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Access pattern Characterization of Last-Level Cache for Effective Replacement

Access Pattern Characterization of Last-Level Cache for Effe...

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international conference on Computational Science and Computational Intelligence (CSCI)

作者： Shafayat Mowla Anik Byeong Kil Lee Department of Electrical and Computer Engineering Colorado Springs (UCCS) The University of Colorado

ISBN: (数字)9798350361513

ISBN: (纸本)9798350372304

Based on our analysis, numerous aspects should be considered to achieve the best solutions because cache line eviction is not influenced by only a single aspect. Cache lines may occasionally be accessed repeatedly during programming loops. Suppose the same cache line goes to writeback, then has long-distance access, then again goes to a small writeback distance, then again to a long distance, and so on. The same cache line can be a sequence of short and long distances or any order of short and long distances. So, we refer to it as an access pattern. By maintaining the cache line when a short distance is expected in the future and discarding it when a long distance is anticipated, we may take advantage of certain access patterns. With the concept of the access pattern of the cache, we can enhance the performance of the last-level cache. In this paper, we characterize the access pattern of the last-level cache and provide the implications of how to increase cache performance by anticipating and training access patterns. Also, we will discuss the cases where some applications do not have any access patterns.

关键词： Training Pollution Scientific computing Programming Cache storage pattern recognition Computational intelligence

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Intelligent and Optimized Deep Learning Techniques for Analyzing the IoMT-Based Remote Patient Monitoring Systems

Intelligent and Optimized Deep Learning Techniques for Analy...

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Emerging Research in Computational Science (ICERCS), international conference on

作者： Sayyed Johar G R Manjula Dept of AIML JNNCE-Shivamogga Visveswaraya Technological University Belagavi India Dept of Data Science JNNCE-Shivamogga Visveswaraya Technological University Belagavi

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534974

The IoMT has dramatically empowered the process of remote patient monitoring (RPM) through much better real-time data collection and health analysis. The real struggle lies in categorizing and evaluating the patient’s health-related datas due to its complexity and unbalanced nature. This study proposes an Intelligent W-DQN model based on a whale-optimized Deep-Q-Network (W-DQN) to identify and categorize pathological information, an LSTM-QDA model for recognizing temporal patterns and reducing dimensions, and a PathoDimClassifier for selecting features to address imbalanced data and noise issues effectively. Optimization techniques, including the Whale Optimization Algorithm and the Adam optimizer, are applied to improve accuracy rates while decreasing training duration and minimizing errors. The approach seeks to enhance health monitoring in time by providing accurate categorization and prompt medical interventions to ultimately enhance patient care results.

关键词： Training Patient monitoring Pathology Accuracy Scientific computing Noise Whale optimization algorithms Data models Real-time systems pattern recognition

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Cloud-Based AI Solutions for Personal Finance Management

Cloud-Based AI Solutions for Personal Finance Management

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Pervasive Computational Technologies (ICPCT), international conference on

作者： Gurpreet Kaur Chhabra Ashutosh Kumar Mishra Varsha Tyagi Chandra Mani Vani Tyagi Pramod Kumar Sagar Department of Management Management Education and Research Institute Delhi India Department of Management SRM Business School Lucknow UP India Computer Science & Engineering Raj Kumar Goel Institute of Technology Ghaziabad UP India Computer Science & Engineering Mangalmay Institute of Engineering & Technology Greater Noida India

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

Cloud-based AI answers for private finance control are out-of-the-field tools that leverage synthetic intelligence and cloud computing technologies to manage people's money higher. In this article, I have described various solutions with features ranging from budgeting to personalized financial planning and investment suggestions. Utilizing superior system learning algorithms and cloud-based AI solutions can help understand an individual's fiscal information, including expenses, profits, or savings transactions, for generating personalized insights and advice. This lets people learn about their spending habits and make more informed financial choices. Through the power of the cloud, these solutions can securely store and access financial data from multiple devices, which helps individuals, manage their finances on the go easily. It also enables the client to get real-time updates and notifications, which offers him more comprehensive knowledge regarding his existing financial scenario. These offerings also come with more automation features like reminders to pay payments and computerized enough transfers for a price range, keeping customers from spending further effort and time on their funds. They also offer financial advisors, tax planning tools, and wealth management.

关键词： Cloud computing Fintech Finance Market research Real-time systems Planning pattern recognition Artificial intelligence Hypertext systems Investment

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Ocean archaea PPI prediction with pretraining models 25

Ocean archaea PPI prediction with pretraining models

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Proceedings of the 2025 5th international conference on Bioinformatics and Intelligent computing

作者： Ying Zhang Yuan Liu Xiaoyong Pan Hongbin Shen Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (纸本)9798400712203

Protein-Protein Interaction (PPI) provides important insights into the metabolic mechanisms of different biological processes. Although PPIs in some organisms have been investigated systematically, PPIs in the ocean archaea remain largely unexplored. But such species have special investigation value since their adaptation to extreme living conditions may generate unique PPIs. In this paper, we aim to characterize and predict PPIs in ocean archaea to advance understanding of their metabolic networks. First, we collect all ocean archaea PPIs with high confidence from STRING database and analyze the PPI network features, including centrality and enrichment analysis. The functional enrichment results of the largest connecting subgraph in the PPI network show most PPIs in our constructed dataset is related to the translation and transcription processes. Then, we generate an equal number of negative PPI pairs, whose members have either different subcellular locations or GO terms. We also use the generated dataset to test the performance of three pretraining methods and their ensemble methods in the binary PPI prediction task. Our results suggest the ensemble methods could be applied to further improve models’ performance. Fine-tuned models trained on the ocean archaea dataset are expected to predict the other ocean archaea PPIs that are not included in the STRING database and get more understanding about the ocean archaea PPI universe.

关键词： Binary PPI prediction

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Broccoli Classification: A Fusion of CNN and AdaBoost

Broccoli Classification: A Fusion of CNN and AdaBoost

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Electronics, computing, Communication and Control Technology (ICECCC), international conference on

作者： Arshleen Kaur Rishabh Sharma Nitin Thapliyal Manika Manwal Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Computer Science and Engineering Graphic Era Hill University Dehradun Uttrakhand India Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttrakhand India

ISBN: (数字)9798350371802

ISBN: (纸本)9798350371819

Although precision farming has been introduced, differentiation of crop species still calls for the development of quick and reliable classification tools used in the process of optimizing cultivation practices, which ultimately boost crop yields. Here we propose a new classification method to identify broccoli varieties particularly of ‘Pusa KTS-1’, ‘Green Magic’, and ‘Calabrese’ with the use of an integrated approach that fuses the strength of Convolutional Neural Networks (CNN) and AdaBoost. This research makes a striking improvement in the accuracy of crop variety classification using the fusion of CNN for powerful feature extraction and AdaBoost which leverages the hard instances to increase the accuracy of the classifier. A database of broccoli images was prepared, auxiliary operations were performed and its diversity was improved to make the model not weak and generalized enough. On this dataset, the CNN-AdaBoost ensemble was trained, validated, and tested and the model achieved an overall accuracy of 93.7% which outperformed standalone models. The model's performance at boundaries validates its potential for automatic classification, which is important for precision agriculture's targets of higher productivity and efficiency. The results of this research consequently transcend beyond the scope of broccoli variety classification, lending ways for improvements in agricultural technologies through the adoption of machine learning. In the future, the model will be applied to other crops and will be used to solve many agricultural problems. Its parameters will be improved to make it more convenient for daily use.

关键词： Precision agriculture Accuracy Face recognition soft sensors Crops Transforms Convolutional neural networks

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Wafer pattern recognition for Detecting Process Abnormalities in NAND Flash Memory Manufacturing 47

Wafer Pattern Recognition for Detecting Process Abnormalitie...

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47th international Symposium for Testing and Failure Analysis conference, ISTFA 2021

作者： Choe, Jeongin Kim, Taehyeon Yoon, Saetbyeol Yoon, Sangyong Song, Ki-Whan Song, Jai Hyuk Kim, Myungsuk Choi, Woo Young Flash Product & Technology Samsung Electronics Co. Ltd. Hwaseong18448 Korea Republic of Department of Computer Science and Engineering Kyungpook National University Daegu41566 Korea Republic of Department of Electronic Engineering Sogang University Seoul04107 Korea Republic of

ISBN: (纸本)9781627084215

We have adopted various defect detection systems in the front stage of manufacturing in order to effectively manage the quality of flash memory products. In this paper, we propose an intelligent pattern recognition methodology which enables us to discriminate abnormal wafer automatically in the course of NAND flash memory manufacturing. Our proposed technique consists of the two steps: pre-processing and hybrid clustering. The pre-processing step based on process primitives efficiently eliminates noisy data. Then, the hybrid clustering step dramatically reduces the total amount of computing, which makes our technique practical for the mass production of NAND flash memory. Copyright © 2021 ASM international® All rights reserved.

关键词： Flash memory

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6th international conference on Information, Communication and computing Technology, ICICCT 2021

6th International Conference on Information, Communication a...

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6th international conference on Information, Communication and computing Technology, ICICCT 2021

ISBN: (纸本)9783030883775

The proceedings contain 20 papers. The special focus in this conference is on Information, Communication and computing Technology. The topics include: Predicting the Decomposition Level of Forest Trees Through Ensembling Methods;can Machine Learning Predict an Employee’s Mental Health?;predicting the Stock Market Trend: An Ensemble Approach Using Impactful Exploratory Data Analysis;detecting Text-Bullying on Twitter Using Machine Learning Algorithms;empirical Laws of Natural Language Processing for Neural Language Generated Text;quantum Based Deep Learning Models for pattern recognition;performance Analysis of Named Entity recognition Approaches on Code-Mixed Data;measurement of Liquid Level Using Prediction Methodology;A MapReduce Approach to Automatic Key File Updates for SPT (Squeeze, Pack and Transfer) Algorithm;web-Based Real-Time Gesture recognition with Voice;performance Enhancement in Big Data by Guided Map Reduce;multi-layer Parallelization in Transportation Management software;Multipath TCP Security Issues, Challenges and Solutions;story Generation from Images Using Deep Learning;preface;comparative Analysis of Security Protocols in IoT;Minimize Penalty Fees During Reconfiguration of a Set of Light-Tree Pairs in an All-Optical WDM Network;secure Data Transmission Techniques for Privacy Preserving Computation Offloading Between Fog computing Nodes.

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A Survey of Semantic Approaches in Personality Traits Profiling Analysis

A Survey of Semantic Approaches in Personality Traits Profil...

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Smart computing, IoT and Machine Learning (SIML), international conference on

作者： Anggit Dwi Hartanto Ema Utami Kusrini Arief Setyanto S3 Informatika Universitas Amikom Yogyakarta Yogyakarta Indonesia

ISBN: (数字)9798350364101

ISBN: (纸本)9798350364118

Natural language processing (NLP) and machine learning have been used in the analysis of individual profiling based on various data sources, including public datasets, social media platforms, and frameworks such as DISC, Big Five, MBTI, Holland’s RIASEC, 16 PF, HEXACO, and others. Deep learning is also utilized in research, with algorithms such as CNN, LSTM, and Galvanic Skin Response (GSR) employed for classification, labeling, and regression. The development of profiling analysis has led to a semantic approach to enhance model performance. Examples of new models that can improve performance include the Semantic-enhanced Personality recognition Neural Network (SEPRNN) and the Robustly Optimized BERT Pre-Training Approach (RoBERTa). This study begins by collecting papers from publication databases. The next step is paper selection, and then the paper content is analyzed according to the research questions that have been determined. The final step is visualization. This study aims to provide a comprehensive understanding of the development of NLP in profiling analysis. The results of this study show that 53% used social media as a data source in profiling analysis, 43% used deep learning for the learning model, 68% used the Big-Five personality framework, 10.2% used support vector machine (SVM) as the algorithm, and 86.7% used lexical semantics for approaches. Research opportunities in profiling analysis include developing and improving predictive models, integrating and exploring NLP, using diverse data and machine learning techniques, applying them in various contexts, and integrating them with other technologies.

关键词： Support vector machines Deep learning Machine learning algorithms Social networking (online) soft sensors Bibliographies Semantics

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10th international conference on Frontier computing, FC 2020

10th International Conference on Frontier Computing, FC 2020

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10th international conference on Frontier computing, FC 2020

ISBN: (纸本)9789811601149

The proceedings contain 300 papers. The special focus in this conference is on Frontier computing. The topics include: Dynamic Modeling and Control Method of Manipulator Based on Model recognition;design of Programmable soft Starter Control System;power Load Forecasting Model Based on Improved Parallel Fuzzy Kernel Clustering Algorithm;The Prototype System of Support Vector Machine (SVM) Based on Big Data international Shipping Index Prediction;big Data Technology in Computer Information System;target recognition and Grab Location Based on Machine Vision and Deep Learning;point Cloud Data Self-adaptive Partition and Triangular Surface Reconstruction;application of Information Platform in Smart Logistics System;trace Car Based on Super Capacitor;Comparison of Influenza Disease Prediction Using ARIMA and LSTM Models for Central Taiwan;online Teaching Platform in Vocational College English Education;application of Adaptable College English Innovation Teaching in the Network Environment;problems and Countermeasures of Information Technology in College English Teaching;brief Analysis of Indian Cybersecurity Policy;advantages and Disadvantages of India and America in Cybersecurity Cooperation;new Model of Educational Information Service in the Era of Big Data;exploration on the Construction of Wisdom Teaching of Universities in the "Internet +" Era;on the Scalability Analysis of computing-Offloading Edge computing;Theoretical Basis of P and NP Problem;Electrical Control and PLC Application Technology;the Design and Implementation of Dynamic Costume Projection System;automatic Control Technology and Integrated Design of Electromechanical Control System;evolution and Frontiers Visualization Analysis of international Cooperation in Vaccine R application Analysis of Computer-Aided Design in Clothing Design Teaching.

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Comparison of global prevalence of sleep disorders in intellectually normal v/s intellectually disabled: A review 11th

Comparison of global prevalence of sleep disorders in intell...

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11th international conference on soft computing and pattern recognition, SoCPaR 2019, and 11th World Congress on Nature and Biologically Inspired computing, NaBIC 2019

作者： Irani, Nushafreen Gandhi, Niketa Deshmukh, Sanjay Deshpande, Abhijit Department of Life Sciences University of Mumbai MumbaiMaharashtra400 098 India International Institute of Sleep Sciences ThaneMaharashtra400602 India

ISBN: (纸本)9783030493448

Sleep is not given due importance unless one suffers from any one type of a sleep disorder. The current paper reviews the prevalence of sleep disorders. There is very low awareness about sleep disorders and their disastrous consequences in the Indian population. These consequences include impairment in physical, psychological and lack of neurodevelopment progress. Common methods to assess sleep disorders prevalence in large population groups have been discussed. This review also includes a special mention of sleep disorders in the intellectually disabled population. Our review finds that the prevalence of sleep disorders in this population is significantly high and comparable to the normal population. Sleep issues in intellectually disabled population present dual consequences. Not only sleep issues hamper the growth of persons suffering but unfortunately even the caregivers are also affected by this issue. Sleep disorders in the mentally challenged population are less studied since the person suffering has difficulty in reporting it. Our literature review reveals the fact that sleep issues are prevalent globally and they especially affect the intellectually disabled population. The review also discusses ancient history on sleep & intellectually disabled population. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Sleep research

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