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5th EAI international conference on Big Data Innovation for Sustainable Cognitive Computing, BDCC 2022

5th EAI International Conference on Big Data Innovation for ...

引用

5th EAI international conference on Big Data Innovation for Sustainable Cognitive Computing, BDCC 2022

ISBN: (纸本)9783031283239

the proceedings contain 18 papers. the special focus in this conference is on Big Data Innovation for Sustainable Cognitive Computing. the topics include: A Comparative Analysis of Assignment Problem;a Survey on Memory Assistive Technology for Elderly;An Experimental Investigation on the Emotion Recognition Using Power Spectrum Density and Machine Learning algorithms in EEG Signals;Detection and Classification of Pneumonia and COVID-19 from Chest X-Ray Using Convolutional Neural Network;stopwords Aware Emotion-Based Sentiment Analysis of News Articles;An Empirical Study to Assess the Factors Influencing Banking Customers Toward FINTECH Adoption in Tamil Nadu;driver’s Drowsiness Detection Using SpO2;a Blockchain Framework for Investment Authorities to Manage Assets and funds;analysis of Metaheuristic algorithms for Optimized Extreme Learning Machines in Various Sectors;metal and Metal Oxide Nanoparticle Image Analysis Using Machine Learning Algorithm;efficient Implementation to Reduce the Data Size in Big Data Using Classification Algorithm of Machine Learning;tracer for Estimation of the Data Changes Delivered and Permalinks;Design and Development of a Smart Home Management System Based on MQTT Incorporated in Mesh Network;novel Machine-Learning-Based Decision Support System for Fraud Prevention;big Data Challenges in Retail Sector: Perspective from Data Envelopment Analysis;restoration of Ancient Kannada Handwritten Palm Leaf Manuscripts Using Image Enhancement Techniques.

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A Machine Learning Approach for Predicting Efficient CPU Scheduling Algorithm 5

A Machine Learning Approach for Predicting Efficient CPU Sch...

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5th international conference on Sustainable Technologies for Industry 5.0, STI 2023

作者： Biswas, Swapnil Ahmed, Md. Shakil Rahman, Md. Jobayer Khaer, Anika Islam, Md. Motaharul United International University Department of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350394290

CPU scheduling is a critical aspect of an operating system design as it dictates how processes are prioritized for execution. In a uniprocessor, only a single process at a time can be executed simultaneously. the CPU scheduler considers various factors to determine the most effective process order. thus, it's vital to decide quickly on the CPU scheduling strategy. In this research, a number of different machine learning algorithms are compared in order to determine which one offers the most suitable CPU scheduling method that is very effective and has the quickest turnaround time to carry out the new process requests. Implementing machine learning techniques in operating systems research can improve critical aspects such as response time, throughput, turnaround time, and waiting time. By enhancing the overall performance of the system, users are delivered a faster and more efficient computing experience. this analysis showcases the significance of utilizing machine learning techniques as a solution to the issues arising due to the ever-increasing complexity and demands of modern computing. By optimizing resource utilization and improving overall performance, we can reduce energy consumption and environmental impact while meeting the demands of modern computing. this aligns with the objectives of sustainable technology and Industry 5.0 to integrate physical and digital systems for sustainable and efficient production. © 2023 IEEE.

关键词： Sustainable development

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Revolutionizing Digital Marketing with Artificial Intelligence Solutions: A Four-Step Sequential Model 5th

Revolutionizing Digital Marketing with Artificial Intelligen...

引用

5th international conference on Energy Systems, Drives and Automations, ESDA 2022

作者： Raju, Valliappan Kassim, Salina Jahan, Sarwat Wahab, Wahadaniah Abdul International Islamic University of Malaysia Kuala Lumpur Malaysia Perdana University Kuala Lumpur Malaysia Manipal Academy of Higher Education Dubai United Arab Emirates Universiti Teknologi MARA Shah Alam Malaysia

ISBN: (纸本)9789819701537

In recent years, technological advancements have led to significant changes across various industries, including marketing. the need to adapt to digital trends has become imperative for marketers. While this may seem like a daunting task, the use of automated applications and systems based on artificial intelligence has simplified traditional targeting and customization processes. Many online promotion platforms incorporate algorithms that identify optimal combinations of parameters, while some companies opt to develop and implement customized systems in-house. the authors of this article seek to solutions to enhance website visibility through the use of keywords. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Artificial intelligence

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Comparative Analysis of CatBoost Against Machine Learning algorithms for Classification of Altered NSL-KDD 5th

Comparative Analysis of CatBoost Against Machine Learning Al...

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5th international conference on Trends in Computational and Cognitive Engineering, TCCE 2023

作者： Sharna, Nadia Ahmed Islam, Emamul Daffodil International University Dhaka Bangladesh University of Dhaka Dhaka Bangladesh

ISBN: (纸本)9789819719228

the importance of information security, especially in network environments, has increased as a result of the expanding volume of data stored in computer systems. the categorization of data instances into two classes using binary classification, a fundamental machine learning activity, facilitates efficient decision-making and risk assessment. the performance of standard classifiers, logistic regression, Gaussian NB, and support vector machine (SVM) is compared with that of CatBoost, a gradient boosting-based classifier known for handling categorical variables and reducing overfitting. the NSL-KDD dataset is used for the evaluation. Results show that CatBoost performs better than conventional classifiers, with higher accuracy, precision, and recall. CatBoost's ability to classify network connections accurately is demonstrated by its training and testing accuracy results, which surpass 97%. Furthermore, its high true positive rate and low false positive rate attest to its competence in accurately identifying network threats. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Intrusion detection

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Comparison Analysis of Various Optimization algorithms for Classification of Radar Returns from the Ionosphere 5

Comparison Analysis of Various Optimization Algorithms for C...

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5th international conference on Control, Communication and Computing, ICCC 2023

作者： Vijaya, J. Jain, Muskan Yadav, Nandita International Institute of Information Technology Department of DSAI Chhattisgarh Naya Raipur India

ISBN: (纸本)9798350334128

Machine learning is developing swiftly as an everexpanding field. the development of the same is occurring rapidly and has made many theoretical breakthroughs in recent times. Due to its importance as a part of machine learning, intelligent optimization algorithms are expected to become increasingly. the exponential growth of data volume and the increase in model complexity present increasing challenges for machine learning optimization strategies. Numerous initiatives have been launched to improve machine learning optimization approaches or address optimization-related problems. Future optimization and machine-learning research can be guided by a detailed evaluation and analysis of optimization strategies from a machine-learning perspective. Machine learning uses a variety of optimization strategies, which makes it easier to compare and analyze how well they function in various situations. In this study, we analyze and contrast seven well-known bio-inspired data engineering techniques and their effectiveness. We apply these techniques to the Radar Returns from the Ionosphere data-set and assess the results with a range of assessment metrics. © 2023 IEEE.

关键词： Machine learning

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5th international conference on Advances in Distributed Computing and Machine Learning, ICADCML 2024

5th International Conference on Advances in Distributed Comp...

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5th international conference on Advances in Distributed Computing and Machine Learning, ICADCML 2024

ISBN: (纸本)9789819718405

the proceedings contain 35 papers. the special focus in this conference is on Advances in Distributed Computing and Machine Learning. the topics include: Computer-Aided Bundle Branch Block Detection Using Symbolic Features of ECG Signal;block-Chain and Cloud-Based Tender Allocation System;reliability Assessment of IoT-Enabled Systems Using Fault Trees and Bayesian Networks;resnet-50 Integrated with Attention Mechanism for Remote Sensing Classification;revolutionizing Data Annotation with Convergence of Deep Learning and Active Learning to Enhance Credibility on Twitter Datasets;statistical and Deep-Learning Approaches for Individual Carbon Footprint Calculation in India;intelligent Healthcare System Using Emerging Technologies: A Comprehensive Survey;Efficient Energy Management by Using SJF Scheduling in Wireless Sensor Network;cost Effective and Energy Efficient Drip Irrigation System for IoT Enabled Smart Agriculture;facial Detection and Recognition in Drone Imagery Using FaceNet;valluvan: Processing Name Board Images to Enhance Communication for Native Tamil Speakers;Optimized VM Migration for Energy and Cost Reduction Using TSO Algorithm in Cloud Computing;A Study on the Mental Health Among Indian Population in the Post COVID-19 Pandemic Using Computational Intelligence;Cloud-Based Anomaly Detection for Broken Rail Track Using LSTM Autoencoders and Cross-modal Audio Analysis;Face Recognition Using CNN for Monitoring and Surveillance of Neurological Disorder Patients;a Novel Approach to Breast Cancer Histopathological Image Classification Using Cross-colour Space Feature Fusion and Quantum–Classical Stack Ensemble Method;comparative Analysis of Deep Learning-Based Hybrid algorithms for Liver Disease Prediction;preface;performance Improvements of Covert Timing Channel Detection in the Era of Artificial Intelligence;sign Detection Using an N-Gram Language Model and MobileNet.

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5th international conference on Advances in Distributed Computing and Machine Learning, ICADCML 2024

5th International Conference on Advances in Distributed Comp...

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5th international conference on Advances in Distributed Computing and Machine Learning, ICADCML 2024

ISBN: (纸本)9789819735228

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5th international conference on Computational Intelligence in Communications and Business Analytics, CICBA 2023

5th International Conference on Computational Intelligence i...

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5th international conference on Computational Intelligence in Communications and Business Analytics, CICBA 2023

ISBN: (纸本)9783031488757

the proceedings contain 52 papers. the special focus in this conference is on Computational Intelligence in Communications and Business Analytics. the topics include: Regression Analysis for Finding Correlation on Indian Agricultural Data;Classification of the Chest X-ray Images of COVID-19 Patients through the Mean Structural Similarity Index;a Religious Sentiment Detector Based on Machine Learning to Provide Meaningful Analysis of Religious Texts;automated Detection of Melanoma Skin Disease Using Classification Algorithm;Identification of Cloud Types for Meteorological Satellite Images: A Character-Based CNN-LSTM Hybrid Caption Model;Prediction and Deeper Analysis of Market Fear in Pre-COVID-19, COVID-19 and Russia-Ukraine Conflict: A Comparative Study of Facebook Prophet, Uber Orbit and Explainable AI;ANN for Diabetic Prediction by Using Chaotic Based Sine Cosine Algorithm;A Deep CNN Framework for Oral Cancer Detection Using Histopathology Dataset;AntiNuclear Antibody Pattern Classification Using CNN with Small Dataset;assessment of Slope Instability in a Hilly Terrain: A Logistic Regression and Random Forest Based Approach;classification of Offensive Tweet in Marathi Language Using Machine Learning Models;An Integrative Method for COVID-19 Patients’ Classification from Chest X-ray Using Deep Learning Network with Image Visibility Graph as Feature Extractor;identification and Multi-classification of Several Potato Plant Leave Diseases Using Deep Learning;A GUI-Based Approach to Predict Heart Disease Using Machine Learning algorithms and Flask API;Classification of Cricket Shots from Cricket Videos Using Self-attention Infused CNN-RNN (SAICNN-RNN);attention-Residual Convolutional Neural Network for Image Restoration Due to Bad Weather;Deep Learning-Based Intelligent GUI Tool For Skin Disease Diagnosis System;a Comparative Study on the Evaluation of k-mer Indexing in Genome Sequence Compression.

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5th international conference on Computational Intelligence in Communications and Business Analytics, CICBA 2023

5th International Conference on Computational Intelligence i...

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5th international conference on Computational Intelligence in Communications and Business Analytics, CICBA 2023

ISBN: (纸本)9783031488788

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HPCAsia 2024 – Proceedings of the international conference on High Performance Computing in Asia-Pacific Region

HPCAsia 2024 – Proceedings of the International Conference ...

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5th international conference on Big-data Service and Intelligent Computation, BDSIC 2023

ISBN: (纸本)9798400708893

the proceedings contain 17 papers. the topics discussed include: portable implementations of work stealing;sKokkos: enabling Kokkos with transparent device selection on heterogeneous systems using OpenACC;parallelized remapping algorithms for km-scale global weather and climate simulations with icosahedral grid system;approximate block diagonalization of symmetric matrices using quantum annealing;QUBO formulation using inequalities for problems with complex constraints;low-latency communication in RISC-V clusters;flexible systolic array platform on virtual 2-D Multi-FPGA plane;an efficient task-parallel pipeline programming framework;and task-based low-rank hybrid parallel Cholesky factorization for distributed memory environment.

关键词： Systolic arrays

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