477,and 0.8674,*** addition,the proposed method was compared with four other state-of-the-art models in terms of total training time and usage of computational *** it outperformed all of them in these aspects.

关键词： Breast cancer U-Net distributed training data privacy low-powerful machines

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