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1st International Conference on Advanced engineering and Technologies, ICONNIC 2023

作者： Wicaksana, Cakra Adipura Hidayat, Wahyu Santoso, Muhammad Iman Alfanz, Rocky Fatkhurrokhman, Mohammad Yusfia Hafid, A. University of Sultan Ageng Tirtayasa Department of Informatics Cilegon Indonesia University of Sultan Ageng Tirtayasa Department of Electrical Engineering Cilegon Indonesia Vocational Education University of Sultan Ageng Tirtayasa Department of Electrical Engineering Serang Indonesia Sebelas Maret University Department of Informatics and Computer Engineering Education Surakarta Indonesia

ISBN: (纸本)9798350306484

Eye-detection technology is used to improve the effectiveness of advertisements. This research uses a mirrorless camera, the Harr Cascade method to detect eyes and faces, and the NVIDIA Jetson Nano as a microcontroller. The results show that the system can detect with high accuracy, even with differences in distance and face accuracy, even with differences in distance and the number of people looking at the camera. camera. This system can be used to calculate the number of effective ad readers. effective advertising readership. In the three cases tested, the system had an accuracy of 91 %,97%, and 90% for calculating the number of people viewing an advertisement, as well as an accuracy of 87% for face detection. © 2023 IEEE.

关键词： Cameras

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End-to-end differentiability and tensor processing unit computing to accelerate materials’ inverse design

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npj Computational Materials 2023年第1期9卷 1072-1083页

作者： Han Liu Yuhan Liu Kevin Li Zhangji Zhao Samuel S.Schoenholz Ekin D.Cubuk Puneet Gupta Mathieu Bauchy SOlids inFormaTics AI-Laboratory(SOFT-AI-Lab) College of Polymer Science and EngineeringSichuan UniversityChengdu 610065China AIMSOLID Research Wuhan 430223China Physics of AmoRphous and Inorganic Solids Laboratory(PARISlab) Department of Civil and Environmental EngineeringUniversity of CaliforniaLos AngelesCA 90095USA Department of Computer Science University of CaliforniaLos AngelesCA 90095USA Google Research Brain TeamMountain ViewCAUSA Department of Electrical and Computer Engineering University of CaliforniaLos AngelesCA 90095USA

Numerical simulations have revolutionized material ***,although simulations excel at mapping an input material to its output property,their direct application to inverse design has traditionally been limited by their high computing cost and lack of ***,taking the example of the inverse design of a porous matrix featuring targeted sorption isotherm,we introduce a computational inverse design framework that addresses these challenges,by programming differentiable simulation on TensorFlow platform that leverages automated end-to-end *** to its differentiability,the simulation is used to directly train a deep generative model,which outputs an optimal porous matrix based on an arbitrary input sorption isotherm ***,this inverse design pipeline leverages the power of tensor processing units(TPU)—an emerging family of dedicated chips,which,although they are specialized in deep learning,are flexible enough for intensive scientific *** approach holds promise to accelerate inverse materials design.

关键词： enough inverse isotherm

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Diesel Engine Fault Detection using Deep Learning Based on LSTM 7

Diesel Engine Fault Detection using Deep Learning Based on L...

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7th International Conference on Electrical, Telecommunication and computer engineering, ELTICOM 2023

作者： Naryanto, Rizqi Fitri Delimayanti, Mera Kartika Naryaningsih, Agustien Warsuta, Bambang Adi, Rizky Setiawan, Bima Aji Universitas Negeri Semarang Faculty of Engineering Department of Mechanical Engineering Indonesia National Research and Innovation Agency Fisheries Reseach Center Indonesia Politeknik Negeri Jakarta Department of Computer and Informatics Engineering Indonesia

ISBN: (纸本)9798350385526

This work aims to create a deep learning model utilizing Long Short-Term Memory (LSTM) as a classification model to detect and diagnose potential problems in diesel engines. The default dataset comprises 3,500 data entries and four distinct dataset types. White noise in the dataset is attributed to additive Gaussian white noise (AWGN). The main contributions of this study comprise developing a classification model aimed at identifying damage in diesel engines, as well as evaluating the performance of the Long Short-Term Memory (LSTM) method. The K-Fold cross-validation technique was utilized, where the value of k was set at 5. This study examines the damage classification in diesel engines using four datasets: default 0dB Noise, 15dB Noise, 30dB Noise, and 60dB Noise. The K-Fold Cross Validation methodology was employed, utilizing a value of k=5. The training and validation processes were conducted iteratively, employing a learning rate of 0.00005 during the training phase. The training phase utilized a mini-batch size of 128, and the maximum number of epochs was set at 10,000. The results indicate that the dataset containing 60dB noise consistently demonstrates the highest level of accuracy, with an average accuracy of 97.91% across the five folds and the efficacy of Long Short-Term Memory (LSTM) models in classification tasks is improved in the presence of white noise. © 2023 IEEE.

关键词： Long short-term memory

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Crude Palm Oil Price Forecasting Based on ECoS-MARS: in Data Science Models 11

Crude Palm Oil Price Forecasting Based on ECoS-MARS: in Data...

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11th International Conference on Cyber and IT Service Management, CITSM 2023

作者： Al-Khowarizmi Ramlan, Fitria Wulandari Efendi, Syahril Lubis, Arif Ridho Riza, Ferdy Amrullah Sary, Yoshida Universitas Muhammadiyah Sumatera Utara Department of Information Technology Medan Indonesia National University Ireland College of Engineering and Informatics Galway Ireland Universitas Sumatera Utara Department of Computer Science Medan Indonesia Politeknik Negeri Medan Department of Computer Engineering & Informatics Medan Indonesia Universitas Muhammadiyah Sumatera Utara Department of Information System Medan Indonesia

ISBN: (纸本)9798350305968

The crude palm oil (CPO) industry is highly competitive due to fluctuating and unpredictable prices. To accurately predict future CPO prices, a forecasting technique is necessary. This can be achieved through various regression techniques and data mining approaches. This paper proposes a Multivariate Adaptive Regression Splines (MARS) model, which utilizes the ECoS machine learning algorithm to obtain optimal forecasting values. This research mainly focuses on forecasting CPO prices using the ECoS-MARS approach. The functional basis (BF) validation has yielded an accuracy of 0.0532%. Furthermore, validation has been conducted to achieve accurate forecasting values, resulting in an accuracy of 0.11714%. The best parameter combination for this forecasting model consists of a learning rate of 0.6 for both learning rates 1 and 2, a sensitivity threshold of 0.3, and an error threshold of 0.05. © 2023 IEEE.

关键词： Forecasting

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Bounded and Uniform Energy-based Out-of-distribution Detection for Graphs 41

Bounded and Uniform Energy-based Out-of-distribution Detecti...

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41st International Conference on Machine Learning, ICML 2024

作者： Yang, Shenzhi Liang, Bin Liu, An Gui, Lin Yao, Xingkai Zhang, Xiaofang School of Computer Science and Technology Soochow University Suzhou China Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong Department of Informatics King's College London London United Kingdom

Given the critical role of graphs in real-world applications and their high-security requirements, improving the ability of graph neural networks (GNNs) to detect out-of-distribution (OOD) data is an urgent research problem. The recent work GNNSAFE (Wu et al., 2023) proposes a framework based on the aggregation of negative energy scores that significantly improves the performance of GNNs to detect node-level OOD data. However, our study finds that score aggregation among nodes is susceptible to extreme values due to the unboundedness of the negative energy scores and logit shifts, which severely limits the accuracy of GNNs in detecting node-level OOD data. In this paper, we propose NODESAFE: reducing the generation of extreme scores of nodes by adding two optimization terms that make the negative energy scores bounded and mitigate the logit shift. Experimental results show that our approach dramatically improves the ability of GNNs to detect OOD data at the node level, e.g., in detecting OOD data induced by Structure Manipulation, the metric of FPR95 (lower is better) in scenarios without (with) OOD data exposure are reduced from the current SOTA by 28.4% (22.7%). The code is available via https://***/ShenzhiYang2000/NODESAFE. Copyright 2024 by the author(s)

关键词： Graph neural networks

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Optimization of SVM Classification Accuracy with Bayesian Optimization Utilizing Data Augmentation 6

Optimization of SVM Classification Accuracy with Bayesian Op...

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6th International Conference on computer and informatics engineering, IC2IE 2023

作者： Lubis, Arif Ridho Lase, Yuyun Yusnida Darwis, R.A. Witarsyah, Deden Politeknik Negeri Medan Department of Computer Engineering and Informatics Medan Indonesia Politeknik Negeri Medan Department of Mechanical Engineering Medan Indonesia School of Industrial and System Engineering Telkom University Bandung Indonesia

ISBN: (纸本)9798350345162

Optimizing the accuracy of SVM classification is one of the most important things in the world of machine learning. This study will use the SVM (Support Vector Machine) algorithm in classifying. This study aims to improve the classification accuracy of the Support Vector Machine (SVM) model by utilizing Bayesian Optimization techniques and data augmentation. The Bayesian Optimization technique will be used to find the most optimal values for the kernel and C parameters in the SVM model. While data augmentation is done to improve the quality of the training data by adding variations and data features. This research uses a synonym replacement technique in data augmentation. The data used was obtained from social media Twitter. The results of this study are in the form of a model with a combination of Bayesian Optimization and augmentation. The accuracy obtained with the SVM model using Bayesian Optimization and data augmentation is 89%. However, the conventional SVM model without Bayesian Optimization only produces 82.19% accuracy so the SVM model with Bayesian Optimization increases the accuracy by 7%. © 2023 IEEE.

关键词： Bayesian Optimization Data augmentation Media Social SVM

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Optimizing the Performance of Sentiment Analysis on Social Media Data Through Feature Selection in Preprocessing Techniques 6

Optimizing the Performance of Sentiment Analysis on Social M...

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6th International Conference on computer and informatics engineering, IC2IE 2023

作者： Lubis, Arif Ridho Yusnida, Yuyun Lase Darwis, R.A. Witarsyah, Deden Politeknik Negeri Medan Department of Computer Engineering and Informatics Medan Indonesia Politeknik Negeri Medan Department of Mechanical Engineering Medan Indonesia School of Industrial and System Engineering Telkom University Bandung Indonesia

ISBN: (纸本)9798350345162

The digital era directly builds user needs in communicating using social media, the data generated by social media has a large volume so it needs to be analyzed with a preprocessing process. The problems that exist in social media data contain a lot of repeated words with non-standard sentence structures and still contain lots of irregular punctuation making it difficult to determine which features are considered important in a sentence to carry out a sentiment analysis process in identifying positive, negative, and neutral sentences. So it is necessary to select features using preprocessing techniques using Recursive Feature Elimination (RFE). This study produces a comparison that shows that the selection of features and combinations in using features results in the accuracy of each algorithm increasing when compared to using one feature. The results showed that the CNN algorithm has an accuracy value of 0.95, the LSTM algorithm is 0.94 and the RNN algorithm is 0.93. when compared to the use of one feature, it increases by 0.09 for the CNN algorithm, increases by 0.07 for the LSTM algorithm, and increases by 0.05 for the RNN algorithm. © 2023 IEEE.

关键词： data feature selection optimizing preprocessing

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A TOPOLOGICAL CHARACTERIZATION OF THE EXISTENCE OF w-STABLE SETS

arXiv

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arXiv 2024年

作者： Andrikopoulos, Athanasios Sampanis, Nikolaos Dept. of Computer Engineering and Informatics University of Patras Patras26504 Greece Department of Computer Engineering & Informatics University of Patras Greece

The theory of optimal choice sets is a solution theory that has a long and well-established tradition in social choice and game theories. Some of important general solution concepts of choice problems when the set of best alternatives does not exist (this problem occurs when the preferences yielded by an economic process are cyclic) is the Stable Set (Von Neumann-Morgenstern set) and its variants (Generalized Stable set, Extended Stable set, m-Stable set and w-Stable set). The theory of w-stable sets solution is more realistic because: (1) It solves the existence problem of solution;(2) It expands the notions of maximal alternative set and (3) The concept of stability is defined in such a way as to prevent a chosen alternative from being dominated by another alternative and sets this stability within the solution. In this paper, we present a topological characterization of the existence of w-Stable sets solution of arbitrary binary relations over non-finite sets of alternatives. Copyright © 2024, The Authors. All rights reserved.

关键词： Game theory

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Neuroadaptive Incentivization in Healthcare using Blockchain and IoT

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SN computer Science 2024年第1期5卷 13页

作者： Kumar, Ajay Singh, Rajiv Ranjan Chatterjee, Indranath Sharma, Nikita Rana, Vaishali Department of Computer Science and Informatics Central University of Himachal Pradesh Himachal Pradesh Dharamshala India Department of Cyber Security and Networks Glasgow Caledonian University Glasgow Scotland United Kingdom Department of Computer Engineering Tongmyong University Busan South Korea School of Technology Woxsen University Hyderabad India

Financially incentivizing health-related behaviors can improve health record outcomes and reduce healthcare costs. Blockchain and IoT technologies can be used to develop safe and transparent incentive schemes in healthcare. IoT devices, such as body sensor networks and wearable sensors, etc. connect the physical and digital world making it easier to collect useful health-related data for further analysis. There are, however, many security and privacy issues with the use of IoT. Some of these IoT security issues can be alleviated using Blockchain technology. Incorporating neuroadaptive technology can result in more personalized and effective therapies using machine learning algorithms and real-time feedback. The research investigates the possibilities of neuroadaptive incentivization in healthcare using Blockchain and IoT on patient health records. The core idea is to incentivize patients to keep their health parameters within standard range thereby reducing the load on healthcare system. In summary, we have presented a proof of concept for neuroadaptive incentivization in healthcare using Blockchain and IoT and discuss various applications and implementation challenges. © 2023, The Author(s).

关键词： Blockchain Healthcare IoT Neuroadaptive Smart contracts

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Automatic Detection of Weapons in Surveillance Cameras Using Efficient-Net

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computers, Materials & Continua 2022年第9期72卷 4615-4630页

作者： Erssa Arif Syed Khuram Shahzad Muhammad Waseem Iqbal Muhammad Arfan Jaffar Abdullah S.Alshahrani Ahmed Alghamdi Department of Computer Science Superior UniversityLahore54000Pakistan Department of Informatics&Systems University of Management&TechnologyLahore54000Pakistan Department of Software Engineering Superior UniversityLahore54000Pakistan Faculty of Computer Science&Information Technology Superior UniversityLahore54000Pakistan Department of Computer Science&Artificial Intelligence College of Computer Science&EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia

The conventional Close circuit television(CCTV)cameras-based surveillance and control systems require human resource *** all the criminal activities take place using weapons mostly a handheld gun,revolver,pistol,swords ***,automatic weapons detection is a vital requirement now a *** current research is concerned about the real-time detection of weapons for the surveillance cameras with an implementation of weapon detection using Efficient–*** time datasets,from local surveillance department’s test sessions are used for model training and *** consist of local environment images and videos from different type and resolution cameras that minimize the *** research also contributes in the making of Efficient-Net that is experimented and results in a positive *** results are also been represented in graphs and in calculations for the representation of results during training and results after training are also shown to represent our research ***-Net algorithm gives better results than existing *** using Efficient-Net algorithms the accuracy achieved 98.12%when epochs increase as compared to other algorithms.

关键词： Detection algorithms machine learning machine vision video surveillance

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