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2022 International Conference for Advancement in Technology, ICONAT 2022

作者： Nayak, Arijit De, Sourav Bhattacharyya, Siddhartha Mukhopadhyay, Debarka Muhammad, Khan Gorbachev, Sergey Alumnus Software Limited Kolkata India Cooch Behar Govt. Engineering College Dept. of Computer Science Engineering Cooch Behar India Rajnagar Mahavidyalaya Birbhum India Department of Computer Science and Engineering Bangalore India Sungkyunkwan University Department of Applied Artificial Intelligence Seoul Korea Republic of National Research Tomsk State University Department of Innovative Technologies Tomsk Russia

ISBN: (纸本)9781665425773

Blockchain Technology is gaining popularity throughout various industry verticals due to its data decentralization and tamper-evident nature. Machine Learning (ML) is all about embedding a learning capability to computing machines so that the machine can learn based on historical data in a way how human beings learn things. An important part of ML is the process of learning which needs humongous processing capability and hence it is time-consuming. Significant benefits have been predicted from the integration of these two technologies. Making a complete blockchain network intelligent in a simple and efficient way is a major challenge. In this work, a Multi Layer Perceptron (MLP) model is implanted in every node of the blockchain network. An efficient technique is proposed to make an intelligent blockchain network in minimum possible time and using minimum processing power. During the network formation, every node of the network has knowledge of the model architecture. At some point in time, the model of the randomly selected node gets trained. After completion of the training of that node, the intelligence is replicated to the entire network. © 2022 IEEE.

关键词： Blockchain

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Data Fusion Architecture Empowered with Deep Learning for Breast Cancer Classification

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computers, Materials & Continua 2023年第12期77卷 2813-2831页

作者： Sahar Arooj Muhammad Farhan Khan Tariq Shahzad Muhammad Adnan Khan Muhammad Umar Nasir Muhammad Zubair Atta-ur-Rahman Khmaies Ouahada Faculty of Computing Riphah International UniversityIslamabad45000Pakistan Department of Forensic Sciences University of Health SciencesLahore54000Pakistan Department of Electrical and Electronic Engineering Science University of JohannesburgP.O.Box 524Johannesburg2006South Africa School of Computing Skyline University CollegeUniversity City SharjahSharjah1797United Arab Emirates Riphah School of Computing and Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Software Faculty of Artificial Intelligence&SoftwareGachon UniversitySeongnamGyeonggido13120Korea Department of Computer Science Bahria UniversityLahore CampusLahore54000Pakistan Department of Computer Science College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)P.O.Box 1982Dammam31441Saudi Arabia

Breast cancer(BC)is the most widespread tumor in females worldwide and is a severe public health *** is the leading reason of death affecting females between the ages of 20 to 59 around the *** detection and therapy can help women receive effective treatment and,as a result,decrease the rate of breast cancer *** cancer tumor develops when cells grow improperly and attack the healthy tissue in the human *** are classified as benign or malignant,and the absence of cancer in the breast is considered *** learning,machine learning,and transfer learning models are applied to detect and identify cancerous tissue like *** research assists in the identification and classification of *** implemented the pre-trained model AlexNet and proposed model Breast cancer identification and classification(BCIC),which are machine learning-based models,by evaluating them in the form of comparative *** used 3 datasets,A,B,and *** fuzzed these datasets and got 2 datasets,A2C and *** A2C is the fusion of A,B,and C with 2 classes categorized as benign and *** B3C is the fusion of datasets A,B,and C with 3 classes classified as benign,malignant,and *** used customized AlexNet according to our datasets and BCIC in our proposed *** achieved an accuracy of 86.5%on Dataset B3C and 76.8%on Dataset A2C by using AlexNet,and we achieved the optimum accuracy of 94.5%on Dataset B3C and 94.9%on Dataset A2C by using proposed model BCIC at 40 epochs with 0.00008 learning *** proposed fuzzed dataset model using transfer *** fuzzed three datasets to get more accurate results and the proposed model achieved the highest prediction accuracy using fuzzed dataset transfer learning technique.

关键词： Breast cancer classification deep learning machine learning transfer learning learning rate

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Development of Automatic Detection and Monitoring Application System for Peristaltic Condition based on Machine Learning and Internet of Thing (IoT) System

Development of Automatic Detection and Monitoring Applicatio...

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International Conference of computer and Informatics engineering (IC2IE)

作者： Winda Astuti Giovanni Benedict Davin K Muhammad Nurul Puji Matthew Kunaifi Yuli Astuti Andriatin Automotive and Robotics Program Computer Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia Cilacap Regional General Hospital Cilacap Indonesia

ISBN: (数字)9798331505530

ISBN: (纸本)9798331505547

Poor sanitation and limited access to clean water are the main causes of increased risk of gastrointestinal diseases. In Indonesia, environmental hygiene problems are the main cause of digestive tract disorders, which contribute to more than 25% of deaths each year according to data from the Ministry of Health. Digestive disease is one of the diseases that often appears due to Poor sanitation and limited access to clean water. The development of a digital stethoscope for early detection of problems in the digestive tract would be useful, especially in remote areas. The sound was recorded from a Mel Frequency Cepstral Coefficient (MFCC) technique. The resulting features extracted from the stethoscope sound signal, are used as the inputs for the machine learning which applied three types of method, they are Logistic Regression (LR), combination of Logistic Regression (LR) and K-Nearest Neighbors (KNN), combination of Logistic Regression (LR) and Support Vector Machine (SVM). The experiment of MFCC with three types of machine learning methods above, based the sound from a stethoscope. Experimental results show the combination of Logistic Regression (LR) and K-Nearest Neighbors (KNN) has exceeded results among other methods, with the training and testing accuracy of 90 % and 88 % respectively. They confirm the effectiveness of the proposed intelligent automatic stethoscope sound identification based on Mel-Frequency Cepstral Coefficient (MFCC) and Linear Regression. Furthermore, the system continuously saves and monitors the result of the patient.

关键词： Support vector machines Training Logistic regression Accuracy Stethoscope Machine learning Nearest neighbor methods Mel frequency cepstral coefficient Diseases Testing

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Comparative Analysis of Optimal ECG-Lead Subsets for Arrhythmia/ECG Pattern Classification: Deep Learning Versus Conventional Methods

SSRN

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SSRN 2024年

作者： Reznichenko, Serhii Whitaker, John Ni, Zixuan Zhou, Shijie The Department of Computer Science and Software Engineering Miami University Oxford45056 OH United States Division of Biomedical Engineering and Imaging Sciences College of Computer Science and Technology Zhejiang University Hangzhou China The Department of Chemical Paper and Biomedical Engineering Miami University Oxford45056 OH United States

Background:Deep Learning methods have been constantly growing in popularity. Prior to Deep Learning methods, Conventional Machine Learning has been effectively used for the classification of heart arrhythmias/ECG patterns. However, there has limited research into the potential benefits of Deep Learning methods over conventional Machine Learning. Given that Deep Learning often demands larger datasets to achieve robust classification results, using conventional techniques could yield superior accuracy. Additionally, the classification of heart arrhythmias/ECG patterns is often dependent on specific ECG leads for acccurate classification. It is unknown how Deep Learning and conventional Machine Learning methods perform on reduced subsets of ECG ***:The goal of the study is to compare the performance of a Deep Learning method to a conventional Machine Learning method to classify 8 different arrhythmias/ECG patterns using subsets of a full 12-lead ECG .Methods:We used a public dataset from the PhysioNet Cardiology Challenge 2020. The research involved the utilization of both Deep Learning and Conventional Machine Learning methods. For the Deep Learning method, we trained a CNN classifier and extracted 32 features for each ECG lead, which were then used in a feedforward neural network. For the conventional Machine Learning method, we employed a Random Forest classifier that operated on manually extracted features from the ECG signals. The optimal subsets of ECG leads were identified using recursive feature elimination for both of the methods. This process involves training a classifier, evaluating its performance, identifying the least important lead, eliminating it, and then iteratively repeating the procedure. We determined lead importance using Random Forest feature importance scores for the conventional Machine Learning method and used SHAP values for Deep Learning. Finally, the subset of ECG leads that was statistically significant (p-value Results:We id

关键词： Electrocardiograms

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Design of Middleware to Support Auto-scaling in Docker-Based Multi Host Environment 11th

Design of Middleware to Support Auto-scaling in Docker-Based...

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11th International Conference on computer Science and its Applications, CSA 2019 and 14th KIPS International Conference on Ubiquitous Information Technologies and Applications, CUTE 2019

作者： Chae, Minsu Han, Sangwook Lee, Hwa Min Department of Computer Science Soonchunhyang University Asan Korea Republic of Department of Computer Software Engineering Soonchunhyang University Asan Korea Republic of

ISBN: (纸本)9789811593420

With the spread of smart devices, the use of big data, and the proliferation of the Internet of Things, virtualization technology for cloud servers have become important worldwide. Also, research has been conducted to efficiently manage the resources of hosts in VMs. Container-based virtualization has less performance degradation than VMs because there is no emulation for the operating system. Using the Docker API is slow to measure. In this paper, we implement the resource measurement module of Job nodes and design middleware that supports auto-scaling in auto-scaling module and Docker-based multi-host environment. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Middleware

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Sentiment Analysis of User Review on Indonesian Food and Beverage Group using Machine Learning Techniques

Sentiment Analysis of User Review on Indonesian Food and Bev...

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2022 IEEE Creative Communication and Innovative Technology, ICCIT 2022

作者： Yanfi, Yanfi Heryadi, Yaya Lukas, Lukas Suparta, Wayan Arifin, Yulyani Bina Nusantara University Binus Graduate Program Computer Science School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia Jakarta12930 Indonesia Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Department of Electrical Engineering Yogyakarta55281 Indonesia

ISBN: (纸本)9781665468763

A vast number of technologies based on Artificial Intelligence (AI) have proliferated into various application domains. As part of its objectives to develop agents which can behave and think like humans, some branches of AI have focused on developing various algorithms that can learn from input data without explicitly being programmed. In the food industry, notably in restaurants, smartphone technology is typically used to collect data from customers efficiently such as orders or feedback toward the services provided. The use of smartphones by some restaurants is motivated by their convenience and efficiency. Furthermore, various promotions, discounts, and membership benefits offered by cellular service providers have attracted the use of smartphones as devices for improving restaurant services to their customers. This study aimed to conduct a sentiment analysis related to multi-brand restaurants in Indonesia based on customer feedback. The data retrieved from the Google Play Store consists of 756 reviews from Boga, Champs, Hangry, Ismaya, Kulo, and Union groups. In this study, TF-IDF was used as a vectorizer to represent customer feedback as numeric vectors. The polarity sentiment of customer feedback was recognized using classifier models based on machine learning such as Logistic Regression, Naive Bayes (NB), Random Forest, and Support Vector Machine (SVM) as the classifier. The empirical results showed that SVM has the best accuracy of 93.10 % average accuracy followed by Logistic Regression with 92.41 %, Random Forest with 84.14 % average accuracy, and Naive Bayes with 76.55 % average accuracy. © 2022 IEEE.

关键词： Sentiment analysis

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Automatic Instantiation of Assurance Cases from Patterns Using Large Language Models

arXiv

引用

arXiv 2024年

作者： Odu, Oluwafemi Belle, Alvine B. Wang, Song Kpodjedo, Segla Lethbridge, Timothy C. Hemmati, Hadi Lassonde School Of Engineering York University Toronto Canada School of Electrical Engineering and Computer Science University of Ottawa Ottawa Canada Department of Software Engineering and Information Technology École de technologie supérieure Montreal Canada

An assurance case is a structured set of arguments supported by evidence, demonstrating that a system’s non-functional requirements (e.g., safety, security, reliability) have been correctly implemented. Assurance case patterns serve as templates derived from previous successful assurance cases, aimed at facilitating the creation of new assurance cases. Despite the use of these patterns to generate assurance cases, their instantiation remains a largely manual and error-prone process that heavily relies on domain expertise. Thus, exploring techniques to support their automatic instantiation becomes crucial. This study aims to investigate the potential of Large Language Models (LLMs) in automating the generation of assurance cases that comply with specific patterns. Specifically, we formalize assurance case patterns using predicate-based rules and then utilize LLMs, i.e., GPT-4o and GPT-4 Turbo, to automatically instantiate assurance cases from these formalized patterns. Our findings suggest that LLMs can generate assurance cases that comply with the given patterns. However, this study also highlights that LLMs may struggle with understanding some nuances related to pattern-specific relationships. While LLMs exhibit potential in the automatic generation of assurance cases, their capabilities still fall short compared to human experts. Therefore, a semi-automatic approach to instantiating assurance cases may be more practical at this time. Copyright © 2024, The Authors. All rights reserved.

关键词： Requirements engineering

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Natural Language Processing Meets Hardware Trojan Detection: Automating Security of FPGAs

Natural Language Processing Meets Hardware Trojan Detection:...

引用

IEEE computer Society Annual Symposium on VLSI

作者： Vaishnavi More Aaditya Chaudhari Barnaboss Puli Vasavi Vuppala Jaya Dofe Wafi Danesh Department of Electrical and Computer Engineering California State University Fullerton CA USA Division of Engineering Program The State University of New York New Paltz NY USA

ISBN: (数字)9798350354119

ISBN: (纸本)9798350354126

The inherent runtime reconfiguration capability of field programmable gate array (FPGA) has been a key feature for deployment in various application scenarios, such as data centers, cloud computing, and edge computing, among others. In such applications, reconfiguration is achieved via remote access, which allows multiple users to utilize FPGA resources concurrently and modify the configuration bitstream. An adversary can exploit the accessibility of the configuration bitstream to insert a hardware Trojan (HT) into the FPGA, thereby creating a critical security vulnerability. Since the HT is designed to remain dormant to avoid detection, it can bypass conventional verification and vali-dation techniques. However, any HT inserted in a configuration bitstream must leave a trace even if it is dormant. This paper proposes a supervised learning method using a deep, recurrent neural network (RNN) algorithm to identify such malicious configuration bitstreams in FPGAs. By analysing the patterns present in the bitstream, the proposed method is able to identify any anomalies present in the implemented design. Our method is applied to three ISCAS 85 benchmark circuits of various sizes and topology, implemented on a Xilinx Artix-7 FPGA. Our experimental results showed a maximum accuracy of 93% in detecting HT in bitstreams.

关键词： Recurrent neural networks Accuracy Benchmark testing Very large scale integration Hardware Topology Trojan horses

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Analysis of Parallel Graph Applications

Analysis of Parallel Graph Applications

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Funda Atik Serif Yesil Hamza Ouarnoughi Smail Niar Ozcan Ozturk Computer Science and Engineering Department Washington University in St. Louis Doimukh MI USA NVidia Urbana IL USA Department of Computer Science Université Polytechnique Hauts-de-France Valenciennes France LAMIH/CNRS Université Polytechnique Hauts-de-France Valenciennes France Computer Science and Engineering Program Electronics Engineering Program Sabanci University Istanbul Turkey

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

Despite the increasing computing power of shared memory systems with high core counts, parallel graph processing frameworks cannot exploit it effectively. The reason behind this is the inherent challenges in parallel graph algorithms, which are efficient management of dynamically created tasks and irregular data access patterns. In this paper, we categorize several popular design choices into three design dimensions: (i) execution mode, (ii) data access pattern, and (iii) work activation. We provide their high-level parallel implementations and analyze various implementations of three representative iterative graph algorithms by considering these design dimensions. To gain a better understanding of design choices, we examine their impacts on performance, communication, scalability, and work efficiency. We also investigate the communication characteristics of the design choices on two state-of-the-art shared-memory platforms by performing micro-architectural analysis. Our microarchitectural analysis reveals that a topology-driven, pull-based model gives up to $20 x$ better performance.

关键词： Analytical models Microarchitecture Scalability Heuristic algorithms Benchmark testing Throughput Iterative algorithms System-on-chip

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Application of Soft Computing for Time Series Water-Level Prediction in Jamuna River

International Journal of Systematic Innovation

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International Journal of Systematic Innovation 2021年第6期6卷 13-21页

作者： Sarkar, Subodh Chandra Bashar, Abul Mahmud, Mohammad Sultan Rasel, Risul Islam Department of Computer Science and Engineering World University of Bangladesh Dhaka 1205 Bangladesh College of Computer Science and Software Engineering Shenzhen University Shenzhen 518060 China School of Science and Engineering Chittagong Independent University Chattogram 4000 Bangladesh

Time series analysis is one of the essential and complicated research methods. It is a well-known fact that improving time series prediction accuracy is a vital yet challenging issue. Recently, soft computing has become popular in time series forecasting in various application areas. Soft computing is a fusion of research of evolutionary and genetic algorithms, neural networks, fuzzy set theory, and fuzzy systems and provides rapid dissemination of results. This study investigates a model for time series water-level prediction using soft computing techniques which is reliable and effective. To illustrate the applicability and capability of soft computing, the Jamuna river, Bangladesh, was used as a case study. We used four areas of the Jamuna river (i.e., Aricha, Bahdurabad, Shariacandi, and Sirajganj) water-level and rainfall events with daily data collected in the past 12 years. In experiments, past 2 to 4 days’ time-series wa-ter-level with and without rainfall has been applied to predict 1 to 4 days ahead water-level. The experimental results demonstrated that the adaptive neurofuzzy inference system (ANFIS) performs superiorly to traditional methods, such as nonlinear autoregressive neural network with external input (NARX) and focused time-delay neural network (FTDNN). © 2021

关键词： ANFIS FTDNN NARX soft-computing Time series analysis water-level prediction

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