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Generalization Evaluation of Seq2seq based Fog Computing Application Placement Algorithm on Limited Dataset 3

Generalization Evaluation of Seq2seq based Fog Computing App...

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3rd International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2023

作者： Pakpahan, Michael S. M. Nugroho, Lukito Edi Widyawan, Widyawan Wardhana, Ajie Kusuma Muhammad Ardian, R.A. Astagenta, Rangga Satria Universitas Gadjah Mada Dept. of Electrical and Information Engineering Yogyakarta Indonesia Universitas Amikom Yogyakarta Faculty of Computer Science Dept. Informatics Yogyakarta Indonesia

ISBN: (纸本)9798350394559

Addressing its growing number and vital role, decentralization of cloud computing becoming a necessity. Fog computing aims to bring application closer to the data source-typically at the network's edge by leveraging local resources to provide faster data processing and decision-making. Fog computing then has to place application strategically to use its limited fog resource to improve application performance metrics. This problem known as Fog Application Placement Problem (FAPP) has been approached using previous methods that rely on rules and prior knowledge that may not be adaptive, but rather being overly specialized to specific problems. Deep learning with its learning mechanism, can offer more adaptable and dynamic solutions for a wide range of scenarios, especially in fog network that continuously evolve. This research investigate Seq2seq placement model inherent limitations, notably the impracticality of generating every possible pattern from all potential request configurations. We aim to address and answer the following critical questions: 1) How does the model's performance vary when confronted with unseen requests or an augmented number of modules, especially considering the limitations in training data?;2) Can the seq2seq model, even with its training limitations, adhere to the heuristic rules of the dataset when dealing with unfamiliar problems? This research shows that in similar availability, there is a reduction of almost half of the response time with 183.03 ms, 2.93 number of hops, and 0.87 megabyte of transmitted messages against the hop3 algorithm. Moreover, we highlight the ability of seq2seq model to follow heuristic rules in unseen scenarios. © 2023 IEEE.

关键词： Fog computing

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Predictive Analysis of Diabetes Mellitus Using Decision Tree Approach 2

Predictive Analysis of Diabetes Mellitus Using Decision Tree...

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2nd Asian Conference on Innovation in Technology, ASIANCON 2022

作者： Thotad, Puneeth Bharamagoudar, Geeta R. Anami, Basavaraj S. Kle Institute of Technology Dept. of Master of Computer Applications Hubballi India Kle Institute of Technology Dept. of Computer Science & Engineering Hubballi India

ISBN: (数字)9781665468510

ISBN: (纸本)9781665468510

Diabetes Mellitus is one of the Non-Communicable Disease. It is characterized by hyperglycemia. It may be reason for numerous health difficulties. According to the statistics in coming years, in 2030, worldwide it reaches up to 643 million counts of diabetic patients. The various factors such as Bad Diet, High Blood Pressure, Obesity, Age, Lack of Exercise etc. can led to Diabetes Mellitus. The various diseases like nerve damage, heart problem, eye problem, kidney disease, stroke in many people caused by high risk of Diabetes Mellitus. Machine Learning plays vital role in healthcare sectors. Using various Machine Learning techniques, a study can be done on large datasets and discover unseen facts, unseen patterns to realize awareness from the data and predict results consequently. In this work, the PIMA Indian Diabetes dataset is considered for Predictive Analysis. The Decision Tree algorithm, namely ID3 (Iterative Dichotomize 3) is used for a predictive analysis. The major phases include preprocessing, feature extraction, classification, and prediction. The classification accuracies of 79.76% and 76.3% are obtained for balanced and unbalanced datasets respectively. The maximum specificity and sensitivity obtained are 79.75% and 79.78% respectively. The ROC curve gives 79.57% overall classification accuracy. The work assists physicians as a second opinion. It can help various health industries to predict the diabetes disease in a better way. © 2022 IEEE.

关键词： Decision trees

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Utilizing Anomaly Detection Methods for Identifying Fraudulent Activities in Credit Card Transactions 1

Utilizing Anomaly Detection Methods for Identifying Fraudule...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Prasad, N. Siva Rama Krishna Gangopadhyay, Amit Prabakaran, R. Mishra, Nilamadhab Prasad, D. Shyam Kapoor, Sneha Saveetha School of Engineering Dept. of CSE Chennai India Mohan Babu University Dept. of ECE Tirupati India Arignar Anna Institute of Science and Technology Dept. of Computer Application India VIT Bhopal University Dept. of CSE Bhopal India CVR College of Engineering Dept. of Electronics and Instrumentation Engineering Hyderabad India Swami Vivekanand Subharti University Dept of CSE Meerut India

ISBN: (纸本)9798350356816

Anomaly detection involves identifying unusual occurrences that could signal potential issues, such as security breaches, system failures, financial fraud, structural defects, or medical errors. In the context of digital finance, credit card fraud has become a significant concern, with fraudulent transactions often designed to appear legitimate. To address this, we employ the Synthetic Minority Oversampling Technique (SMOTE) to balance the dataset during sampling, and use the ARIMA model for time series forecasting to ensure accurate results. This research explores various fraud detection techniques and models, including K-Nearest Neighbors (KNN), Random Forest, Support Vector Classifier (SVC), and Decision Trees. We evaluate and compare the performance of these models to determine which offers the highest accuracy in detecting fraud. © 2024 IEEE.

关键词： Anomaly detection

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Start-End Writing Integration with Convolutional Neural Network for Bengali Handwritten Numeral Recognition 12

Start-End Writing Integration with Convolutional Neural Netw...

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12th International Conference on Electrical and computer engineering, ICECE 2022

作者： Rahat-Uz-Zaman, Md. Hye, Shadmaan Akhand, M.A.H. Khulna University of Engineering & Technology Dept. of Computer Science and Engineering Khulna Bangladesh

ISBN: (纸本)9798350398793

Convolutional neural network (CNN) based methods have been very successful for handwritten numeral recognition (HNR) applications. However, CNN seems to misclassify similar shaped numerals (i.e., the silhouette of the numerals that look same). This paper presents an enhanced HNR system to improve the classification accuracy of the similar shaped handwritten numerals incorporating the terminals points (i.e., the start and end positions) with CNN's recognition. Start-End Writing Measure (SEWM) and its integration with CNN is the main contribution of this research. Traditionally, CNN's classification (i.e., C LC N N in 0,1, L9) for highest probability is the outcome of CNN-based system. In the proposed system, along with classification in CLCNN, its probability value (say CNN's confidence level 0CNN) is also used as a regulating element. Parallel to CNN's classification operation, SEWM measures start-end points of the numeral image, suggests CLSEWM numeral category for which measured start-end points are found closed to reference start-end points of the numeral class. Finally, output label or system's classification (C L sy s) of the given numeral image is provided comparing 0CNN with predefined threshold value (o0): CNN's outcome is considered as system's outcome (i.e., C Lsy s=CLcNN) if 0CNN = > σ 0;otherwise, SEWM rectifies CNN's decision (i.e., C Lsy s=CLsEWM) as CNN's confidence is low. The proposed method is tested on rich benchmark numeral datasets of Bengali and revealed itself as a suitable HNR method. © 2022 IEEE.

关键词： Convolutional neural networks

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Bulk Sample Analysis Using Associated Alpha Particle Neutron Generator and Artificial Neural Network 14

Bulk Sample Analysis Using Associated Alpha Particle Neutron...

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14th International Conference on Information, Intelligence, systems and Applications, IISA 2023

作者： Shahabinejad, Hadi Sudac, Davorin Alamaniotis, Miltiadis Nad, Karlo Objodas, Jasmina Institute Ruder Boskovic Dept. of Experimental Physics Zagreb Croatia University of Texas at San Antonio Dept. of Electrical and Computer Engineering Texas United States

ISBN: (纸本)9798350318067

Container inspection using x-rays and neutrons is in great demand at borders nowadays. While x-ray inspection has been extensively developed, neutron inspection is still under development. This work investigates the possibility of utilizing an artificial neural network (ANN) to analyze prompt-gamma spectra generated by tagged fast neutrons in an experimental setup. A simple multi-layer perceptron neural network is trained to map 10 Regions of Interest (ROIs) of 14 MeV neutron-induced prompt gamma-ray (PG) spectra, which are measured using a 3x3 inch NaI detector, to the weight percentages of 4 key constituents. The key constituents consist of one liter of Graphite, Water, Quartz sand, and Melamine. The selected inputfeatures are the integrated counts under major peaks of the PG spectra of the key constituents. The required dataset for training and testing the network is generated using the fundamental spectra of key constituents. Finally, the trained network is evaluated using a mixed sample measured over a period of 2 hours. The obtained primary results are quite satisfactory. The errors in determining the weight percentages of key constituents are less than 1 percent when the sample itself is a key constituent, and the correlation coefficient between target values and predicted values of ANN for both the train and test data is almost one. © 2023 IEEE.

关键词： Neutrons

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Impact of Project Selection Criteria on Organizational Performance: A Machine Learning Approach

Impact of Project Selection Criteria on Organizational Perfo...

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ASCE Inspire 2023: Infrastructure Innovation and Adaptation for a Sustainable and Resilient World

作者： Maqsoom, Ahsen Ali, Usman ul Basharat, Mubeen Naeem, Muhammad Hassaan Irfan, Muhammad Dept. of Civil Engineering COMSATS Univ. Islamabad Wah Cantt Pakistan Dept. of Computer Sciences HITEC Univ. Taxila Pakistan Dept. of Civil Engineering HITEC Univ. Taxila Pakistan

ISBN: (纸本)9780784485163

Recent studies in project management have indicated the importance of project selection criteria in organizational performance. This study proposes a machine learning approach to predict the impact of project selection criteria on organizational performance. This relationship was investigated using a dataset containing data from reputable organizations and applying different machine learning algorithms including multiple linear regression, k-nearest neighbor, and support vector machine. Our results demonstrated that there is a significant relationship between the parameters of project selection criteria and organizational performance. Further, it was identified that most significant relationship exists between project time, project return, technical risks, and sales, and the weak relationship exists between TechAvail and MarShare. This study contributes to the relationship between project selection criteria and organizational performance and provides insights into the application of emerging tools in data science and machine learning in operations management and project management research. © 2023 by the American Society of Civil Engineers. All Rights Reserved.

关键词： Multiple linear regression

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ML-Inspired Multiple Relay Selection in UAV-Enabled Untrusted Networks

ML-Inspired Multiple Relay Selection in UAV-Enabled Untruste...

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IEEE Conference on Wireless Communications and Networking

作者： Lefteris Tsipi Emmanouel T. Michailidis Konstantinos Maliatsos Demosthenes Vouyioukas Dept. of Information and Communication Systems Engineering University of the Aegean Samos Greece Dept. of Informatics and Computer Engineering University of West Attica Egaleo Greece Dept. of Digital Systems University of Piraeus Piraeus Greece

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

In this paper, multiple relay selection (MRS) schemes for untrusted unmanned aerial vehicle (UAV)-enabled networks are proposed. In this context, various machine learning (ML) models are employed to improve secrecy performance by optimally selecting multiple aerial relays from the available ones, all of which are regarded as untrusted. Notably, these ML models can cope with the quickly changing and random positioning of the aerial relays, effectively decoupling the intricate coupling relationship between the secrecy rate, channel coefficients, and inter-node distances. The results indicate that the ML-based MRS schemes obtain sufficient accuracy and better decoupling than a respective exhaustive searching (ES) approach, while also maintaining a lower computational complexity.

关键词： Couplings Accuracy Predictive models Autonomous aerial vehicles Security Ensemble learning Relays Computational complexity Context modeling

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Dynamic Optimization of Provider-Based Scheduling for HPC Workloads 31

Dynamic Optimization of Provider-Based Scheduling for HPC Wo...

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31st International Conference on Software, Telecommunications and computer Networks, SoftCOM 2023

作者： Marino, Jacopo Risso, Fulvio Bighi, Mauro Politecnico di Torino Dept. of Control and Computer Engineering Torino Italy PUNCH Torino S.p.A Dept. of Information Technology Torino Italy

ISBN: (纸本)9798350301076

The vast array of cloud providers present in today's market proffer a suite of High-Performance Computing (HPC) services. However, these offerings are characterized by significant variations in execution times and cost structures. Consequently, selecting the optimal cloud provider and configuring the features of the chosen computing instance (e.g. virtual machines) proves to be a challenging task for users intending to execute HPC workloads. This paper introduces a novel component designed for effortless integration with existing HPC scheduling systems. This module's primary function is to facilitate the selection of the most appropriate cloud provider for each distinct job, thereby empowering dynamic and adaptive cost-minimization strategies. Through the application of data augmentation techniques and the employment of Continuous Machine Learning, the system is endowed with the capability to operate efficiently with cloud providers that have not been previously utilized. Furthermore, it is capable of tracking the evolution of jobs over time. Our results show that this component can achieve consistent economic savings, based on the quality of the data used in the training phase. © 2023 University of Split, FESB.

关键词： Machine learning

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Question Answering System for Healthcare Information based on BERT and GPT 8

Question Answering System for Healthcare Information based o...

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Joint 8th International Conference on Digital Arts, Media and Technology and 6th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2023

作者： Jeong, Seung Won Kim, Cheong Ghil Whangbo, Taeg Keun Gachon University Dept. of IT Convergence Engineering Gyeonggi-Do13120 Korea Republic of Namseoul University Dept. of Computer Scicence Choongnam 31020 Korea Republic of

ISBN: (纸本)9798350396546

Recently, demand for telemedicine counseling has been on the rise. Although telemedicine is legally prohibited in Korea, the number of healthcare-related questions on online question and answer platforms is steadily increasing. However, doctors have to manually write answers to similar questions, which is time-consuming and inefficient. This study implemented a healthcare information provision question and answer system using an algorithm that combines BERT and GPT-2 structures. Intellectual Q&A pairs were used as training data, and the BERT-GPT algorithm experimented with creating sentences that provide healthcare information such as related medical subjects and suspected diseases as answers to healthcare-related questions. As a result of BERT-GPT2 algorithm gets worse PPL score and loss than the baseline model, but better score in the qualitative assessment. This result means BERT-GPT2 understands the conversation intention better. © 2023 IEEE.

关键词： Telemedicine

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Egyptian Hieroglyphics Handwriting Character Classification Using CNN with Explainable AI: SHAP and LIME

Egyptian Hieroglyphics Handwriting Character Classification ...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Md. Ismiel Hossen Abir M D. Shaek Ahmed Nurul Bashar Hasibul Islam Peyal Dept. of Computer Science & Engineering International Standard University Dhaka Bangladesh Dept. of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Egyptian hieroglyphics, one of the oldest writing and human communication systems. In our modern life, it's challenging to interpret this language due to its complex visual symbols. In this study, we propose a Convolutional Neural Network (CNN) model for classifying Egyptian hieroglyphic handwriting characters. The dataset provides 18 different class of handwritten hieroglyph character images. To enhance model interpretability, we apply explainable AI techniques, specifically SHAP and LIME, to identify regions that influence model predictions. The results that our custom CNN model achieves train accuracy 90.62%, validation accuracy 88.25%, and test accuracy of 84.5%, with specific characters showing high classification performance. Also, AUC score reflects 0.99 to 1.00 for each of the classes.

关键词： Support vector machines Visualization Accuracy Translation Explainable AI Computational modeling Symbols Writing Predictive models Convolutional neural networks

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