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Novel Intelligent and Leading Emerging sciences Conference (NILES)

作者： Hesham Mamdouh Mohamed Tarek Abdelrahman Radwan Adel Saeed Ahmed Abdeen Monier Ashraf Khaled M. Fouad Ibrahim Abdelbaky School of Engineering and Applied Science (EAS) Nile University Giza Egypt CS program School of Information Technology and Computer Science (ITCS) Nile University Giza Egypt Faculty of Computer Science and Engineering New Mansoura University Artificial Intelligence Department Faculty of Computers and Artificial Intelligence Benha University Banha Egypt

ISBN: (数字)9798350378511

ISBN: (纸本)9798350378528

The increasing sophistication of cyber attacks necessitates effective intrusion detection systems. We propose a novel intrusion detection method integrating deep learning with big data management using Apache Spark. Leveraging the comprehensive CSE-CIC-IDS2018 dataset, we apply extensive data preprocessing, including handling missing and unreliable values, duplicates, and redundant columns. In addition, implementation of a Random Forest based feature importance approach is derived to prioritize the most impactful Features. Furthermore, stratified k-fold cross-validation is used for a model selection process on a class-imbalanced dataset. Our weighted Random Forest classifier achieves a remarkable weighted average F1-score of 0.999 and a test inference time of 0.673 seconds using only the top 34 features, outperforming previous studies without sampling techniques. The proposed architecture offers a scalable and accurate solution for intrusion detection in cloud architectures, demonstrating the effectiveness of combining deep learning and big data technologies for cybersecurity.

关键词： Deep learning Cloud computing Data preprocessing Network intrusion detection Cluster computing Forestry Big Data Random forests Cyberattack Testing

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Comparative analysis of deep learning models for detecting face mask 7

Comparative analysis of deep learning models for detecting f...

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7th International Conference on computer science and Computational Intelligence, CSCI 2022

作者： Ramadhan, M. Vickya Muchtar, Kahlil Nurdin, Yudha Oktiana, Maulisa Fitria, Maya Maulina, Novi Elwirehardja, Gregorius Natanael Pardamean, Bens Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh23111 Indonesia Universitas Syiah Kuala Banda Aceh23111 Indonesia Microbiology Department School of Medicine Universitas Syiah Kuala Banda Aceh23111 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia

The spread of Corona Virus Disease 19 (COVID-19) in Indonesia is still relatively high and has not shown a significant decrease. One of the main reasons is due to the lack of supervision on the implementation of health protocols such as wearing masks in daily activities. Recently, state-of-The-Art algorithms were introduced to automate face mask detection. To be more specific, the researchers developed various kinds of architectures for the detection of masks based on computer vision methods. This paper aims to evaluate well-known architectures, namely the ResNet50, VGG11, InceptionV3, EfficientNetB4, and YOLO (You Only Look Once) to recommend the best approach in this specific field. By using the MaskedFace-Net dataset, the experimental results showed that the EfficientNetB4 architecture has better accuracy at 95.77% compared to the YOLOv4 architecture of 93.40%, InceptionV3 of 87.30%, YOLOv3 of 86.35%, ResNet50 of 84.41%, VGG11 of 84.38%, and YOLOv2 of 78.75%, respectively. It should be noted that particularly for YOLO, the model was trained using a collection of MaskedFace-Net images that had been pre-processed and labelled for the task. The model was initially able to train faster with pre-Trained weights from the COCO dataset thanks to transfer learning, resulting in a robust set of features expected for face mask detection and classification. © 2022 Elsevier B.V.. All rights reserved.

关键词： Face recognition

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Convolutional Neural Network Approach for Sentiment Analysis of GoFood Services

Convolutional Neural Network Approach for Sentiment Analysis...

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computer sciences, engineering, and Technology Innovation (ICoCSETI), International Conference on

作者： Zaenal Abidin Adzqi Aulia Ryan Randy Suryono Permata Akmal Junaidi Wamiliana Wamiliana Doctoral Program of Mathematics and Natural Sciences Universitas Lampung Bandarlampung Indonesia Faculty of Engineering and Computer Science Universitas Teknokrat Indonesia Bandarlampung Indonesia Department of Computer Science Universitas Lampung Bandarlampung Indonesia Department of Mathematics Universitas Lampung Bandarlampung Indonesia

ISBN: (数字)9798331508616

ISBN: (纸本)9798331508623

In the age of swift digital advancement, social media has emerged as a vital and readily available information source for users. Individuals utilize social media to communicate, articulate thoughts, and disseminate their viewpoints, both textually and visually. Twitter is a social media platform that people extensively utilize to disseminate both favorable and unfavorable information regarding GoFood services. This study intends to apply the Convolutional Neural Network (CNN) algorithm within the GoFood service on Twitter. This research also seeks to assess the accuracy of the CNN algorithm in relation to GoFood services. Employing deep learning techniques, particularly the convolutional neural network algorithm, to analyze Twitter user sentiment can enhance the efficiency, accuracy, and simplicity of classifying answers to GoFood services. The findings indicated that the Convolutional Neural Network technique was effectively employed to classify positive, negative, and neutral feelings. The optimal accuracy rate was attained through preprocessing with Stemming, yielding 51% accuracy for multiclass classification and 72% for binary class classification. The accuracy percentage for classification without stemming was 49% for multiclass classification and 70% for binary classification. This research holds possible consequences for Gojek firms and can serve as evaluative and assessment material for GoFood services, enabling them to implement necessary corrective measures.

关键词： Sentiment analysis Accuracy Social networking (online) Convolution Blogs Text categorization Feature extraction Classification algorithms Convolutional neural networks Testing

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Extraction of c- F os Protein Regions from Mouse Brain Slice Images using Multi-channelization, 1 x 1 Convolution and U -Net 13

Extraction of c- F os Protein Regions from Mouse Brain Slice...

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Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Wang, Ziyue Okamura, Hibiki Luo, Shijie Hayashi, Yu Takizawa, Hotaka Master's Program in Computer Science University of Tsukuba Tsukuba Japan International Institute for Integrative Sleep Medicine University of Tsukuba Ph.D. Program in Humanics Tsukuba Japan International Institute for Integrative Sleep Medicine University of Tsukuba Tsukuba Japan Institute of Systems and Information Engineering Academic Computing and Communications Center University of Tsukuba Tsukuba Japan

ISBN: (纸本)9798350373332

This study proposed a novel extraction method of c-Fos protein regions in DAB(3,3'-diaminobenzidine)-stained mouse brain slice images using the U-Net model combined with the multi-channelization and 1×1 convolution techniques. Our U-Net m odel, called 1× 1 conv U-Net, was applied to three DAB- strainedmouse slice images whose resolutions were 1000x1000 pixels. The experimental results demonstrated that the 1×1 conv U-Net outperformed the conventional U-Net. © 2024 IEEE.

关键词： Mammals

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Multi-Temporal Relationship Inference in Urban Areas

arXiv

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arXiv 2023年

作者： Li, Shuangli Zhou, Jingbo Liu, Ji Xu, Tong Chen, Enhong Xiong, Hui School of Computer Science and Technology University of Science and Technology of China Baidu Research China Business Intelligence Lab Baidu Research China Big Data Lab Baidu Research China School of Computer Science and Technology University of Science and Technology of China State Key Laboratory of Cognitive Intelligence China Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China State Key Laboratory of Cognitive Intelligence China The Department of Computer Science and Engineering The Hong Kong University of Science and Technology China

Finding multiple temporal relationships among locations can benefit a bunch of urban applications, such as dynamic offline advertising and smart public transport planning. While some efforts have been made on finding static relationships among locations, little attention is focused on studying time-aware location relationships. Indeed, abundant location-based human activities are time-varying and the availability of these data enables a new paradigm for understanding the dynamic relationships in a period among connective locations. To this end, we propose to study a new problem, namely multi-Temporal relationship inference among locations (Trial for short), where the major challenge is how to integrate dynamic and geographical influence under the relationship sparsity constraint. Specifically, we propose a solution to Trial with a graph learning scheme, which includes a spatially evolving graph neural network (SEENet) with two collaborative components: spatially evolving graph convolution module (SEConv) and spatially evolving self-supervised learning strategy (SE-SSL). SEConv performs the intra-time aggregation and inter-time propagation to capture the multifaceted spatially evolving contexts from the view of location message passing. In addition, SE-SSL designs time-aware self-supervised learning tasks in a global-local manner with additional evolving constraint to enhance the location representation © 2023, CC BY.

关键词： Location

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Electro-Polymerization Process with Double-Sided Electrodes for Supercapacitors 5

Electro-Polymerization Process with Double-Sided Electrodes ...

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5th IEEE International Flexible Electronics Technology Conference, IFETC 2023

作者： Koripally, Nandu Yao, Lulu Eedugurala, Naresh Azoulay, Jason D. Ng, Tse Nga University of California San Diego Department of Electrical and Computer Engineering La Jolla CA United States University of California San Diego Materials Science Engineering Program La Jolla CA United States Georgia Institute of Technology Chemisty Department AtlantaGA United States

ISBN: (纸本)9798350332094

Supercapacitors are known for longer cycle life and faster charging rate compared to batteries. However, the energy density of supercapacitors requires improvement to expand their application space. To raise the energy density of redox supercapacitors, this work demonstrates electro-polymerization on both sides of electrodes to increase material loading, reduce overall device mass, and consequently increase the capacitance, power, and energy density of supercapacitors. We achieved an energy density of 1823 mWh kg-1 and a power density of 16.5 W kg-1 with a wide potential window of 3 V. © 2023 IEEE.

关键词： Supercapacitor

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White rice stem borer pest detection system using image-based convolution neural network

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Procedia computer science 2024年 245卷 518-527页

作者： Akhmad Saufi Suharjito Computer Science Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia 11480 Industrial Engineering Department BINUS Graduate Program – Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia 11480

Preventing agricultural resource loss caused by pests remains a crucial issue. While technological advancements are being achieved, the current agricultural management methods and equipment have yet to meet the required level for precise pest control, a huge portion of the pest population analysis process is still conducted manually. As a solution to this issue, the development of a White Rice Stem Borer pest detection system has been conducted by applying Convolutional Neural Network (CNN) technology to calculate the pest population count at the research location. This system has been specifically designed to detect the White Rice Stem Borer using available traps. The method involves training data from a direct dataset obtained from the field, categorized into two positive and negative classes of the White Stem Borer pests. Six models have been trained from this dataset, utilizing two different architectures. Out of the six trained models, four showed potential overfitting, one exhibited underfitting, and one model demonstrated optimal results. The highest accuracy in image detection achieved by the most optimal CNN model was 97.35%, with a training accuracy of 98.54%. This best-performing model utilized an architecture with three Convolution layers, 50 Epochs, and an automatic data split with an 80:20 training-validation data ratio. From the research findings, it is concluded that this study can assist in automatically analyzing the quantity of White Stem Borer pests in a specific area without directly counting the number of pests from existing traps. However, the study still encounters a limitation—the detection process still requires substantial server resources and cannot be directly processed on the Raspberry PI device installed in the trap. Consequently, the detection relies on transmitting image data from the field device to the server before the detection process can occur.

关键词： White Rice Stem Borer Stem Borer White Stem Borer Detection Pest Detection Convolutional Neural Network (CNN)

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From Pictures to Prompts: Analyzing and Reconstructing AI-Generated Images with BLIP2 and CLIP 24

From Pictures to Prompts: Analyzing and Reconstructing AI-Ge...

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8th International Conference on Natural Language Processing and Information Retrieval, NLPIR 2024

作者： Kuo, Yi-Hsuan Chang, Hsien-Tsung Department of Computer Science and Information Engineering Chang Gung University Taoyuan China Bachelor Program in Artificial Intelligence Chang Gung University Taoyuan China

ISBN: (纸本)9798400717383

This study presents a novel approach to analyzing and reconstructing AI-generated images using BLIP2 and CLIP models, focusing on a dataset of 268,000 Midjourney-generated images and prompts. We introduce a multi-level feature classification system encompassing 11 categories and 1,100 features, and employ BLIP2 for descriptive text generation and CLIP for feature similarity analysis. Our methodology involves image pre-processing, custom feature extraction, and image reconstruction under various conditions. The effectiveness of our approach is demonstrated through similarity scores between original and reconstructed images, ranging from 68.15 to 71.25 across different feature extraction conditions. This research contributes to the fields of natural language processing and information retrieval by offering insights into AI-generated image analysis, proposing new methods for feature extraction and reconstruction, and suggesting avenues for improving image generation models and multi-modal understanding. © 2024 Copyright held by the owner/author(s).

关键词： Sentiment analysis

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Development of Charge Integrator Circuits with a Microcontroller for Cylindrical Capacitive Sensors in Paddy Moisture Balance Detectors

Development of Charge Integrator Circuits with a Microcontro...

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International Electrical engineering Congress (iEECON)

作者： Sudarat Tokampang Nattapong Apirattanon Noppadon Sisuk Anucha Kaewpoonsuk Deparment of Physics Faculty of Science Naresuan University Phitsanulok Thailand Program of Computer Engineering Faculty of Industrial Technology Phibunsongkhram Rajabhat University Phitsanulok Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

This article presents a method for indirectly measuring the moisture content of paddy using a cylindrical capacitive sensor combined with Charge Integrator Circuits. The moisture measurement device operates based on the principle of the dielectric constant of paddy, which changes according to the moisture content within them. The system consists of a moisture detection section utilizing a cylindrical capacitor, a signal generator operating at a frequency of 500 Hz with a microcontroller, a signal conditioning circuit converting the signal to voltage, and a processing unit. Testing was conducted with paddy at five different moisture levels, demonstrating the ability to accurately determine the moisture content of the rice. When compared to the commercially available PM-450TH moisture meter, the results showed a linear response across the measurement range, with an R 2 value of 0.9824 and an average error of 2.6%. This technology has the potential for further development and application to other types of seeds in the future.

关键词： Accuracy Moisture measurement Microcontrollers Circuits Measurement uncertainty Moisture Dielectric measurement Capacitive sensors Standards Charge measurement

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Digital Technology in Health Education Trend Mapping through Bibliometrics Analysis 4

Digital Technology in Health Education Trend Mapping through...

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4th International Conference on Informatics, Multimedia, Cyber and Information System, ICIMCIS 2022

作者： Maulana, Fairuz Iqbal Lestari, Dian Widartha, Vandha Pradwiyasma Haidar, Nirwana Dirgantara, Wahyu School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Airlangga University Faculty of Medicine Surabaya60132 Indonesia Telkom University Information System of Industrial Engineering Faculty Bandung Indonesia University of Madura Informatics Engineering Study Program Pamekasan Indonesia University of Merdeka Malang Department of Electrical Engineering Malang Indonesia

ISBN: (纸本)9781665473279

This study examines the mapping of research data on digital technology in the field of health education using bibliometric analysis method. Data was collected by identifying keywords in the Scopus database and sorting them out to sort the studies from the last 10 years (2012-2021). Through this step, a total of 1482 documents were obtained (articles, journals, proceedings, books, and others). The data is then processed using the VOSViewer instrument to obtain a visualization of the mapping analysis. This study also analyzes the network types of authors and co-authors through the VOSViewer instrument. The result of this study indicates that the most document types published within ten years are Articles (66.5%), Review (22.9%), Conference Paper (4.2%), and others. The most studied subjects are Medicine (55.7%), Nursing (9.0%), Health Professions (8.2%), Social sciences (7.8%), engineering (3.9%), computer science (2.6%), Environmental science (2.6%), Biochemistry-Genetics and Molecular Biology (2.2%), Psychology (1.6%), and Dentistry (1.3%). This study offers a written communication process and the nature and direction of developing descriptive means of counting and analyzing the various phases of communication as well as recognizing the authorship and direction of its symptoms in documents on the subject of digital technology in the health sector. © 2022 IEEE.

关键词： Mapping

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