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Blockchain and API Development to Improve Relational Database Integrity and System Interoperability

Procedia computer Science 2023年 216卷 151-160页

作者： Andhika Naafi Ramadhan Kharisma Naufal Pane Kun Rifki Wardhana Suharjito Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Industrial Engineering Department BINUS Graduate Program – Master of Industrial Engineering Bina Nusantara University Jakarta 11480 Indonesia

Technological developments have resulted in a trend of cryptocurrencies that use a technology called blockchain to create and record all transactions made into a digital ledger. Along with the emergence of the trend of cryptocurrencies, this development has also resulted in crimes that hit the digital world, such as data leakage and cyber espionage. This threat can be prevented by applying blockchain technology to the database that has been used. Therefore, we need a system that can facilitate the use of blockchain technology and can process data from an existing relational database into a blockchain-based database. The system developed in this study was built with the FastAPI framework that uses the Python programming language and the React framework that uses the Javascript programming language. This system was tested using Katalon and Wireshark software to perform throughput testing and man-in-the-middle attacks. Evaluation of this system is assessed based on the average throughput time and also the results of Wireshark packet capture. The system designed in this research is expected to help overcome interoperability problems when using blockchain and improve relational database integrity. The results of the test show that the system is safe from man-in-the-middle attacks while sending data through API and has a faster throughput time than BigchainDB system by 4.151 seconds.

关键词： Blockchai Cybercrim Database Interoperability

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Using Real-Time Integrated computer Vision and Deep Learning for Advanced Factory Safety 4

Using Real-Time Integrated Computer Vision and Deep Learning...

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4th IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2024

作者： Tsai, Shang-Te Chang, Yu-Cheng Huang, Chao-Yi Yang, Fang-Yuan Chien, Wei Wu, Zong-Rong School Art and Design Sanming University Fujian China National Taipei University of Technology Department of Computer Science and Information Engineering Taipei Taiwan National Yunlin University of Science and Technology Department of Information Management Yunlin Taiwan Lunghwa University of Science and Technology Department of Computer Information and Network Engineering Taoyuan Taiwan National Yunlin University of Science and Technology Department of Technical and Vocational Program in Artificial Intelligence Yunlin Taiwan

ISBN: (纸本)9798350360721

Innovative safety in the workplace is vital as the high safety risks associated with electrical engineering construction can lead to injuries or even fatalities. Using computer vision technology, we experimented with scenarios such as 'normal operations' and 'unexpected incidents' to enhance safety measures. We integrated an Internet of Things system into the setup, enabling the system to quickly detect and alert to unexpected events in real-time, thereby improving workplace safety. © 2024 IEEE.

关键词： computer vision

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Modeling the Self-Capacitance of Individual Plates in a Multi-Conductor System using PEEC 5

Modeling the Self-Capacitance of Individual Plates in a Mult...

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5th IEEE Research and Applications of Photonics in Defense Conference, RAPID 2022

作者： Wolf, Henry Striker, Ryan Cleveland, Jerika Gieser, Anna Braaten, Benjamin D. Enochson, Avery Ewert, Daniel Mitra, Dipankar Allen, Jeffery Allen, Monica Vitola, Sabina North Dakota State University Department of Electrical and Computer Engineering Fargo United States Program of Minnesota State University Department of Integrated Engineering at Iron Range Engineering MankatoMN United States University of Wisconsin - la Crosse Department of Computer Science and Computer Engineering La CrosseWI United States Eglin Air Force Base Eglin Afb Air Force Research Laboratory Munitions Directorate FL32542 United States Minnesota State University Bell Program Department of Integrated Engineering MankatoMN United States

ISBN: (数字)9781665402231

ISBN: (纸本)9781665402231

In many areas of electronics design, it is necessary to understand the different aspects of capacitance associated with various conducting surfaces in a particular layout. This is because as operating frequencies increase and dimensions decrease, capacitive coupling can become the dominant means in which noise is induced in a design. Therefore, having the ability to extract an equivalent circuit for modeling capacitive coupling and understanding the coupling can be very important, and challenging. This paper presents insight on capacitive coupling by evaluating the capacitance between two parallel plates, with particular attention paid to the self-capacitance (with a reference at infinity) of each of the individual plates. More specifically, the Partial Element Equivalent Circuit (PEEC) method is used to compute the self-capacitance of each individual plate in the parallel plate capacitor problem, and results are verified by comparison to values from James C. Maxwell's original works and the electrostatic solver ANSYS Maxwell 3D. Overall, it is shown how the self-capacitance of each individual plate changes as a function of distance between the parallel plates. © 2022 IEEE.

关键词： Capacitance

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Banana Plants Soil Health Analysis Using Wireless Sensor Networks 2021

Banana Plants Soil Health Analysis Using Wireless Sensor Net...

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2021 10th International Conference on Computing and Pattern Recognition

作者： Diane Michael L. Vilvestre Joshua James P. Lawas Carl Rafael A. Becerra Hanna Leah Pacure Angelia Electronic Engineering Program University of Mindanao Philippines Computer Engineering Program University of Mindanao Philippines

ISBN: (纸本)9781450390439

The tedious process of acquiring sample from a hundred-hectare wide or more of banana plantation to determine soil moisture, soil pH, Nitrogen, Phosphorus and Potassium nutrient levels through a laboratory test is time-consuming and inefficient. In this technical research, the objective is to automate the soil health analysis by introducing RGB sensors, motors, submersible pumps, microcontrollers and algorithm that measures Nitrogen, Phosphorus, potassium, pH level and moisture content. The data gathered is transmitted wirelessly. The interpretation of the assessed data is displayed through an android application. Using confusion matrix, the accuracy is found to be 93.33% for both Nitrogen and Phosphorus, and 90% for potassium. Also, the results arrived at 0.97706 and 0.098957 as the coefficient of determination for soil moisture and pH respectively. Considering the high accuracy rate, it is therefore concluded that the system is effective in soil health analysis and minimizes human interaction.

关键词： Soil analysis Agricultural automation Wireless sensor network

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Application of text mining techniques to identify actual wrong-way driving (WWD) crashes in police reports

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International Journal of Transportation Science and Technology 2023年第4期12卷 1038-1051页

作者： Parisa Hosseini Seyedalireza Khoshsirat Mohammad Jalayer Subasish Das Huaguo Zhou Department of Civil and Environmental Engineering Center for Research and Education in Advanced Transportation Engineering Systems(CREATEs)Rowan University201 Mullica Hill RoadGlassboroNJ 08028USA Department of Computer&Information Sciences University of Delaware18 Amstel AveNewarkDE 19716USA Civil Engineering Program Ingram School of EngineeringTexas State UniversityRFM 5202601 University DriveSan MarcosTX 78666USA Harbert Center Department of Civil and Environmental EngineeringAuburn UniversityAuburnAL 36849USA

Wrong-way driving(WWD)has been a long-lasting issue for transportation agencies and law enforcement,since it causes pivotal threats to road *** being rare,crashes occurring due to WWD are more severe than other types of *** order to analyze WWD crashes,there is a need to obtain WWD incidents or crash ***,it is time-consuming to identify actual WWD crashes from potential WWD crashes in large crash *** often involves large man-hours to review hardcopy of crash narratives in the police ***,it may cause an overestimation or underestimation of WWD crash *** fill this gap,the present study,as the first-of-its-kind,aims at identifying actual WWD crashes from potential WWD crashes in police reports by using machine learning ***,Bidirectional Encoder Representations from Transformers(BERT)models have shown promising results in natural language *** this study,we implemented the BERT model as well as five conventional classification algorithms,including Naïve Bayes(NB),Support Vector Machine(SVM),Decision Tree(DT),Random Forest(RF),and Single Layer Perceptron(SLP)to classify crash report narratives as actual WWD and non-WWD ***-validation and different performance metrics were used to evaluate the performance of each classification *** indicated that the BERT model outperformed in identifying actual WWD crashes in comparison with other algorithms with an accuracy of 81.59%.The BERT classification algorithm can be implemented to reduce the time needed to identify actual WWD crashes from crash report narratives.

关键词： Wrong-Way Driving Crashes Crash Report Narratives Text Mining Text Classification

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Neuronal Modeling Tool Using DynaSim 5

Neuronal Modeling Tool Using DynaSim

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5th IEEE Research and Applications of Photonics in Defense Conference, RAPID 2022

作者： Gieser, Anna Vitola, Sabina Enochson, Avery Wolf, Henry Striker, Ryan Cleveland, Jerika Braaten, Benjamin D. Ewert, Daniel Mitra, Dipankar Allen, Jeffery Allen, Monica North Dakota State University Department of Electrical and Computer Engineering Fargo United States Minnesota State University Bell Program Department of Integrated Engineering MankatoMN United States Program of Minnesota State University Department of Integrated Engineering at Iron Range Engineering MankatoMN United States University of Wisconsin - la Crosse Department of Computer Science and Computer Engineering La CrosseWI United States Munitions Directorate Air Force Research Laboratory Eglin Air Force Base FL32542 United States

ISBN: (数字)9781665402231

ISBN: (纸本)9781665402231

Human cognitive processes remain an area of strong interest and ongoing research. One tool to gain greater insight into this process is neuronal modeling. The following features are desirable in a neuronal modeling tool: a library of known parameters for different neurons and species, the ability to select the neuron and species of interest, the ability to quickly simulate neuronal behavior, and the ability to calculate metabolic requirements and efficiencies for various neuronal activity. Many of these can be found in software today, but not all elements can be found in one entity. The goal of this work is to develop a neuronal modeling tool that incorporates these features. © 2022 IEEE.

关键词： Neurons

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Bioinformatics exploration into potential mechanisms of Calamus draco for colorectal cancer treatment

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Procedia computer Science 2024年 245卷 127-136页

作者： Christian Kenneth Rudi Nirwantono Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Biotechnology Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

As one of the cancer types with the highest incidence rates, colorectal cancer (CRC) would benefit from treatments with fewer side effects and reduced treatment-resistant potential. One of the options is to harness the anti-CRC potential of natural products. Previous studies have shown that Calamus draco exudate, dragon's blood, has anticancer activity in liver cancer and acute myeloid leukemia, but its bioactivity has not been studied in CRC. Here we conduct a bioinformatics study based on network pharmacology to explore the anti-CRC potential and mechanism of C. draco -derived compounds. The bioinformatics pipeline is composed of compound and target collection, biological network evaluation, and enrichment analysis. We found that there are 43 bioactive compounds from C. draco targeting 91 CRC-related targets, of which most compounds target MEN1, PTGS2, and IDH1. Further analyses show that the targets of C. draco are involved in the cellular response to hypoxia. By inhibiting those targets, C. draco bioactive compounds can potentially hinder angiogenesis and increase treatment response efficacy.

关键词： bioinformatics network pharmacology Calamus draco colorectal cancer drug discovery

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Gallium Oxide Schottky Barrier Diodes for Alpha Spectroscopy

Gallium Oxide Schottky Barrier Diodes for Alpha Spectroscopy

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2022 Transactions of the American Nuclear Society Annual Meeting, ANS 2022

作者： Heckert, Christopher Taylor, Neil R. Dhara, Sushovan Rajan, Siddharth Cao, Lei R. Blue, Thomas E. Department of Mechanical and Aerospace Engineering Nuclear Engineering Program The Ohio State University ColumbusOH43210 United States Department of Electrical and Computer Engineering The Ohio State University ColumbusOH43210 United States

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Involution symmetry quantification using recurrences

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Physical Review E 2024年第2期110卷 024203-024203页

作者： Gabriel Marghoti Thiago de Lima Prado Sergio Roberto Lopes Yoshito Hirata Physics Department Degree Program in Computer Science Interdisciplinary Center for Science Technology and Innovation CICTI Departament of Physics - Telegraphenberg 14473 Potsdam Germany Institute of Systems and Information Engineering

Symmetries are ubiquitous in science, aiding theoretical comprehension by discerning patterns in mathematical models and natural phenomena. This work introduces a method for assessing the extent of symmetry within a time series. We explore both microscopic and macroscopic features extracted from a recurrence plot. By analyzing the statistics of small recurrence matrices, our approach delves into microscale dynamics, facilitating the identification of symmetric time series segments through diagonal macroscale structures on a recurrence plot. We validate our approach by successfully quantifying involution symmetries for three-dimensional dynamical models, specifically, order-2 rotational symmetry in the Lorenz '63 model, and inversion symmetry in the Chua circuit. Our quantifier also detects symmetry breaking in the modified Lorenz model for El Niño phenomenon. The method can be applied in a versatile manner, not only to three-dimensional trajectories but also to univariate time series. Symmetry quantification in time series is promising for enhancing dynamical system modeling and profiling.

关键词： Chaos Dynamical systems Time series analysis

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Enhancing Plant Leaf Disease Identification with a CNN and DenseNet Hybrid Model 5

Enhancing Plant Leaf Disease Identification with a CNN and D...

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5th International Conference on Information Management and Machine Intelligence, ICIMMI 2023

作者： Dari, Sukhvinder Singh Tiwaskar, Shweta Raja Kumar, Jambi Ratna Langote, Vaishali Mohadikar, Gopal Madhukar Ashtagi, Rashmi Pune India VIIT Maharashtra Pune India Department of Computer Engineering Genba Sopanrao Moze College of Engineering Balewadi Maharashtra Pune India Program - CSE Department of Polytechnic Dr. Vishwanath Karad World Peace University Maharashtra Pune India Tolani Maritime Institute Induri Maharashtra Talegaon Dabhade India Department of Computer Engineering Vishwakarma Institute of Technology Bibwewadi Maharashtra Pune411037 India

ISBN: (纸本)9798400709418

The identification of plant leaf diseases is a essential section of agriculture, serving as a critical measure in mitigating the reduction in crop productivity. This study presents a novel methodology for improving the precision of plant leaf disease detection. This is achieved by employing a hybrid architecture that combines Convolutional Neural Networks (CNNs) and DenseNet models. The main aim of our study is to enhance the accuracy and effectiveness of disease detection by leveraging the combined capabilities of Convolutional Neural Networks (CNNs) and DenseNet. These two deep learning architectures are recognized for their proficiency in extracting and reusing features. In this study, we conduct a comparative analysis of our proposed hybrid model by evaluating its performance against traditional Convolutional Neural Networks (CNNs) as well as other advanced models such as Recurrent Neural Networks (RNNs) and Capsule Networks (CapsNets). The findings of our study demonstrate that the hybrid model, combining CNN and DenseNet architectures, showed an accuracy rate of 98.79%. This performance surpassed that of other models under comparison. The hybrid model demonstrated higher levels of precision, recall, F1 score, and AUC-ROC values, shows its effectiveness in the identification of plant leaf diseases. This study highlights the potential of hybrid deep learning models in the domain of precision agriculture and emphasizes the importance of integrating diverse architectural paradigms for accurate disease identification. The research conducted in this study makes a significant contribution to the progress of agricultural technology by providing a dependable mechanism for the early detection of diseases. As a result, this research contributes to the enhancement of crop health and the promotion of food security. © 2023 ACM.

关键词： Convolutional neural networks

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