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Dynamic Independent Component Extraction with Blending Mixing Vector: Lower Bound on Mean Interference-to-Signal Ratio 48

Dynamic Independent Component Extraction with Blending Mixin...

引用

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Cmejla, Jaroslav Koldovsky, Zbynek Kautsky, Vaclav Adali, Tulay Technical University of Liberec Faculty of Mechatronics Informatics and Interdisciplinary Studies Czech Republic University of Maryland Department of Electrical and Computer Engineering BaltimoreMD21250 United States

ISBN: (纸本)9781728163277

This paper deals with dynamic Blind Source Extraction (BSE) from where the mixing parameters characterizing the position of a source of interest (SOI) are allowed to vary over time. We present a new source extraction model called CvxCSV which is a parameter-reduced modification of the recent Constant Separation Vector (CSV) mixing model. In CvxCSV, the mixing vector evolves as a convex combination of its initial and final values. We derive a lower bound on the achievable mean interference-to-signal ratio (ISR) based on the Cramér-Rao theory. The bound reveals advantageous properties of CvxCSV compared with CSV and compared with a sequential BSE based on independent component extraction (ICE). In particular, the achievable ISR by CvxCSV is lower than that by the previous approaches. Moreover, the model requires significantly weaker conditions for identifiability, even when the SOI is Gaussian. © 2023 IEEE.

关键词： Blind source separation

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Comparative Analysis of Word vs. Character Embedding for Machine Learning Based Detection of Malicious URLs and DGA-Generated Domains 15

Comparative Analysis of Word vs. Character Embedding for Mac...

引用

15th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2023

作者： Ayodele, Oludayo C. Yerima, Suleiman Y. School of Computer Science and Informatics De Montfort University Leicester United Kingdom British University in Dubai Faculty of Engineering and Information Technology Dubai United Arab Emirates

ISBN: (纸本)9798350324433

This study presents a comprehensive comparative analysis of the effectiveness of word-level and character-level embeddings in the context of machine learning-based detection of malicious URLs and DGA-generated domains. Utilizing distinct datasets comprising DGA-generated domains and Spam URLs, we systematically evaluate various machine learning models coupled with word-level and character-level tokenization techniques. Our findings indicate that character-level tokenization yields superior results in identifying DGA-generated domains, particularly due to the random character composition of these URLs. Conversely, both word-level and character-level embeddings exhibit comparable success rates in classifying Spam URLs, owing to the non-random nature of their URL structures. The study sheds light on the importance of tailoring tokenization strategies based on the unique characteristics of the data. We recommend character-level embeddings for detecting DGA-generated domains characterized by random characters. In contrast, the choice between word-level and character-level embeddings is less critical when dealing with Spam URLs, as both approaches yield effective results. © 2023 IEEE.

关键词： Embeddings

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IoT-Enabled LoRa Wireless Sensor Network for Real-Time Air Quality Monitoring with Geographic Information System Mapping

IoT-Enabled LoRa Wireless Sensor Network for Real-Time Air Q...

引用

2023 International Conference on informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

作者： Maulana, Nurhuda Matheus Edward, Ian Joseph Universitas Pembangunan Nasional Veteran Jakarta Faculty of Computer Science Jakarta12450 Indonesia Bandung Institute of Technology School of Electrical Engineering and Informatics Bandung40132 Indonesia

ISBN: (纸本)9798350307306

The Internet of Things (IoT) can potentially change how humans interact with the environment. This technology is able to be applied in various fields, such as urban management, agriculture, environmental monitoring, health, and transportation. Conventional air quality monitoring systems are complex sensing equipment and they can measure various parameters of air pollutants. This system requires very high costs and maintenance. Therefore, a low-cost air quality measurement system is required and is able to measure the surrounding air pollutants. By using wireless sensor network technology as the acquisition of environmental monitoring sensor data. A system design was created in the form of geographic information capable of describing air quality in a particular area. The air parameters used are carbon monoxide, dust particulate, light intensity, temperature, and relative humidity. The wireless network uses the LoRa module connected to the gateway. The web server provides a database and geographic information system interface from the gateway through the MQTT broker. Data transmission using a radio frequency of 915 Mhz and able to send data sensors from various points. Outdoor testing at many buildings is capable of transmitting sensor data with a packet loss of 0% at a distance of fewer than 111 meters and the largest packet loss of 51.28% at a distance of 442 meters. © 2023 IEEE.

关键词： Internet of things

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Forecasting the Number of Traffic Accidents in Jakarta Using Artificial Neural Network

Forecasting the Number of Traffic Accidents in Jakarta Using...

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2023 International Conference on Information Technology and Computing, ICITCOM 2023

作者： Yulianti, Henny Pranoto, Gatot Tri Yaddarabullah Krishnasari, Erneza Dewi Marchenko, Michael Informatics Engineering Universitas Siber Asia Jakarta Indonesia Informatics Engineering Pelita Bangsa University Bekasi Indonesia Universitas Trilogi Department of Informatics Jakarta Indonesia Universitas Trilogi Department of Visual Communication Design Jakarta Indonesia Dnipro National University Dnipropetrovsk Faculty of Physics Electronics and Computer Systems Oblast Ukraine

ISBN: (纸本)9798350359633

Traffic accidents are a problem confronted by means of each a around the arena, Indonesia isn't any exception. The Indonesian national Police recorded an increase inside the number of traffic accidents in 2019 in comparison to 2018. based totally on POLRI information, there had been 107,500 traffic accidents in 2019, an increase from 103,672 incidents in 2018. in particular in Jakarta, that is the capital city of the Republic of Indonesia, traffic accidents are a common phenomenon, in conjunction with the unexpectedly developing use of motorized motors on the dual carriageway. for that reason, a examine is wanted to determine the variety of traffic accidents with the intention to occur in the future. The observe will forecast the number of traffic accidents for every month based totally on datasets from 2015 to 2020 from the Jakarta Metro Jaya Police Traffic Directorate. The approach used on this examine makes use of facts mining methods by using comparing the artificial Neural community Backpropagation, Deep getting to know and Linear Regression algorithms to decide which algorithm is right for predicting the range of traffic injuries inside the Jakarta region in each following month. This examine concludes that the synthetic Neural community Backpropagation set of rules is the first-rate set of rules to be applied in predicting the number of visitors accidents within the Jakarta vicinity with a Root suggest square mistakes (RMSE) of 29.477. studies trying out with the aid of applying the synthetic Neural network Backpropagation algorithm version has verified to expose the price of forecasting the number of traffic accidents in Jakarta with a mean monthly accuracy cost of 97.34%. © 2023 IEEE.

关键词： Forecasting

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Beyond Integration: Advancing Electricity Metering with Secure and Transparent Blockchain-IoT Solutions

Beyond Integration: Advancing Electricity Metering with Secu...

引用

International Conference on Telecommunications and Signal Processing (TSP)

作者： Kristián Koštál Muhammad Nasim Bahar Sultan Numyalai Michal Ries Faculty of Informatics and Information Technologies Institute of Computer Engineering and Applied Informatics Slovak University of Technology in Bratislava Bratislava Slovakia

ISBN: (数字)9798350365597

ISBN: (纸本)9798350365603

As the demand for reliable and accurate electricity metering escalates in the digital era, traditional systems increasingly need to improve due to their centralized nature, prone to inaccuracies, privacy breaches, and cybersecurity threats. This paper proposes a novel solution that harnesses the transformative power of Blockchain and Internet of Things (IoT) technologies to address these critical challenges. By integrating the immutable ledger capabilities of blockchain with the real-time data acquisition prowess of IoT, we introduce a decentralized system that ensures the secure and transparent measurement and billing of electricity for domestic consumers. Adopting Hyperledger Fabric within our framework offers a robust, scalable architecture that supports secure transactions and enhances trust through digital identities for all participants. This system mitigates the vulnerabilities associated with centralized authorities and efficiently monitors and manages energy consumption with unprecedented precision. Through this approach, we aim to revolutionize the electricity metering infrastructure, providing a blueprint for future advancements in the energy sector.

关键词： Electricity Signal processing Privacy breach Particle measurements Real-time systems Blockchains Telecommunications

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Comparison Between Bi-Directional LSTM And Transfer Learning in Correcting Typing Errors on Twitter Social Media Posts 11

Comparison Between Bi-Directional LSTM And Transfer Learning...

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11th International Conference on Cyber and IT Service Management, CITSM 2023

作者： Lubis, Arif Ridho Prayudani, Santi Hamzah, Muhammad Luthfi Politeknik Negeri Medan Department of Computer Engineering and Informatics Medan Indonesia Universitas Islam Negeri Sultan Syarif Kasim Riau Faculty of Science and Technology Pekanbaru Indonesia

ISBN: (纸本)9798350305968

Typing errors are a behavior that often occurs in communication via short messages or posts on social media platforms. In communicating on social media, many individuals without realizing it often make typing errors that can affect the meaning of sentences. This research will apply a model that can check typing errors using Bi-Directional LSTM and transfer learning which results in the Bi-Directional LSTM model achieving an accuracy of 78% after being trained for 20 epochs with a batch size of 64. On the other hand, the transfer learning model achieves an accuracy of 80% with the same epoch and batch size settings. Furthermore, when the Bi-Directional LSTM model was set with 20 epochs and batch size 64, the accuracy increased to 83%, while the transfer learning model achieved 87% accuracy. Finally, with 20 epochs and a batch size of 64, the Bi-Directional LSTM model achieved 87% accuracy, while the transfer learning model achieved an impressive accuracy of 93%. © 2023 IEEE.

关键词： Social networking (online)

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Performance Evaluation of Oversampling Methods on Deep Learning-Based Skin Cancer Classification

Performance Evaluation of Oversampling Methods on Deep Learn...

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2023 International Seminar on Application for Technology of Information and Communication, iSemantic 2023

作者： Supriyanto, Catur Salam, Abu Zeniarja, Junta Wijaya, Adi Universitas Dian Nuswantoro Bachelor Program in Informatics Engineering Faculty of Computer Science Semarang Indonesia Universitas Indonesia Maju Department of Health Information Management Jakarta Indonesia

ISBN: (纸本)9798350339215

The field of dermatology faces considerable challenges when it comes to early detection of skin cancer. Our study focused on using different datasets, including original data, augmented data, and SMOTE oversampled data, to identify skin cancer. Our dataset consisted of images of skin lesions from the MNIST Skin Cancer dataset (HAM 10000), including samples of both cancerous and benign cases in the dataset. We employed data augmentation to expand the dataset's size and increase the diversity of skin lesion features. Furthermore, to tackle class imbalance in the dataset, we applied the SMOTE oversampling technique to generate synthetic samples for the under-represented group. With the original, augmented, and SMOTE oversampled datasets, we trained a Convolutional Neural Network (CNN) model. The performance of the model was evaluated using accuracy, recall, precision, and F1-score. The comparison between the results obtained from the original data, augmented data, and SMOTE oversampling data clearly revealed distinctions in performance. Our findings clearly demonstrate that employing data augmentation and SMOTE oversampling can significantly enhance the efficacy of skin cancer detection. © 2023 IEEE.

关键词： Neural network models

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Blockchain-based Security for Cloud Data Storage 9

Blockchain-based Security for Cloud Data Storage

引用

9th IEEE International Conference on Smart Instrumentation, Measurement and Applications, ICSIMA 2023

作者： Bin Idrus, Mohammad Ariff Zakwan Rahman, Farah Diyana Abdul Khalifa, Othman Omran Yusoff, Nelidya Md Islamic University Malaysia Department of Electrical and Computer Engineering International Kuala Lumpur Malaysia Universiti Teknologi Malaysia Razak Faculty of Technology and Informatics Kuala Lumpur Malaysia

ISBN: (纸本)9798350343380

Data Cloud Storage is a service that allows data to save data on offsite storage system and managed by third-party and is made accessible by a web services user. It has become one of the most convenient and efficient methods to store data online. However, like other technologies and services, cloud storage has its advantages and disadvantages, but the challenge of security is the big issue. In this project, an investigation of the use of blockchain technology to improve the security of cloud data storage. The focus to address in this project is the vulnerability of cloud data storage systems to cyber-attacks and data breaches. The design and implementation of a proof-of-concept blockchain-based data storage system, and an evaluation of the system's security and performance will be presented. A proposal system to secure cloud storage could be an alternative for traditional methods. It is proposed to use blockchain as a data protection system. © 2023 IEEE.

关键词： Cloud storage

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Change Management in Preparing Indonesian Team to IOI 35

Change Management in Preparing Indonesian Team to IOI

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35th International Olympiad in informatics, IOI 2023

作者： Jingga, Felix Isal, Yugo K. Cendranata, Andreas Liem, Inggriani Mulyanto, Adi Indonesia Faculty of Computer Science University of Indonesia Indonesia Bebras Indonesia National Board Organization Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Indonesia

Indonesia has been participating at the International Olympiad in informatics (IOI) since 1995. The process of selecting the four contestants remains unchanged. All medalists in the national level competition were invited to take a series of training camps to select competitively the candidates. In the first two decades of participation in IOI, Indonesia have collected two gold medals, and these surprising achievements were merely more because of luck, and not as a result of a reliable training/selection process. We believe reliable and good training is a basis to predict and expect a stable achievement. Through careful observations and reflections, a change of course of action is needed to bring Indonesia’s achievement to the next level. This paper shares Indonesia’s experience in changing and implementing strategies during the national training camps to better prepare the four contestants to IOI. What are the changes taken by the coaching team that have been exercised that made it possible for Indonesia to achieve three gold in the last four consecutive years? © 2023 IOI, Vilnius University.

关键词： Human resource management

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Optimizing Resource Utilization Using Vector Databases in Green Internet of Things

Optimizing Resource Utilization Using Vector Databases in Gr...

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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Kumari, Rani Sah, Dinesh Kumar Cengiz, Korhan Nauman, Ali Ivković, Nikola Mihaljević, Ivan Birla Institutue of Technology Department of Computer Science and Engineering Ranchi India Siksha 'o' Anusandhan Department of Computer Science and Engineering Bhubaneswar India Istinye University Department of Computer Engineering Istanbul Turkey University of Hradec Kralove Faculty of Informatics and Management Department of Information Technologies Kralove50003 Czech Republic Yeungnam University Department of Information and Communication Engineering 280 Daehak-Ro Gyeongsan Gyeongbuk38541 Korea Republic of University of Zagreb Faculty of Organization and Informatics Pavlinska 2 Varaždin42000 Croatia

ISBN: (纸本)9798350370218

With the rapid proliferation of Internet of Things (IoT) devices and the ever-increasing volume of sensor data, optimizing resource utilization has become crucial for building sustainable and efficient IoT systems. In this study, we propose a novel approach for optimizing resource utilization in Green IoT through efficient storage and retrieval in vector databases. Our approach leverages specialized data structures, including k-d trees and ball trees, to achieve improved storage efficiency and accelerated retrieval performance for high-dimensional sensor data. We conducted extensive experiments to evaluate the effectiveness of our proposal, comparing it with traditional database approaches. The results demonstrate significant improvements in storage efficiency, with vector databases requiring considerably less storage space compared to traditional databases. Moreover, our approach enables fast and accurate retrieval of high-dimensional sensor data, reducing query times and enhancing real-time data analysis and decision-making capabilities. The technical achievements of our proposal offer promising prospects for the development of sustainable and efficient IoT systems in various domains, such as environmental monitoring, healthcare, and smart cities. Our work contributes to advancing the field of Green IoT by addressing the challenges of resource utilization and query performance through efficient storage and retrieval in vector databases. © 2023 IEEE.

关键词： Green IoT Indexing Techniques Resource Utilization Specialized Data Structures Vector Databases

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