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International Conference on Information Technology, Information Systems and Electrical engineering (ICITISEE)

作者： Jihadul Akbar Ema Utami Ainul Yaqin Magister of Informatics Engineering Universitas Amikom Yogyakarta Yogyakarta Indonesia Computer Science Faculty Universitas Amikom Yogyakarta Yogyakarta Indonesia

Movies were still a very popular means of entertainment. The current distribution of internet users causes a large amount of movie data to be created and distributed online. The emergence of movie streaming services makes consumers very interested in using automatic film genre classification. In this study, a multi-label film genre classification will be carried out based on an English synopsis. Data were collected from the Internet Movie Database (IMDb) website. The amount of data used in this study was 10,432 lines of data obtained using scraping techniques on June 7, 2022. Researchers divided the dataset labels into 18 labels representing each genre. Feature extraction using TF-IDF and Stemming. The multi-label classification algorithm used is the Support Vector Machine, Logistic Regression, and Naive Bayes Algorithms. Optimal parameter search using GridSearch of each algorithm. The optimum result in this study was obtained f1-score value of 0.58 using the SVM algorithm with TF-IDF feature extraction with stemming dataset, followed by NB with the f1-score value of 0.48 and LR with an f1-score value of 0.43.

关键词： Electrical engineering Support vector machine classification Entertainment industry Motion pictures Feature extraction Classification algorithms Naive Bayes methods

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Using Arduino Iot Modules As A Low Cost Environmental Research Monitoring System

Using Arduino Iot Modules As A Low Cost Environmental Resear...

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computer Science and Emerging Technologies (CSET), International Conference on

作者： Murad Magableh Ziyad Marie Rajina R. Mohamed Mohd Hafizuddin Bin Ibrahim Julaily Aida Jusoh Rohit Kumar MIS BI and Data Analysis Department Faculty of Business and Financial Science Irbid National University Irbid Jordan Public administration Department Faculty of Administrative sciences and Informatics Al-Istiqlal University Palestine College of Computing dan Informatics Universiti Tenaga Nasional Malaysia Department of Electrical Engineering Politeknik Kuala Terenngganu Kuala Terengganu Malaysia Faculty of Informatics and Computing UniSZA Malaysia School of Computer Science and Engineering Jain Deemed To Be University Bangalore India

Environmental monitoring plays a crucial role in understanding the impact of human activities on our ecosystems. However, the cost and complexity of traditional monitoring systems often hinder widespread deployment, especially in resource-constrained regions. This research paper explores the feasibility and effectiveness of employing Arduino Internet of Things (IoT) modules as a low-cost alternative for environmental research monitoring. The study focuses on designing and implementing an Arduino-based environmental monitoring system capable of gathering real-time data on key environmental parameters, such as temperature, humidity, air quality, and soil moisture. The hardware setup comprises Arduino microcontrollers interfaced with a range of environmental sensors and connectivity modules for data transmission. To assess the system's reliability and accuracy, a series of field experiments were conducted in diverse environmental settings, including urban areas, agricultural zones, and natural habitats. The collected data was compared against measurements from conventional monitoring equipment to validate the Arduino-based system's performance. The research demonstrates that the Arduino IoT modules can effectively monitor and transmit environmental data with a reasonable level of accuracy and stability, making them suitable for various environmental research applications. The low-cost nature of Arduino components and the open-source ecosystem facilitate customization and scalability, enabling researchers and organizations to develop tailored monitoring solutions for specific environmental contexts. Additionally, the paper discusses the challenges and limitations encountered during the deployment of Arduino-based monitoring systems, including power constraints, sensor calibration, and data processing techniques. Furthermore, it explores potential avenues for improvement and optimization to enhance the system's capabilities and data accuracy. In conclusion, this research

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ADX: IOT-Based Integrated Smart Control System for Prevention of Airborne COVID-19 Spread

ADX: IOT-Based Integrated Smart Control System for Preventio...

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International Conference on Information Technology, Information Systems and Electrical engineering (ICITISEE)

作者： Rizqi Sukma Kharisma Kusrini Uyock Anggoro Saputro Mulia Sulistiyono Majid Rahardi Bernadhed Elik Hari Muktafin Faculty of Computer Science Universitas Amikom Yogyakarta Yogvakarta Indonesia Magister of Informatics Engineering Universitas Amikom Yogyakarta Yogyakarta Indonesia

Enclosed spaces are places with a higher potential for spreading the COVID-19 virus. This is because the COVID-19 virus can be carried in the air. Closed space makes the air last longer in the space. Moreover, closed spaces are widely used, such as homes, schools, malls, offices, places of worship, etc. So for closed spaces, serious attention must be given to avoid the spread of the virus. ADX is an IoT-based tool equipped with UVC rays that can kill viruses, including the COVID-19 virus. This tool can be controlled remotely, manually, or on a timer. So that the device can be activated first before the closed space is used. This study also used the ESP8266 microcontroller in supporting the development of IoT-based ADX tools. The results of this study with an accuracy of the IoT ADX system of 96.10% and the average response time is 1.47 seconds.

关键词： COVID-19 Electrical engineering Microcontrollers Aerospace electronics Control systems Time factors Information technology

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Instance-wise Grasp Synthesis for Robotic Grasping

Instance-wise Grasp Synthesis for Robotic Grasping

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Yucheng Xu Mohammadreza Kasaei Hamidreza Kasaei Zhibin Li School of Informatics University of Edinburgh UK Department of Artificial Intelligence Faculty of Science and Engineering Bernoulli Institute University of Groningen The Netherlands Department of Computer Science University College London UK

Generating high-quality instance-wise grasp con-figurations provides critical information of how to grasp specific objects in a multi-object environment and is of high importance for robot manipulation tasks. This work proposed a novel Single-Stage Grasp (SSG) synthesis network, which performs high-quality instance-wise grasp synthesis in a single stage: instance mask and grasp configurations are generated for each object simultaneously. Our method outperforms state-of-the-art on robotic grasp prediction based on the OCID-Grasp dataset, and performs competitively on the JACQUARD dataset. The benchmarking results showed significant improvements compared to the baseline on the accuracy of generated grasp configurations. The performance of the proposed method has been validated through both extensive simulations and real robot experiments for three tasks including single object pick-and-place, grasp synthesis in cluttered environments and table cleaning task.

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Efficient Chrome Extension for Phishing Detection Using Machine Learning Techniques

arXiv

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arXiv 2024年

作者： Thaçi, Leand Halili, Arbnor Vishi, Kamer Rexha, Blerim Faculty of Electrical and Computer Engineering University of Prishtina Kodra e Diellit p.n. Prishtina10000 Serbia Department of Informatics University of Oslo Gaustadalléen 23B OsloN-0373 Norway

The growth of digitalization services via web browsers has simplified our daily routine of doing business. But at the same time, it has made the web browser very attractive for several cyber-attacks. Web phishing is a well-known cyberattack that is used by attackers camouflaging as trustworthy web servers to obtain sensitive user information such as credit card numbers, bank information, personal ID, social security number, and username and passwords. In recent years many techniques were developed to identify the authentic web pages that user visits and warn them when the webpage is a phish. In this paper, we have developed an extension for Chrome as the most favorite web browser, that will serve as a middleware between the user and phishing websites. The Chrome extension named "NoPhish" shall identify a phishing webpage based on several Machine Learning techniques. We have used the training dataset from "PhishTank" and extracted the 22 most popular features as rated by Alexa database. The training algorithms used are Random Forest, Support Vector Machine, and k-Nearest Neighbor. The performance results show that Random Forest delivers the best precision. © 2024, CC BY.

关键词： Phishing

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A structure-activity relationship modelling of opioid compounds by using machine learning 15

A structure-activity relationship modelling of opioid compou...

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15th Conference of the Euro-American Consortium for Promoting the Application of Mathematics in Technical and Natural Sciences, AMiTaNS 2023

作者： Sapundzhi, Fatima Lazarova, Meglena Dzimbova, Tatyana Georgiev, Slavi Ivanova, Antonina Department of Communication and Computer Engineering Faculty of Engineering South-West University Neofit Rilski 66 Ivan Myhailov Str. Blagoevgrad2700 Bulgaria Faculty of Applied Mathematics and Informatics Technical University of Sofia 8 St. Kliment Ohridski Blvd. Sofia1000 Bulgaria Institute of Molecular Biology Bulgarian Academy of Sciences Acad. G.Bonchev Str bl. 21 Sofia1113 Bulgaria Department of Information Modeling Institute of Mathematics and Informatics Bulgarian Academy of Sciences Acad. Georgi Bonchev Str. bl. 8 Sofia1113 Bulgaria Department of Applied Mathematics and Statistics Faculty of Natural Sciences and Education University of Ruse 8 Studentska Str. Ruse7004 Bulgaria Department of Computer Science Faculty of International Economics and Administration Varna Free University Chernorizets Hrabar 84 Yanko Slavchev Str. Chaika Resort Varna9007 Bulgaria

Opiates are among the oldest drugs that are used to treat many medical problems. They are analgesic and sedative drugs that contain opium. The morphine is its most active ingredient and it is a widely used pain reliever despite its side effects. The main objective of this study is to construct a model which gives the structure-activity relationship among a series of mu-opioid ligands and molecular docking results. For this purpose, a model of mu-opioid receptors using machine learning is introduced. By obtaining a relationship between the docking results and the in vivo test, we could predict the biological effect of the newly synthesized ligands. © Published under licence by IOP Publishing Ltd.

关键词： Medical problems

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Student's skills competency test prediction using c4.5 algorithm 7

Student's skills competency test prediction using c4.5 algor...

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7th Mathematics, Science, and computer Science Education International Seminar, MSCEIS 2019

作者： Enri, Ultach Jaman, Jajam Haerul Ananda, Muhammad Rizky Informatics Engineering Program Faculty of Computer Science Universitas Singaperbangsa Karawang Indonesia

ISBN: (纸本)9781631902567

Competency Skills (UKK) is part of government intervention in ensuring the quality of education in the Secondary Vocational School aims to measure the achievement of a certain level of competency in students' appropriate competency skills. The purpose of the research is to find out how competent the students' in their vocation as well as new strategies for educators in providing more effective learning by using C4.5 algorithm combining with feature selection. Overall the results of the validation of the model experiments that have the best influence are supplied with a set of test accuracy values of 96.875%, and 13 optimal attributes are selected. Therefore, in this study, the C4.5 algorithm with feature selection can provide good and effective results. © 2020 EAI.

关键词： Students

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Comparison of Kernels on Support Vector Machine (SVM) Methods for Analysis of Cyberbullying

Comparison of Kernels on Support Vector Machine (SVM) Method...

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International Conference on Information Technology, Information Systems and Electrical engineering (ICITISEE)

作者： Sal Sabila Wijayanti Ema Utami Ainul Yaqin Master of Informatics Engineering Universitas Amikom Yogyakarta Yogyakarta Indonesia Computer Science Faculty Universitas Amikom Yogyakarta Yogyakarta Indonesia

Along with the development of social media, there are various abuses and deviations from the ethics of interaction by its users. One form of social media abuse is cyberbullying. Recently, cyberbullying has become more frequent and these events are still difficult to detect. Therefore, researchers conducted an analysis of cyberbullying on Twitter social media using the Support Vector Machine (SVM). The data used is the crawled data of Twitter in Indonesian. This is included in non-linear data so it requires the kernel to carry out the text mining process. However, to date there has been no specific research on what kernels are good to use in cases of cyberbullying. Therefore, researchers conducted an experiment to implement SVM on tweet classification. The researchers will also look for the best kernels among the four kernels, namely Polynomial, Radial Basis Function (RBF), Sigmoid, and Linear. From the results of the experiments conducted, the SVM method can be used for the classification of cyberbullying. In addition, it is known that the sigmoid kernel has the highest accuracy of 83.85%.

关键词： Support vector machines Text mining Electrical engineering Ethics Blogs Text categorization Cyberbullying

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HCA-NET: Hierarchical Context Attention Network for Intervertebral Disc Semantic Labeling

HCA-NET: Hierarchical Context Attention Network for Interver...

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IEEE International Symposium on Biomedical Imaging

作者： Afshin Bozorgpour Bobby Azad Reza Azad Yury Velichko Ulas Bagci Dorit Merhof Faculty of Informatics and Data Science University of Regensburg Germany Electrical Engineering and Computer Science Department South Dakota State University USA Faculty of Electrical Engineering and Information Technology RWTH Aachen University Germany Machine and Hybrid Intelligence Lab Northwestern University Chicago IL USA Fraunhofer Institute for Digital Medicine MEVIS Germany

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Accurate and automated segmentation of intervertebral discs (IVDs) in medical images is crucial for assessing spine-related disorders, such as osteoporosis, vertebral fractures, or IVD herniation. We present HCA-Net, a novel contextual attention network architecture for semantic labeling of IVDs, with a special focus on exploiting prior geometric information. Our approach excels at processing features across different scales and effectively consolidating them to capture the intricate spatial relationships within the spinal cord. To achieve this, HCA-Net models IVD labeling as a pose estimation problem, aiming to minimize the discrepancy between each predicted IVD location and its corresponding actual joint location. In addition, we introduce a skeletal loss term to reinforce the model’s geometric dependence on the spine. This loss function is designed to constrain the model’s predictions to a range that matches the general structure of the human vertebral skeleton. As a result, the network learns to reduce the occurrence of false predictions and adaptively improves the accuracy of IVD location estimation. Through extensive experimental evaluation on multi-center spine datasets, our approach consistently outperforms previous state-of-the-art methods on both MRI T1w and T2w modalities. The code-base is accessible to the public on GitHub.

关键词： Adaptation models Accuracy Spinal cord Semantics Pose estimation Predictive models Skeleton

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A comparison of unsupervised and supervised machine learning algorithms to predict water pollutions

A comparison of unsupervised and supervised machine learning...

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2022 International Conference on Industry Sciences and computer Science Innovation, iSCSi 2022

作者： Zamri, Nurnadiah Pairan, Mohammad Ammar Azman, Wan Nur Amira Wan Abas, Siti Sabariah Abdullah, Lazim Naim, Syibrah Tarmudi, Zamali Gao, Miaomiao Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Besut Campus Terengganu Besut Malaysia Management Science Research Group Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Malaysia Management Science Research Group Terengganu Kuala Nerus Malaysia Computer and Information Sciences University of Strathclyde Glasgow United Kingdom Universiti Technology MARA Johor Branch Segamat Johor Malaysia Faculty of Biology Medicine and Health School of Medical Siences University of Manchester Manchester United Kingdom

Clean and safe water is vital for our lives and public health. In recent decades, population growth, agriculture, industries, and climate change have worsened freshwater resource depletion and clean water pollution. Several studies have focused on water pollutions risk simulation and prediction in the presence of pollution hotspots. However, the increase and complexity of big data caused by uncertain water quality parameters led to a new efficient algorithm to trace the most accurate pollution hotspots. Therefore, this study proposes to offer different algorithms and comparative studies using Machine Learning (ML) algorithms. Ten different most widely used algorithms, including unsupervised and supervised ML, will be employed to categorize the pollution hotspots for the Terengganu River. Besides, we also validate algorithms' accuracies by improving and changing each parameter in ML algorithms. Our results list all the accurate and efficient ML algorithms for the classification of river pollutions. These results help to facilitate river prediction using efficient and accurate algorithms in various water quality scenario. © 2022 Elsevier B.V.. All rights reserved.

关键词： Rivers

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