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International Conference on Electrical engineering, Computer Science and Informatics (EECSI)

作者： Syaifuddin Syaifuddin Diah Risqiwati Hanugra Aulia Sidharta Faculty of Engineering School of Information Technology University of Muhammadiyah Malang Malang Indonesia Faculty of Technology and Design Computer Science Program Study Bina Nusantara Institute of Creative Technology Malang Malang Indonesia

ISBN: (纸本)9781538684030

In modern era, data has become precious and important, data leak can lead to high damage to business, impact on losing profit. To avoiding those problems, organization need equip with updated security tools to protecting data and network. As computer network growing rapidly, currently security become important to prevent from attack, both from outside and inside the network. In line with that, new vulnerability grown on everyday basis and resulting increment number of intrusion and attack. Unfortunately, each intrusion and attack have a different pattern and unique behavior, continuous development and research on intrusion detection is mandatory. By statistic, in 2017 attack associated with XSS attack is grew 39% compared with 2015.

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Integration of Artificial Intelligence in Software Defined Networking Technology through Developing Machine Learning Algorithms 3

Integration of Artificial Intelligence in Software Defined N...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Naibaho, Lamhot Saxena, Pulkit Raj Sharma, Kuldeep Alfurhood, Badria Sulaiman Pallavi, L. Pant, Bhasker Faculty of Letters and Languages Christian University of Indonesia English Language Education Study Program Indonesia Teer Thanker Mahaveer University College of Computing Science and Information Technology Uttar Pradesh Moradabad India Lovely Professional University Department of Computer Science and Engineering Punjab Jalandhar Phagwara India College of Computer Information Sciences Princess Nourah Bint Abdulrahman University Department of Computer Sciences Saudi Arabia B v Raju Institute of Technology Cse Telangana Narsapur India Graphic Era Deemed to Be University Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350399264

A future networking design called "software-defined networking"combines network programmability with centralized administration (SDN). Network administration is currently handled by SDN, which, at the regulator, which acts as the cable network processor and divides the data plane into a control plane and a data aircraft. Recent breakthroughs in artificial intelligence (Al) have shown a stronger inclination for the science community to benefit from their ability to give learning capabilities and enhance Indicator. The author of this research provides a comprehensive analysis of initiatives underway to incorporate Al with SDN. The study concluded that the three primary Al thread where scientific research was centered were computer science, conceptual, and fuzzy reasoning systems. The authors of this paper explore the several fields in which these approaches may be used, their potential future applications, and the innovations made possible by the integration of AI-based methods into the SDN paradigm. By choosing the right SDN controller, any large organization may lower network complexity, implementation expenses, and maintenance costs. The focus of this article is software defined networking's benefits and downsides (SDN). Following is a basic explanation of artificial intelligence and a list of some of its most significant applications in SDN. © 2023 IEEE.

关键词： Software defined networking

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Text-to-Speech User Interface for ChatGPT

Text-to-Speech User Interface for ChatGPT

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

作者： Alfa Faridh Suni Aryo Baskoro Utomo Khoiruddin Fathoni Budiandra Yusuf Mahendra Izzati Gemi Seinsiani Ahmad Fashiha Hastawan Computer Engineering Universitas Negeri Semarang Semarang Indonesia Electrical Engineering Universitas Negeri Semarang Semarang Indonesia Information Technology Education Universitas Negeri Semarang Semarang Indonesia English Literature Study Program Universitas Negeri Semarang Semarang Indonesia

ISBN: (数字)9798350378368

ISBN: (纸本)9798350378375

The emergence of OpenAI's ChatGPT is paying a lot of attention to Generative AI and its impact on Academic integrity. Generative AI is a system designed to generate content or output such as text, image, audio, simulation, video and code based on their respective training data. ChatGPT itself is in the form of a chatbot where users can communicate with AI through dialogue applications via text. GPT 3 API is the technology behind the ChatGPT. However, interaction via text is not as flexible as dialogue using voice. Therefore, speech-to-text technology is needed to interface between the user and the AI. Chatbot AI powered by GPT3 with the optional voice UI powered by Google Text to Speech clearly benefits accessibility and usability. The output of dialogue between human users and AI can be done through text or sound. This research attempts to implement a Text to Speech and vice versa Speech to Text as a User Interface on a Gen-AI using the GPT 3 API so that it will be more user-friendly and accessible. This research opens many possibilities for human-AI interaction (HAX) in implementing virtual assistants. A seamless dialogue between humans and AI will be our goal in the future.

关键词： Electrical engineering Generative AI Virtual assistants Training data Chatbots Text to speech Internet Usability Intelligent systems Speech to text

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Hunting axion dark matter signatures in low-frequency terrestrial magnetic fields

arXiv

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arXiv 2025年

作者： Taruya, Atsushi Nishizawa, Atsushi Himemoto, Yoshiaki Center for Gravitational Physics and Quantum Information Yukawa Institute for Theoretical Physics Kyoto University Kyoto606-8502 Japan Kavli Institute for the Physics and Mathematics of the Universe Todai Institutes for Advanced Study The University of Tokyo Kavli IPMU WPI Chiba Kashiwa277-8583 Japan Physics Program Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima Higashi-Hiroshima739-8526 Japan Astrophysical Science Center Hiroshima University Hiroshima Higashi-Hiroshima739-8526 Japan Graduate School of Science The University of Tokyo Tokyo113-0033 Japan Department of Liberal Arts and Basic Sciences College of Industrial Technology Nihon University Chiba Narashino275-8576 Japan

We show that Earth’s natural environment can serve as a powerful probe for ultralight axion dark matter. In the presence of global geomagnetic fields, the axions with masses ranging from 10−15 eV–10−13 eV induce electromagnetic waves in the (sub-) extremely low-frequency band (0.3 − 30 Hz) through the axion-photon coupling. We predict the amplitude of induced magnetic fields in the Earth-ionosphere cavity, taking the finite conductivity of the atmosphere into account. This allows us to constrain the axion-photon coupling parameter, gaγ, from the long-term monitoring data of the low-frequency magnetic fields, resulting in a significant improvement from the previous constraints down to gaγ 4 × 10−13 GeV−1 for axion mass ∼ 3 × 10−14 eV. © 2025, CC BY-NC-ND.

关键词： Geomagnetism

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Combination of Otsu and Canny Method to Identify the Characteristics of Solo Batik as Surakarta Traditional Batik

Combination of Otsu and Canny Method to Identify the Charact...

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International Conference of Computer and Informatics engineering (IC2IE)

作者： Jani Kusanti Agung Suprapto Faculty of Electrical and Information Engineering Informatics Study Program Surakarta University Surakarta Central Java Indonesia Faculty of Da'wah Islamic Broadcast Communication Studies (KPI) IAIN Salatiga Salatiga Central Java Indonesia

ISBN: (纸本)9781728123851

Batik Surakarta has characteristics that not everyone understands, the difference in characteristics lies in the design characteristics, primary colors, and motifs. Surakarta's traditional batik designs and motifs have cultural values that are worth preserving. The combination of the otsu method and the canny method is used to build an application system to improve the results of identifying traditional Surakarta batik images, characteristic features, and shapes of batik patterns. Segmentation is done by partitioning the image into the object area using otsu and determining the boundary of the object using canny to get the characteristics of the image. The results of segmentation processed by selecting and extracting features by selecting quantitative information from existing features using the GLCM method. The characteristics taken are energy, homogeneity, correlation, and contrast. The results of the four characteristics classified into seven Surakarta batik classes, namely Kawung motif, Sido Mukti motif, Truntum motif, Sawat motif, Satrio Manah motif, Parang motif, and Semen Rante motif. The data used are 100 image data, which divided into 70 training data and 30 test data. The results showed that the accuracy rate was 93%.

关键词： Image edge detection Feature extraction Image segmentation Informatics Backpropagation Gray-scale Correlation

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Impact of Filter-Based Feature Selection on Prediction Model Performance for Early Detection of Stunting

Impact of Filter-Based Feature Selection on Prediction Model...

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International Conference on Informatics and Computing (ICIC)

作者： Sri Handani Widiastuti Purwanto Adi Wibowo Ratih Wirapuspita Wisnuwardani Informatics Engineering Study Program Sekolah Tinggi Teknologi Bontang Indonesia Doctoral Program of Information System School of Postgraduate Studies Diponegoro Univercity Indonesia Department of Chemical Engineering Faculty of Engineering Diponegoro Univercity Semarang Indonesia Department of Informatics Faculty of Science and Mathematics Diponegoro Univercity Semarang Indonesia Faculty of Public Health Mulawarman Univercity Samarinda Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

This study investigates the impact of feature selection results using the filter method on the performance of predictive models for the nutritional status of children aged 0-23 months. This study aimed to understand how correlation-based feature selection methods affect the efficiency and accuracy of predictive models. The dataset comes from the Basic Health Research survey, which consists of 84 variables and around 130,000 data, including anthropometric, family, socioeconomic, and environmental information. In this study, various filter-based feature selection methods were applied to evaluate and identify features that have significant relationships with children’s nutritional status, both linearly and non-linearly. The mutual information method was used to identify complex relationships between features and target variables, while the Pearson Correlation Coefficient and information gain were used to assess the relevance of numerical and categorical features. Of the 84 variables in the dataset, 37 features were selected after the selection process. The model performance evaluation was carried out using the Random Forest (RF) and Gradient Boosting (GB) algorithms, by comparing the results between the dataset that had been selected and without feature selection. The RF model without feature selection produces accuracy: 0,84600, precision: 0,83958, recall: 0,84600, F1-score: 0,78962, and a computation time of 9 seconds. The GB model without feature selection produces accuracy: 0,86330, precision: 0,84722, recall: 0,86330, F1-score: 0,84925, and a computation time of 2 minutes 50 seconds. After feature selection and cross-validation, the RF model shows accuracy: 0,85701, precision: 0,83599, recall: 0,85701, and F1 score: 0,83484. The GB model shows accuracy at 0,86124, precision at 0,84226, recall at 0,86130, and F1 score at 0,84120.

关键词： Radio frequency Pediatrics Accuracy Correlation Computational modeling Predictive models Aging Feature extraction Prediction algorithms Numerical models

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Database Design for Website Service Guide 'Waterfall Tour South Sumatera'

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Journal of Physics: Conference Series 2019年第1期1167卷

作者： Meidyan Permata Putri Hendra Effendi Information Systems Study Program STMIK PalComTech Palembang Informatics Engineering Study Program STMIK PalComTech Palembang

The purpose of this research was to design a database system scheme for the 'Waterfall Tour South Sumatra' website service guide. The database design method on this Website used Database Life Cycle (DBLC). The variable in this research was the database design service guide 'Waterfall Tour South Sumatra' with a relational data model consisting of 4 (four) tables namely admin table, waterfall table, news table, testimony tables using conceptual schemes, logical schemes, and physical schemes.

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Large language models for diabetes training:a prospective study

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Science Bulletin 2025年第6期70卷 934-942页

作者： Haoxuan Li Zehua Jiang Zhouyu Guan Yuqian Bao Yuexing Liu Tingting Hu Jiajia Li Ruhan Liu Liang Wu Di Cheng Hongwei Ji Yong Wang Ya-Xing Wang Carol Y.Cheung Yingfeng Zheng Jihong Wang Zhen Li Weibing Wu Cynthia Ciwei Lim Yong Mong Bee Hong Chang Tan Elif I.Ekinci David C.Klonoff Justin B.Echouffo-Tcheugui Nestoras Mathioudakis Leonor Corsino Rafael Simó Charumathi Sabanayagam Gavin Siew Wei Tan Ching-Yu Cheng Tien Yin Wong Huating Li Chun Cai Lijuan Mao Lee-Ling Lim Yih-Chung Tham Bin Sheng Weiping Jia Shanghai University of Sport Shanghai 200438China School of Clinical Medicine Beijing Tsinghua Changgung HospitalTsinghua MedicineTsinghua UniversityBeijing 100084China Beijing Visual Science and Translational Eye Research Institute(BERI) Tsinghua MedicineTsinghua UniversityBeijing 100084China Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and EngineeringSchool of ElectronicInformationand Electrical EngineeringShanghai Jiao Tong UniversityDepartment of Endocrinology and MetabolismShanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai Diabetes InstituteShanghai Clinical Center for DiabetesShanghai 200240China MoE Key Lab of Artificial Intelligence Artificial Intelligence InstituteShanghai Jiao Tong UniversityShanghai 200240China Department of Cardiology the Affiliated Hospital of Qingdao UniversityQingdao 266011China CEMS NCMISHCMSMADISAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematics University of Chinese Academy of SciencesChinese Academy of SciencesBeijing 100049China Key Laboratory of Systems Health Science of Zhejiang Province School of Life ScienceHangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesHangzhou 310024China Beijing Institute of Ophthalmology Beijing Tongren HospitalCapital Medical UniversityBeijing Ophthalmology and Visual Sciences Key LaboratoryBeijing 100730China k.Department of Ophthalmology and Visual Sciences The Chinese University of Hong KongHong Kong 999077China State Key Laboratory of Ophthalmology Zhongshan Ophthalmic CenterSun Yat-sen UniversityGuangdong Provincial Key Laboratory of Ophthalmology and Visual ScienceGuangdong Provincial Clinical Research Center for Ocular DiseasesGuangzhou 510060China Department of Renal Medicine Singapore General HospitalSingHealth-Duke Academic Medical CentreSingapore169

Diabetes poses a considerable global health challenge,with varying levels of diabetes knowledge among healthcare professionals,highlighting the importance of diabetes *** Language Models(LLMs)provide new insights into diabetes training,but their performance in diabetes-related queries remains uncertain,especially outside the English language like *** first evaluated the performance of ten LLMs:ChatGPT-3.5,ChatGPT-4.0,Google Bard,LlaMA-7B,LlaMA2-7B,Baidu ERNIE Bot,Ali Tongyi Qianwen,MedGPT,HuatuoGPT,and Chinese LlaMA2-7B on diabetes-related queries,based on the Chinese National Certificate Examination for Primary Diabetes Care in China(NCE-CPDC)and the English Specialty Certificate Examination in Endocrinology and Diabetes of Membership of the Royal College of Physicians of the United ***,we assessed the training of primary care physicians(PCPs)without and with the assistance of ChatGPT-4.0 in the NCE-CPDC examination to ascertain the reliability of LLMs as medical *** found that ChatGPT-4.0 outperformed other LLMs in the English examination,achieving a passing accuracy of 62.50%,which was significantly higher than that of Google Bard,LlaMA-7B,and *** the NCE-CPFC examination,ChatGPT-4.0,Ali Tongyi Qianwen,Baidu ERNIE Bot,Google Bard,MedGPT,and ChatGPT-3.5 successfully passed,whereas LlaMA2-7B,HuatuoGPT,Chinese LLaMA2-7B,and LlaMA-7B ***-4.0(84.82%)surpassed all PCPs and assisted most PCPs in the NCE-CPDC examination(improving by 1%–6.13%).In summary,LLMs demonstrated outstanding competence for diabetes-related questions in both the Chinese and English language,and hold great potential to assist future diabetes training for physicians globally.

关键词： Diabetes Diabetestraining Largelanguagemodels Primarydiabetescare Prospectivestudy

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Passenger volume forecasting information system for PT. KAI Daop 2 Bandung using double exponential smoothing method

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IOP Conference Series: Materials Science and engineering 2020年第1期732卷

作者： E L Amalia B Harijanto A Santoso Informatics Engineering Study Program Information Technology Politeknik Negeri Malang

Daop 2 Bandung is one of the Indonesian railroad operations areas, under the administration of PT Kereta Api Indonesia (Persero). Daop 2 Bandung is currently experiencing a problem in predicting the number of passenger volumes. It is common for railway transportation services to experience passengers flows that increases or decreases in the volume which in turn causes the service of the PT. KAI becomes less optimum. Therefore, a forecasting information system is needed to forecast the volume of train passengers in the upcoming period. This study applied Double Exponential Smoothing method as a forecasting method. The data used is the number of passenger volume data for two periods, i.e. 2017 and 2018 periods. For forecasting accuracy, PE (Percentage Error) and MAPE (Mean Absolute Percentage Error) were used. The passenger volume forecasting results on the Executive Class category shows 946,824 with an error percentage of 11.9962%, the Business Class category shows 221,188 results with an error percentage of 21.6714%, and the Economy Class category shows 5,144,074 with an error percentage of 10.4366%.

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Performance LoRa Technology for Autonomous Vehicles 24

Performance LoRa Technology for Autonomous Vehicles

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24th International Seminar on Intelligent Technology and Its Applications, ISITIA 2023

作者： Adi, Puput Dani Prasetyo Purnama, Iwan Siregar, Arfanda Anugrah Juledi, Angga Putra Edi, Firman Karim, Abdul Wahyu, Yuyu Maulana, Fairuz Iqbal Susilo, Suhardi Atmoko Budi Toni Harahap, Ilham Maliki Patria, Lintang Bandung Indonesia Labuhanbatu University Faculty of Science and Technology North Sumatra Labuhanbatu Indonesia Politeknik Negeri Medan Telecommunication Engineering Study Program North Sumatra Medan City Indonesia Batam Institute of Technology Faculty of Industrial Technology Department of Engineering Management Riau Islands Batam City Indonesia Budi Darma University Department of Information Technology North Sumatra Medan Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia University Labuhanbatu Faculty of Teacher Training and Education North Sumatra Labuhanbatu Indonesia Universitas Terbuka Faculty of Engineering Jakarta Indonesia

ISBN: (纸本)9798350313956

This research is an approach to intelligent vehicles with a LoRa communication system, LoRaWAN compatible for Long-Range and Outdoor Communication, but in this paper, we will test the ability of LoRa to handle autonomous vehicles, a Blind-Adaptive Data method will be tried on vehicles systems. The system offered is when the vehicle is running, the sensor will send position data in real-time, the number of vehicles is four, and GPS data is sent alternately using the Blind Adaptive Data Rate principle to avoid data collisions, and when the GPS shows a certain position, the value of SF is ascertained, which affects the data rate and ToA, and the output given is RSSI on each car, speed of a car, power consumption of sensor nodes, signal power, SNR, Bit rate or data rate, and sensitivity. With the Blind ADR principle, it is ensured that there is no data collision between car position data that causes packet loss. © 2023 IEEE.

关键词： Autonomous vehicles

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