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Monitoring Stress Level Through EDA by Using Convex Optimization 1

1st International Conference on Electronics, Biomedical engineering, and Health informatics, ICEBEHI 2020

作者： Fajriaty, Nuzula Dwi Rahma, Osmalina Nur Putri, Yang Sa’ada Kamila Ariyansah Putra, Alfian Pramudita Rahmatillah, Akif Ain, Khusnul Biomedical Engineering Study Program Department of Physics Faculty of Science and Technology Universitas Airlangga Surabaya60115 Indonesia Biomedical Signals and Systems Research Group Faculty of Science and Technology Universitas Airlangga Surabaya60115 Indonesia

ISBN: (数字)9789813369269

ISBN: (纸本)9789813369252

This study designed an effective system for monitoring stress levels based on Electrodermal Activity (EDA) sensors by knowing the signal patterns and characteristics using convex optimization decomposition. The EDA sensor considered accurate and sensitive for identifying stress by analyzing the skin conductivity (SC) due to the changes in sympathetic nerve activity. However, the SC signal consists of phasic and tonic components, which needed to decompose to analyze stress levels. The SC signals also followed by the white Gaussian noise, which represents the error value. Hence, deconvolution is a crucial stage for the further process because the quality of the measurement depends on this result. This study aims to deconvolve the SC signal using the convex optimization method (cvxEDA). This model is physiology inspired by EDA based on Bayesian statistics, convex mathematical optimization, and sparsity. This research conducted with 18 subjects through three sessions of measurement. The given stimuli arise in each session to increase the level of stress. The results showed that this method could separate and identify SC. The Phasic component shows an increase in the stimulus of each session, as seen from the number of peaks. In contrast, there were no significant differences in the tonic component. This study shows that the phasic component is closely related to changes in sudomotor nerve activity (SMNA) and response to a stressor, which could be useful to classify stress levels in the future study. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convex optimization

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Implementation of Malaria Detection Using MobileNetV2 Pretrained Model for Improved Deep Learning Based Diagnostics

Implementation of Malaria Detection Using MobileNetV2 Pretra...

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International Conference on Computing, engineering, and Design (ICCED)

作者： Rahmat Hidayat Achmad Solichin Adhitia Erfina Sudin Saepudin Muhamad Muslih Mohd Zainuri Saringat Master of Computer Science Budi Luhur University Jakarta Indonesia Information Systems Study Program Nusa Putra University Sukabumi Indonesia Department of Software Engineering Faculty of Computer Science and Information Technology Tun Hussein Onn Malaysia University

ISBN: (数字)9798331529376

ISBN: (纸本)9798331529383

The Plasmodium parasite, which causes malaria, is an acute fever illness that infects people when a female Anopheles mosquito bites them. It is predicted that malaria would claim 619,000 lives in 2021, with 96% of those deaths occurring in the African continent. We can achieve this by using a microscope to examine thick and thin blood smears. The proficiency of a microscope examiner is crucial for doing microscopic examinations. Consider how time-consuming, ineffective, and costly it would be to examine thousands of malaria cases. Consequently, Creating an automated method for detecting malaria parasites is the aim of this study. We employ a MobileNetV2 pretrained model with CNN technology. Because it has been trained on dozens or even millions of data points, this pretrained model is incredibly light but dependable. There are two main benefits of automatic malaria parasite detection: firstly, it can offer a more accurate diagnosis, particularly in locations with limited resources; secondly, it lowers diagnostic expenses. The optimizer utilizes Adam Weight, the criteria uses NLLLoss, and the model is trained using 32 for batch_size. In the fourteenth epoch, we obtained the maximum accuracy score of 96.26% based on the training data. The outcomes of the predictions demonstrate how excellent this score is. EfficienceNet, DenseNet, AlexNet, and other pretrained models are among the alternatives that scientists are advised to try training with.

关键词： Training Adaptation models Accuracy Costs Malaria Microscopy Data visualization Training data Cost function Data models

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Salt Hash Password Using MD5 Combination for Dictionary Attack Protection

Salt Hash Password Using MD5 Combination for Dictionary Atta...

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International Conference of Computer and informatics engineering (IC2IE)

作者： Agung Nugroho Teddy Mantoro Cyber Defense Engineering Study Program Sains and Defense Technology Faculty The Republic of Indonesia Defense University Bogor Indonesia Departement of Computer Science Media-Tech Lab Sampoerna University Jakarta Indonesia

Using MD5 hashing of passwords in web logins is highly vulnerable to dictionary attacks. One online application for cracking hashed passwords using a dictionary attack that is free and easy to use is ***. Even so, the use of hashing passwords using MD5 which has a low level of security is still commonly found to secure web login passwords. This research presents an increase in security through a combination of salt hash passwords when using MD5. The methodology of this study uses a quantitative experimental method to apply hash salt passwords using the MD5 combination. The password cracking test was carried out using the *** online hash cracking application on weak passwords such as “admin” with the addition of a password hash salt using the MD5 combination. The results showed that Salt Password using the MD5 combination could not be cracked by *** using a dictionary attack.

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High Level Learning Approach for High Level Collision Avoidance Using Sensors 1

High Level Learning Approach for High Level Collision Avoida...

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1st IEEE International Interdisciplinary Humanitarian Conference for Sustainability, IIHC 2022

作者： Naibaho, Lamhot Razauddin Vertivendan, L. Karunakaran, V. Oberoi, Ashish Vishwath, N.C. Ajay Christian University of Indonesia English Language Education Study Program Faculty of Letters and Languages Indonesia Gulf Colleges Saudia Arabia Department of Computer and Information Sciences Saudi Arabia Galgotias University Department of CSE Uttar Pradesh Greater Noida India Faculty of Engineering and Technology Department of CSE Bangalore India RIMT University Department of CSE Godindgarh Mandi India Parul Institute of Engineering and Technology Parul University Aeronautical Department India

ISBN: (纸本)9781665456876

Motion planning for many unmanned aircraft is challenging because they have a larger configuration space than self-driving automobile development (Automated guided vehicles). Additionally, there are more significant uncertainties and disruptions in UAV environments, which makes inter automatic navigation more difficult. In this letter, I proposed a 2 recurrent neural networks (RL) oriented multi-UAV collision avoidance technique by modeling the unpredictability or noise in the environment. Our objective is to create a strategy that can construct a path without clashing with anything using local noisy data. But unlike supervised algorithms, RL lacks a steady training data set with floor labels, thus its collision avoidance strategies often show significant fluctuation and are difficult to reproduce. To solve these issues, we created a two-stage training method for RL-based collision avoidance. We first optimize the policy, and then we utilize a supervised learning methodology with a loss function that encourages the agents to adopt the well-known reciprocity obstacle detection mechanism. In the second step, we use transmission to fine-tune the policy. The complete computer simulation findings demonstrate that this approach can handle noisy local views with erratic sound levels and can design moment & accident paths under inadequate sensing. We review the impact of our policies in a variety of ways. © 2022 IEEE.

关键词： Recurrent neural networks

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Catalytic cracking of polyethylene terephthalate (PET) plastic waste and palm fibre mixtures using Ni-USY zeolite catalyst 6

Catalytic cracking of polyethylene terephthalate (PET) plast...

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6th International Conference on Mathematics, Science, and Education, ICMSE 2019

作者： Nazarudin Jayanti, N. Alfernando, O. Prabasari, I.G. Ulyarti Sarip, R. Chemistry Engineering Study Program Faculty of Engineering Universitas Jambi Indonesia Center of Energy and Nanomaterial Study Universitas Jambi Indonesia Agricultural Product Technology Study Program Faculty of Agricultural Technology Universitas Jambi Indonesia Chemistry Department Faculty of Science University of Malaya Malaysia

This research was carried out to produce fuel (Oil Liquid Poduct/OLP) from catalytic cracking of a mixture of Polyethylene Terephthalate (PET) and fiber waste from palm oil. USY zeolite was previously ion-exchanged by three levels of Ni-metal concentration (1%, 2%, 3%) before used as catalyst in the cacking process. The catalytic cracking was carried out at rasio of sample to catalyst was 6:1, three levels of temperature (400°C, 425°C and 450°C) and three levels of reaction time (10, 20 and 30 minutes).The SEM images and EDX spectra of Ni-USY zeolite catalysts showed that all catalysts were in crystalline form of FAU framework. The highest of OLP percentage, 24.5%, was produced by catalytic cracking at 450°C for 10 minutes using Ni-USY prepared by ion-exchange using 2% concentration of Nickel Nitrate solution. © Published under licence by IOP Publishing Ltd.

关键词： Catalysts

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Paracetamol degradation in a dual-chamber rectangular membrane bioreactor using microbial fuel cell system with a microbial consortium from sewage sludge

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Case Studies in Chemical and Environmental engineering 2024年 9卷

作者： Utami, Tania Surya Arbianti, Rita Hidayatullah, Ibnu Maulana Yusupandi, Fauzi Hamdan, Mukti Fadilah Putri, Najah Riyadi, Fatimah Azizah Boopathy, Ramaraj Department of Chemical Engineering Faculty of Engineering Universitas Indonesia West Java Depok 16424 Indonesia Bioprocess Engineering Study Program Faculty of Engineering Universitas Indonesia West Java Depok 16424 Indonesia Research Center for Biomass Valorization Universitas Indonesia West Java Depok 16424 Indonesia Department of Chemical Engineering Institut Teknologi Sumatera Lampung Selatan Lampung 35365 Indonesia Department of Biological Sciences Nicholls State University Thibodaux 70310 LA United States

The public widely uses Paracetamol (PCT) as an analgesic drug to reduce pain and fever symptoms in the human body. The COVID-19 pandemic has led to an increase in PCT consumption, resulting in its presence in water bodies and establishing it as a common aquatic environmental pollutant. Conventional methods for PCT removal rely on chemical treatments, which necessitate harsh reaction conditions and incur high operational costs. This study aims to investigate the potential for PCT degradation within the anode environment of the Microbial Fuel Cell (MFC) system, using a consortium of bacteria isolated from sewage sludge. The initial PCT concentration and the pH of the anode environment were varied. The results showed that PCT degradation occurred within 72 hours. The highest PCT removal rate was achieved at elevated concentrations, specifically 28.54 ± 18.84% for an initial PCT concentration of 31.45 mg/L. Furthermore, the pH levels of the anode environment were observed to influence PCT degradation, with the highest degradation rate recorded at 48.69 ± 0.86% at a pH of 8.2. In this study, the highest average power density reached was 1174.42 mW/m² with an external resistance of 1000 Ω at an initial PCT concentration of 31.45 mg/L. Microbial community analysis was conducted, revealing that Burkholderia sp. dominated the reactor containing PCT. These findings offer valuable insights for the development of more efficient technologies for the removal of PCT from pharmaceutical wastewater using natural microbial communities. © 2023 The Authors

关键词： COVID-19 Dual chamber microbial fuel cell Microbial consortium Paracetamol Pollutant degradation

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The influence of aging time variations on the fatigue life of aluminum alloy 2024-T351

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AIP Conference Proceedings 2024年第1期3053卷

作者： I. Made Astika I. Ketut Suarsana Dewa Ngakan Ketut Putra Negara Study Program of Mechanical Engineering Faculty of Engineering Udayana University Badung Bali 80361 Indonesia

Aluminum and its alloys are used to make a number of automotive components. It is due to aluminum’s light weight, anti-corrosiveness, formability, and affordable price. However, numerous procedures have been carried out to boost its strength due to mechanical characteristics, one of which is artificial aging. The effects of aging on the fatigue life of aluminum 2024-T351 will be examined in this study. The technique of treating the solution involved heating it to 500 oC for an hour and then immediately cooling it to room temperature. By reheating the aluminum alloy to 180 oC and maintaining heat there for a variety of 2, 4, and 6 hours, the fake aging process was continued. The goal was to obtain various precipitation types in anticipation of an increase in the material’s fatigue life. Then, a reversed bending test was carried out with loads of 1.0, 0.9, 0.7, 0.5, and 0.4 σu. The results of the study indicated that aging time had an influence on the fatigue life of aluminum alloy 2024-T351. Fatigue life lengthens with increasing aging time. The increase in fatigue life ranges from 13.3–63.3% compared to aluminum without heat treatment.

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Production of bioinsecticide from durian peel waste and Dioscorea hispida tuber extract by ultrasonic-assisted extraction

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AIP Conference Proceedings 2024年第1期2710卷

作者： Hasna Nisrina Sumayyah Widya Rahmanisa Fitri Tania Surya Utami Rita Arbianti Bioprocess Engineering Study Program Department of Chemical Engineering Faculty of Engineering University of Indonesia Depok 16424 Indonesia

A new armyworm (Spodoptera frugiperda) is rapidly spreading and causing damage to corn crops in Indonesia, thus prompting the need to control the pest with an effective, yet environmentally friendly pest control method, such as bioinsecticide. Natural raw materials like durian peel waste and Dioscorea hispida tuber can be processed to produce a natural bioinsecticide. To discover the optimum processing condition for bioinsecticide production, research is conducted to find the effective solvent material and concentration in ultrasonic-assisted extraction methods. Durian peel waste is extracted using ethanol 30% and methanol 70% as solvents with the ratio variation of 1:0, 0:1, 1:1, 1:2, 2:1. Meanwhile, Dioscorea hispida tubers are extracted with various ethanol concentrations as solvents (ethanol 50%, 70%, 80%, absolute, and distilled water). The result shows that the most effective solvent for durian peel waste extraction is a 2:1 ratio of ethanol and methanol, with the optimum yield of 12.58%. The result also shows that Dioscorea hispida tuber is best extracted with distilled water, yielding 34.70%. The efficacy test concludes that the best solvent variation for pest elimination is a 0:1 ratio of ethanol and methanol durian peel waste and ethanol 80% for Dioscorea hispida tuber. The LC-MS/MS test results identified that alkaloid, flavonoid, and fatty acid compounds potentially caused larvae mortality.

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Optimization of Certainty Factor Algorithm to Overcome Uncertainty in Expert System Identification of Pests and Diseases of Cocoa

Optimization of Certainty Factor Algorithm to Overcome Uncer...

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Electronic and Electrical engineering and Intelligent System (ICE3IS), International Conference on

作者： Rakhmat Dedi Gunawan Imam Ahmad Parjito Harry Anggono Ego Hengki Vernando Firman Jaya Faculty of Engineering and Computer Science Universitas Teknokrat Indonesia Bandarlampung Indonesia Information Technology Education Study Program STKIP PGRI Situbondo Indonesia

ISBN: (纸本)9781665465427

Lampung Province is one of the regions with the highest cocoa production, but many factors can interfere with cocoa production. One of them is the factor of pests and diseases of cocoa that cannot be identified and prevented beforehand. An expert system is a system that adopts expert knowledge or experts in a particular field. However, the diagnosis usually contains uncertainty in the form of answers and statements used. Certainty Factor (CF) algorithm, is one of the algorithms that is able to overcome uncertainty by providing a value for the level of trust from experts and users. However, because the CF value is based on the level of confidence in the symptoms experienced by the user, it creates a problem, namely the consistency of the user's answers. So, it is necessary to make improvements to the CF algorithm, to overcome the inconsistency of user answers through the use of Consistency Ratio (CR). Through the use of CR, inconsistencies are overcome through comparisons between the consistency index and the random index. Based on the results of the accuracy test, the proposed model is able to produce an accuracy rate of 86.67% in the diagnosis of pests and diseases on cocoa plants.

关键词： Electrical engineering Deep learning Uncertainty Production Indexes Expert systems Intelligent systems

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Characterization of a thermostable-organic solvent-tolerant lipase from thermotolerant Rhizopus sp. strain PKC12B2 isolated from palm kernel cake

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Case Studies in Chemical and Environmental engineering 2024年 9卷

作者： Riyadi, Fatimah Azizah Alam, Md Zahangir Salleh, Md Noor Salleh, Hamzah Mohd Hidayatullah, Ibnu Maulana Hara, Hirofumi Department of Chemical Engineering Faculty of Engineering Universitas Indonesia West Java Depok 16424 Indonesia Bioprocess Engineering Study Program Faculty of Engineering Universitas Indonesia West Java Depok 16424 Indonesia Research Center for Biomass Valorization Universitas Indonesia West Java Depok 16424 Indonesia Bioenvironmental Engineering Research Centre (BERC) Department of Biotechnology Engineering Faculty of Engineering International Islamic University Malaysia Kuala Lumpur 53100 Malaysia Department of Biotechnology Graduate School of Agricultural and Life Sciences The University of Tokyo 1-1-1 Yayoi Bunkyo-ku Tokyo 113-8657 Japan

In this work, a new thermostable and organic solvent-tolerant lipase produced from Rhizopus sp. strain PKC12B2 was characterized to evaluate its activity and stability toward pH, temperature, organic solvents, and surfactants. The catalytic efficiency of the produced enzyme was also analyzed through enzyme kinetics study. Produced lipase exhibits remarkable stability at pH 8.0 to pH 10.0 and shows optimum activity at pH 9.0, demonstrating alkali lipase properties. Lipase has an optimum temperature of 45 °C and possesses significant thermal stability in the temperature range between 45 and 55 °C. Exceptional enzyme stability was also demonstrated in the presence of both polar solvents, such as glycerol, isopropanol and acetone, as well as non-polar solvents, such as cyclohexane and heptane up to 30 % (v/v) solvent concentration, where the enzyme retained more than 80 % activity after 1-h pre-incubation. For methanol, ethanol, and n-octanol, the relative activity of lipase was highly stable in the presence of up to 20 % (v/v) solvent concentration. Na⁺, Zn²⁺, and Fe³⁺ ions significantly stimulate the lipase activity at a final concentration of 10 mM. Lipase also demonstrated stimulatory effects towards a broad concentration range (0.2–1 %) of surfactants Tween 80 and Triton X-100. The Linearized Michaelis-Menten model in the form of Lineweaver-Burk (R² = 0.995) and Hanes-Woolf (R² = 0.996) models was found to successfully display the enzyme kinetics, in which the former generated Km and V_max of 1.965 mM and 4.51 mM/min respectively, while the latter generated Km and V_max of 1.295 mM and 3.389 mM/min, respectively. These new enzyme attributes demonstrate its great potential in biotechnology applications, such as for detergent formulations, biodiesel production and organic synthesis industries. © 2024 The Authors

关键词： Characterization Lipase Rhizopus sp. strain PKC12B2 Thermostable-organic solvent-tolerant enzyme

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