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International Conference on informatics and Computing (ICIC)

作者： Nur Ghaniaviyanto Ramadhan Gita Fadila Fitriana Data Science School of Computing Telkom University Bandung Indonesia Software Engineering Faculty of Informatics Telkom University Purwokerto Purwokerto Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

In the context of movie reviews, sentiment analysis plays a crucial role as these reviews often capture the emotions and opinions of viewers regarding the film. With the growing number of reviews across platforms like social media and movie review sites, it becomes feasible to automatically classify them into positive or negative sentiments. However, manually analyzing a dataset of 50,000 reviews is impractical. Therefore, this study focuses on conducting sentiment analysis on the IMDB movie review dataset, consisting of 50,000 entries, using an ensemble voting model. The machine learning models applied in this ensemble include Random Forest (RF) and Adaptive Boosting (AdaBoost). Two test scenarios were employed: the first compared the soft and hard voting approaches, while the second examined the impact of different training-test data splits. The findings indicate that the soft voting ensemble (SVE) model with an 80%-20% training-testing split achieved an accuracy of 88%, an F1-score of 88%, and an AUC of 0.94, which is 4% higher than the hard voting model.

关键词： Radio frequency Sentiment analysis Analytical models Adaptation models Accuracy Reviews Social networking (online) Motion pictures Ensemble learning Random forests

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Unprecedented Smart Algorithm for Uninterrupted SDN Services During DDoS Attack

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Computers, Materials & Continua 2022年第1期70卷 875-894页

作者： Muhammad Reazul Haque Saw Chin Tan Zulfadzli Yusoff Kashif Nisar Rizaludin Kaspin Iram Haider Sana Nisar J.P.C.Rodrigues Bhawani Shankar Chowdhry Muhammad AslamUqaili Satya Prasad Majumder Danda B.Rawat Richard Etengu Rajkumar Buyya Faculty of Computing&Informatics Multimedia UniversityPersiaranMultimediaCyberjaya63100SelangorMalaysia Faculty of Engineering Multimedia UniversityPersiaran MultimediaCyberjaya63100SelangorMalaysia Faculty of Computing and Informatics University Malaysia SabahJalan UMSKota Kinabalu Sabah88400Malaysia Telekom Malaysia Research&Development TM Innovation Centre63000CyberjayaSelangorMalaysia Federal University of Piauí(UFPI) TeresinaPIBrazil Instituto de Telecomunica珲es 6201-001Covilh?Portugal National Center of Robotics and Automation-Condition Monitoring Systems Lab MUETJamshoroPakistan Department of Electrical and Electronic Engineering BUETDhaka1205Bangladesh Department of Electrical Engineering and Computer Science Data Science and Cybersecurity CenterHoward UniversityWashingtonDCUSA Cloud Computing and Distributed Systems(CLOUDS)Laboratory School of Computing and Information SystemsThe University of MelbourneMelbourneVIC 3053Australia

In the design and planning of next-generation Internet of Things(IoT),telecommunication,and satellite communication systems,controller placement is crucial in software-defined networking(SDN).The programmability of the SDN controller is sophisticated for the centralized control system of the entire ***,it creates a significant loophole for the manifestation of a distributed denial of service(DDoS)attack ***,recently a Distributed Reflected Denial of Service(DRDoS)attack,an unusual DDoS attack,has been ***,minimal deliberation has given to this forthcoming single point of SDN infrastructure failure ***,recently the high frequencies of DDoS attacks have increased *** this paper,a smart algorithm for planning SDN smart backup controllers under DDoS attack scenarios has *** proposed smart algorithm can recommend single or multiple smart backup controllers in the event of DDoS *** obtained simulated results demonstrate that the validation of the proposed algorithm and the performance analysis achieved 99.99%accuracy in placing the smart backup controller under DDoS attacks within 0.125 to 46508.7 s in SDN.

关键词： SDN smart algorithm RTZLK-DAASCP DDoS attack DRDoS

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Segmentation of Brain Metastases in MRI: A Two-Stage Deep Learning Approach with Modality Impact Study

arXiv

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arXiv 2024年

作者： Sadegheih, Yousef Merhof, Dorit Faculty of Informatics and Data Science University of Regensburg Regensburg93053 Germany Fraunhofer Institute for Digital Medicine MEVIS Bremen28359 Germany

Brain metastasis segmentation poses a significant challenge in medical imaging due to the complex presentation and variability in size and location of metastases. In this study, we first investigate the impact of different imaging modalities on segmentation performance using a 3D U-Net. Through a comprehensive analysis, we determine that combining all available modalities does not necessarily enhance performance. Instead, the combination of T1-weighted with contrast enhancement (T1c), T1-weighted (T1), and FLAIR modalities yields superior results. Building on these findings, we propose a two-stage detection and segmentation model specifically designed to accurately segment brain metastases. Our approach demonstrates that leveraging three key modalities (T1c, T1, and FLAIR) achieves significantly higher accuracy compared to single-pass deep learning models. This targeted combination allows for precise segmentation, capturing even small metastases that other models often miss. Our model sets a new benchmark in brain metastasis segmentation, highlighting the importance of strategic modality selection and multi-stage processing in medical imaging. Our implementation is freely accessible to the research community on GitHub. © 2024, CC BY.

关键词： Medical imaging

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Digital Avatar Sub-Metaverse Modeling Using Terrestrial Photogrammetry Techniques

Digital Avatar Sub-Metaverse Modeling Using Terrestrial Phot...

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International Conference on Computer, Control, informatics and Its Applications (IC3INA)

作者： Abdurrakhman Prasetyadi Muhammad Yudhi Rezaldi Cahyo Trianggoro Arafat Febriandirza Ridwan Suhud Aang Gunawan Haidaruddin Muhammad Ramdhan Research Center for Data and Information Science National Research and Innovation Agency Bandung Indonesia Faculty of Informatics Telkom University Bandung Indonesia

In this study, we explore the metaverse, a virtual realm blending the digital and real worlds. The metaverse promises immersive experiences, and we focus on creating lifelike avatars and environments within a scientific hub called the science and Technology Region (KST). We use a method called terrestrial photogrammetry to make 3D avatars, focusing on researchers at KST. The results show that Agisoft Metashape makes highly precise 3D models with symmetrical details, while the KIRI Engine is faster in processing. These findings suggest that we can make better avatars and environments in the metaverse, but the choice between precision and speed depends on the specific application.

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An adaptive web-based framework for mobile telemonitoring systems

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2017年第1期11卷 40-52页

作者： Setyono, A. Haryanto, H. Astuti, E.Z. Department of Informatics Engineering majoring in computer system and data communication Faculty of Computer Science Dian Nuswantoro University Semarang50131 Indonesia Dian Nuswantoro University Semarang India Department of Informatics Engineering majoring in intelligent system Faculty of Computer Science Dian Nuswantoro University Semarang50131 Indonesia

Mobile long-distance web-based monitoring systems will become increasingly common in the near future. This paper focuses on the development of a healthcare telemonitoring system that uses an adaptive web-based framework that monitors several parameters simultaneously. The proposed system simplifies patient and physician access to the healthcare system. It combines many healthcare service functions for a long distance monitoring system. The system immediately sends blood pressure, blood sugar, respiration, heartbeat, pulse, body temperature and transmits them to the clinic's server. All of these parameters can be inputted by patients themselves while the patients are far from the clinical environment and forwarded to the physician for further diagnosis and medical treatment. The advantages of the proposed system are simple patient access through the internet and simple connectivity to a physician from remote areas. A smartphone with multimedia features and an internet connection in a mobile environment can be used to perform mobile computing anywhere and anytime. It keeps real-time recording and monitoring of physiological parameters of patients at a low cost and at home. The physiological parameters of the patient are enhanced by multimedia data. By using the good quality of the multimedia data, the physician can be more precise when providing a diagnosis or medical treatment to the patient. The proposed framework can be used to develop a practical web-based mobile telemonitoring system in a mobile environment.

关键词： Blood pressure

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Longest common subsequence in sublinear space

arXiv

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arXiv 2020年

作者： Kiyomi, Masashi Horiyama, Takashi Otachi, Yota School of Data Science Yokohama City University Japan Faculty of Information Science and Technology Hokkaido University Japan Department of Mathematical Informatics Nagoya University Japan

We present the first o(n)-space polynomial-time algorithm for computing the length of a longest common subsequence. Given two strings of length n, the algorithm runs in O(n3) time with O (n2log √log1.5nn) bits of spa... 详细信息

关键词： Polynomial approximation

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YOLOv8 vs. YOLOv9: Evaluating Object Detection Algorithms for Marine Waste Recognition

YOLOv8 vs. YOLOv9: Evaluating Object Detection Algorithms fo...

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IEEE International Symposium on Consumer Electronics (ISCE)

作者： Achmad Zahir Wajdi Prasetyo Wibowo Shiori Sasaki Department of Informatics and Computer Engineering Politeknik Elektronika Negeri Surabaya Surabaya Indonesia Faculty of Data Science Musashino University Tokyo Japan

ISBN: (数字)9798350365191

ISBN: (纸本)9798350365207

Marine debris, the majority of which is composed of plastic (61 % to 87 %), is a significant environmental issue facing the world. Between 4.8 million and 12.7 million metric tons of plastic are thought to have entered the ocean in 2010 alone, increasing the estimated total amount of particles in the water to at least 5 trillion. Global plastic production, which reached 348 million tons in 2017, is expected to double in the next 20 years. To overcome this challenge, effective waste detection methods are needed, one of which is by utilizing computer vision, specifically the You Only Look Once (YOLO) algorithm. YOLOv9, as the latest version, offers improved object detection performance compared to its predecessor, YOLOv8. This research aims to evaluate the performance and efficiency of YOLOv9 and YOLOv8 in detecting marine waste. Using the Trash Annotations in Context dataset (TACO), We train both models and conduct tests to evaluate their accuracy and efficiency. Experimental results show that YOLOv9 has higher precision at 45% but lower recall at 6%, compared to YOLOv8, which has a precision of 33 % and a recall of 15 %. Overall, YOLOv8 shows better performance with higher mAP50 at 13% and mAP50-95 values at 10%, compared to YOLOv9, which has mAP50 at 4% and mAP50-95 values at 3%.

关键词： YOLO Waste management Measurement Accuracy Oceans Production Plastics Tuning Monitoring Context modeling

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NCC: Neural concept compression for multilingual document recommendation[Formula presented]

NCC: Neural concept compression for multilingual document re...

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作者： Tashu, Tsegaye Misikir Lenz, Marc Horváth, Tomáš ELTE – Eötvös Loránd University Faculty of Informatics Institute of Academia-Industry Cooperation Department of Data Science and Engineering Pázmány Péter sétány 1/C 1117 Budapest Hungary Pavol Jozef Šafárik University in Košice Faculty of Science Institute of Computer Science Jesenná 5 Košice040 01 Slovakia Department of Artificial Intelligence Bernoulli Institute of Mathematics Computer Science and Artificial Intelligence University of Groningen Groningen Netherlands Wollo University Kombolcha Institute of Technology College of Informatics Kombocha Ethiopia

In this work, we propose a novel method for generating inter-lingual document representations using neural network concept compression. The presented approach is intended to improve the quality of content-based multilingual document recommendation and information retrieval systems by creating a language-independent representation. The main idea is to use mappings to align monolingual representation spaces, using concept compression, to create inter-lingual representations. The proposed approach outperforms traditional cross-lingual retrieval and recommendations methods in experiments conducted on JRC-Acquis and EU bookshop multilingual corpora. Our dataset and code are publicly available at https://***/Tsegaye-misikir/NCC. © 2023

关键词： Information retrieval systems

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A Cross-Sampling Method for Hidden Structure Extraction to Improve Imbalanced Multiclass Classification Accuracy

A Cross-Sampling Method for Hidden Structure Extraction to I...

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Vocational Education and Electrical Engineering, International Conference on

作者： Wiyli Yustanti Nur Iriawan Irhamah I Kadek Dwi Nuryana Aries Dwi Indriyanti Department of Informatics Faculty of Engineering Universitas Negeri Surabaya Surabaya Indonesia Department of Statistics Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Surabaya Indonesia

Class prediction problems in classification cases are often faced with irregular data conditions. This research contributes to providing a more structured hidden pattern extraction approach to data sets that already have class labels. The process of taking samples from data collection in this study is called the cross-sampling (CS) technique. The basic idea of this technique is to regroup the data using the appropriate clustering method. This study applied the Mini Batch K-Mean and Spectral algorithms to four public datasets with an imbalanced multiclass distribution. The grouping results are then assigned a label based on the original label reference using the pattern-matching concept on the first principal component through the PCA procedure. The labelling results are then used as external validation for the actual data labels to represent all classes. The validation results produce a confusion matrix used as the basis for the cross-sampling method. The dataset before cross-sampling and the results of cross-sampling were compared to the performance of the classification prediction accuracy using the F1-Score and Area Under Curve (AUC) measurements. The statistical hypothesis testing results show a significant difference in performance before and after the cross-sampling procedure. This difference is demonstrated by the accuracy of all the classification algorithms used, which increased significantly from the average performance value of 82.09% to 96.7%.

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Intelligent Machine Learning with Metaheuristics Based Sentiment Analysis and Classification

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Computer Systems science & Engineering 2023年第1期44卷 235-247页

作者： R.Bhaskaran S.Saravanan M.Kavitha C.Jeyalakshmi Seifedine Kadry Hafiz Tayyab Rauf Reem Alkhammash Department of Information Technology PSNA College of Engineering and TechnologyDindigul624622India Department of Electronics and Communication Engineering K.Ramakrishnan College of TechnologyTrichy621112India Department of Electronics and Communication Engineering K.Ramakrishnan College of TechnologyTiruchirappalli621112India Deparmtent of Applied Data Science Noroff University CollegeKristiansandNorway Department of Computer Science Faculty of Engineering&InformaticsUniversity of BradfordBradfordUnited Kingdom English Department University CollegeTarabaTaif UniversityTaif21944Saudi Arabia

Sentiment Analysis(SA)is one of the subfields in Natural Language Processing(NLP)which focuses on identification and extraction of opinions that exist in the text provided across reviews,social media,blogs,news,and so *** has the ability to handle the drastically-increasing unstructured text by transform-ing them into structured data with the help of NLP and open source *** current research work designs a novel Modified Red Deer Algorithm(MRDA)Extreme Learning Machine Sparse Autoencoder(ELMSAE)model for SA and *** proposed MRDA-ELMSAE technique initially performs pre-processing to transform the data into a compatible ***,TF-IDF vec-torizer is employed in the extraction of features while ELMSAE model is applied in the classification of ***,optimal parameter tuning is done for ELMSAE model using MRDA technique.A wide range of simulation analyses was carried out and results from comparative analysis establish the enhanced effi-ciency of MRDA-ELMSAE technique against other recent techniques.

关键词： Sentiment analysis data classification machine learning red deer algorithm extreme learning machine natural language processing

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