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Proceedings of the 7th International Conference on Communication and Information Processing

作者： Ananda Rizky Duto Pamungkas Diyanatul Husna Fransiskus Astha Ekadiyanto I Ketut Eddy Purnama Afif Nurul Hidayati Mauridhi Hery Purnomo Ingrid Nurtanio Reza Fuad Rachmadi Supeno Mardi Susiki Nugroho Anak Agung Putri Ratna Department of Electrical Engineering Universitas Indonesia Indonesia Department of Computer Engineering Sepuluh November Institute of Technology Indonesia Department of Dermatology and Venereology Airlangga University Indonesia Department of Informatics Hassanudin University Indonesia

ISBN: (纸本)9781450385190

In this study, a higher level of security was implemented by using InterPlanetary File System (IPFS) based peer-to-peer data storage on an Ethereum blockchain. IPFS is used as an image data storage medium that generates an IPFS hash, or known as content identifier (CID). The CID is used to access image data on the IPFS, which can be done with the help of an IPFS gateway. The CID is sent into the blockchain using a smart contract. The results of the study demonstrates that the use of IPFS as additional security on a blockchain system can be done. The size of file sent using IPFS proportionally affects the time it takes for IPFS to store the file. The larger the file size, the more time it will take. For each additional 1 MB file, there is an approximately 5% increase in access time. The distance from the IPFS gateway will also affect file access times in which, the farther the IPFS gateway, the more time it takes. If the IPFS gateway is at a distance of 1,000 km, there will be a decrease in access time of about 5%. The distance between the IPFS gateway and the accessor also affects success with accessing data. For the most distant gateway, i.e. the United States, there was even an access failure of 24%.

关键词： IPFS Ethereum Telemedicine Blockchain Data storage

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Comparison of Google Analytics with Similar Web for Statistical Analysis of Website Traffic 1st

Comparison of Google Analytics with Similar Web for Statisti...

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1st International Conference on Advanced Network Technologies and Computational Intelligence, ICANTCI 2024

作者： Garg, Bindu Kasar, Manisha Kotecha, Ketan Rahmani, Mohammad Khalid Imam University College of Engineering Pune India Symbiosis Center for Applied Artificial Intelligence Symbiosis Institute of Technology Symbiosis International University Pune India Department of Computer Science College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia

ISBN: (纸本)9783031860713

This study addresses the burgeoning demand for website data collection and analysis in business operations, emphasizing the pivotal role of web analytics in providing crucial insights into customer behaviour. Despite the prevalence of various website traffic analysing tools, there exists a notable lack of comprehensive testing on the precision and reliability of these services. The research compares four key analytics measures from Google Analytics (GA) with SimilarWeb (SW) using average monthly statistics over a one-year period for 50+ websites across different nations and business verticals. Employing various statistical tests, including paired t-tests, Shapiro-Wilk tests, and correlation analysis, the study reveals statistically significant variations in key metrics between GA and SW. Notably, SimilarWeb tends to provide lower estimates for the number of visits and unique visitors compared to Google Analytics. However, the findings underscore the potential complementarity of using both tools in cases where direct access to a site’s analytics is not feasible, particularly for competitive intelligence and benchmarking purposes. The study’s extensive statistical analysis covers unique visitors, total visits, bounce rates, and average session duration, offering valuable insights into the nuanced variations and potential sources of error. There are statistically significant differences between the two web traffic tools in terms of unique visitors, total visits, average session time & bounce rates. In comparison to Google Analytics, SimilarWeb figures were on average, 40% less for unique visitors, 50% greater for average session time, 25% greater for bounce rate, and 20% lesser for total visits. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Error statistics

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Exploring the Benefits and Drawbacks of Machine Learning in Cybersecurity to Strengthen Cybersecurity Defences

Exploring the Benefits and Drawbacks of Machine Learning in ...

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Annual Software technology Conference

作者： Touraj Khodadadi Mazdak Zamani Saman Shojae Chaeikar Yashar Javadianasl Marieh Talebkhah Mojtaba Alizadeh Department of Information Technology Malaysia University of Science and Technology Selangor Malaysia School of Business and Information Sciences Felician University Rutherford NJ USA Australian Institute of Higher Education Macquarie University Sydney Australia Razak Faculty of Technology and Informatics Universiti Technology Malaysia Kuala Lumpur Malaysia Department of Computer and Communication Systems Engineering Universiti Putra Malaysian (UPM) Seri Kembangan Malaysia Department of Computer Engineering) Lorestan University Khorramabad Iran

Although the initial motivations for carrying out cyberattacks stay the same, cybercriminals demonstrated a heightened level of sophistication in their methodologies due to the growing number of people, and deluge of data, devices, and programs in the contemporary company. The efficacy of conventional cybersecurity measures in identifying and addressing emerging cyber threats is diminishing. In light of the escalating cyber-threat landscape, there is a pressing need to deploy sophisticated tools and technologies that can effectively identify, examine, and promptly respond to emerging attacks and threats. Artificial intelligence (AI) has become a ground¬breaking technology with enormous promise across various industries. One area where AI has made a considerable influence is cybersecurity. AI, notably Machine learning (ML), enhances cybersecurity against more complex attacks and addresses significant concerns such as real-time attack detection, data leakage prevention, malware protection, vulnerability assessments, and many more. There is a growing interest in ML-based solutions that automate the identification of attacks and address sophisticated cybersecurity challenges. This article examines the advantages of the most promising ML applications now in use while highlighting the drawbacks and factors businesses should carefully consider when using ML- powered tools in cybersecurity and ensuring they are used alongside other security procedures.

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Improving fNIRS Signal Quality Using Smoothing Filtering

Improving fNIRS Signal Quality Using Smoothing Filtering

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Progress in Applied Electrical engineering (PAEE)

作者： Aleksandra Kawala-Sterniuk Dariusz Mikołajewski Luis A. Leiva Tuukka Ruotsalo Adam Lysiak Barbara Grochowicz Edward Jacek Gorzeńariczyk Adrian Luckiewicz Anna Wieczorek Mariusz Pelc Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Opole Poland Institute of Computer Science Kazimierz Wielki University Bydgoszcz Poland Department of Computer Science University of Luxembourg Luxembourg Luxembourg University of Helsinki Denmark University of Copenhagen Finland Institute of Philosophy Kazimiez Wielki University Bydgoszcz Poland

Biomedical signals are extremely difficult to analyze, mainly due to the non-stationary nature of these signals. Filtering does not always bring the desired results, because often the desired information is filtered out. In the case of EEG signals, smoothing filters gave very good results. In this paper, various types of smoothing filters for the analysis of infrared spectroscopy signals were compared.

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An Intelligent Thermal Compensation System Using Edge Computing for Machine Tools

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Sensors 2024年第8期24卷 2531页

作者： Kristiani, Endah Wang, Lu-Yan Liu, Jung-Chun Huang, Cheng-Kai Wei, Shih-Jie Yang, Chao-Tung Department of Computer Science Tunghai University Taichung City407224 Taiwan Department of Informatics Krida Wacana Christian University Jakarta11470 Indonesia Industrial Technology Research Institute Hsinchu310401 Taiwan Department of Mechanical Engineering National Chin-Yi University of Technology Taichung City411030 Taiwan Research Center for Smart Sustainable Circular Economy Tunghai University Taichung City407224 Taiwan

This paper focuses on the use of smart manufacturing in lathe-cutting tool machines, which can experience thermal deformation during long-term processing, leading to displacement errors in the cutting head and damage to the final product. This study uses time-series thermal compensation to develop a predictive system for thermal displacement in machine tools, which is applicable in the industry using edge computing technology. Two experiments were carried out to optimize the temperature prediction models and predict the displacement of five axes at the temperature points. First, an examination is conducted to determine possible variances in time-series data. This analysis is based on the data obtained for the changes in time, speed, torque, and temperature at various locations of the machine tool. Using the viable machine-learning models determined, the study then examines various cutting settings, temperature points, and machine speeds to forecast the future five-axis displacement. Second, to verify the precision of the models created in the initial phase, other time-series models are examined and trained in the subsequent phase, and their effectiveness is compared to the models acquired in the first phase. This work also included training seven models of WNN, LSTNet, TPA-LSTM, XGBoost, BiLSTM, CNN, and GA-LSTM. The study found that the GA-LSTM model outperforms the other three best models of the LSTM, GRU, and XGBoost models with an average precision greater than 90%. Based on the analysis of training time and model precision, the study concluded that a system using LSTM, GRU, and XGBoost should be designed and applied for thermal compensation using edge devices such as the Raspberry Pi. © 2024 by the authors.

关键词： Edge computing

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Efficient Self-Learning Evolutionary Neural Architecture Search

SSRN

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SSRN 2023年

作者： Qiu, Zhengzhong Bi, Wei Xu, Dong Guo, Hua Ge, Hongwei Liang, Yanchun Lee, Heow Pueh Wu, Chunguo Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Jilin Changchun130012 China YGSOFT INC Guangdong Zhuhai519085 China Department of Electrical Engineering and Computer Science Informatics Institute Christopher S. Bond Life Sciences Center University of Missouri ColumbiaMO65211 United States College of Computer Science and Technology Dalian University of Technology Liaoning Dalian116081 China School of Computer Science Zhuhai College of Science and Technology Guangdong Zhuhai519041 China Department of Mechanical Engineering National University of Singapore 9 Engineering Drive 1 117575 Singapore

The evolutionary algorithm has become a major method for neural architecture search recently. However, the fixed probability distribution adopted by the traditional evolutionary algorithm makes the algorithm unable to control the increase or decrease of the individual architecture size and thus cannot guarantee the lightweight and inference efficiency of the candidate architecture. In addition, the existing approaches cannot learn the optimal sampling probability distribution relevant to the specific problem from the empirical information accumulated through the search process. What’s more, the algorithm needs to evaluate the performance of all the individual architectures, which requires huge computing resources and time overhead. To overcome these problems and challenges, an Efficient Self-learning Evolutionary Neural Architecture Search method, called ESE-NAS, is presented in this paper. Firstly, we propose a Model Size Control module, which generates the probability distribution for sampling the type of mutation operators according to the current model size, so as to control both the node number of network architecture and the sparsity of links, therefore ensuring that neural architectures can remain compact and efficient as they evolve. Thereafter, a Mutation Candidate Credit Assignment method is proposed, which enables the algorithm to dynamically adjust the probability distribution for available types of node operations and directed links according to the empirical information from individuals’ performance evaluation, which can guide the evolutionary direction of neural architecture and shorten the first hitting time of the optimal architecture. Finally, a neural architecture performance predictor is formulated, and the accuracy of each individual architecture is predicted by using the simplified input features, to further improve the efficiency of neural architecture search. Experiments show that the ESE-NAS method proposed in this paper can learn a highly i

关键词： Evolutionary algorithms

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INCODE: Implicit Neural Conditioning with Prior Knowledge Embeddings

arXiv

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arXiv 2023年

作者： Kazerouni, Amirhossein Azad, Reza Hosseini, Alireza Merhof, Dorit Bagci, Ulas School of Electrical Engineering Iran University of Science and Technology Iran Institute of Imaging and Computer Vision RWTH Aachen University Germany School of Electrical and Computer Engineering College of Engineering University of Tehran Iran Faculty of Informatics and Data Science University of Regensburg Germany Fraunhofer Institute for Digital Medicine MEVIS Bremen Germany Department of Radiology Northwestern University Chicago United States

Implicit Neural Representations (INRs) have revolutionized signal representation by leveraging neural networks to provide continuous and smooth representations of complex data. However, existing INRs face limitations in capturing fine-grained details, handling noise, and adapting to diverse signal types. To address these challenges, we introduce INCODE, a novel approach that enhances the control of the sinusoidal-based activation function in INRs using deep prior knowledge. INCODE comprises a harmonizer network and a composer network, where the harmonizer network dynamically adjusts key parameters of the activation function. Through a task-specific pre-trained model, INCODE adapts the task-specific parameters to optimize the representation process. Our approach not only excels in representation, but also extends its prowess to tackle complex tasks such as audio, image, and 3D shape reconstructions, as well as intricate challenges such as neural radiance fields (NeRFs), and inverse problems, including denoising, super-resolution, inpainting, and CT reconstruction. Through comprehensive experiments, INCODE demonstrates its superiority in terms of robustness, accuracy, quality, and convergence rate, broadening the scope of signal representation. Please visit the project’s website for details on the proposed method and access to the code. © 2023, CC BY.

关键词： Inverse problems

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MRIGAT: Many-to-many Recommendation using Interaction based Knowledge Graph Attention 22

MRIGAT: Many-to-many Recommendation using Interaction based ...

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3rd International Symposium on Artificial Intelligence for Medical Sciences, ISAIMS 2022

作者： Gao, Feng Yuan, Kai Gu, Jinguang Liu, Yun Department of Computer Science and Technology Wuhan University of Science and Technology China The Key Laboratory of Rich-Media Knowledge Organization Service of Digital Publishing Content Institute of Scientific and Technical Information of China China Guangxi Eco-engineering Vocational & Technical College China 1st Affiliated Hospital Dept. of Medical Informatics School of Biomedical Engineering & Informatic Nanjing Medical University China

ISBN: (纸本)9781450398442

Recommendation systems combining Graph Neural Networks and Knowledge Graphs have been successfully applied in various domains. However, most existing approaches only consider one-to-one or one-to-many user-item interactions and cannot cater to many-to-many recommendation scenarios, such as providing prescriptions based on clinical diagnosis and medical history. Providing such a recommendation requires considering the implicit interaction within input user features as well as output candidates. In this paper, we propose a two-stage knowledge graph attention aggregation mechanism that helps recommend drug combinations for a patient based on his conditions. First, a disease-drug interaction graph is constructed and integrated with the medical domain knowledge, forming a collaborative knowledge graph. Secondly, an intra-feature attention aggregation is performed to obtain the representations of diseases and drugs based on drug-drug and disease-disease interactions, respectively. Thirdly, an inter-feature attention aggregation is performed using the disease-drug interaction to better represent a user's condition. Finally, the user's condition representation is concatenated with other user features to generate the final user representation for the prescription recommendation. Experiments with realistic datasets show that our approach can outperform existing recommendation systems by 4.3% and 6.1% in precision and recall, respectively. © 2022 ACM.

关键词： Knowledge graph

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Lung cancer prediction using multi-gene genetic programming by selecting automatic features from amino acid sequences

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Computational Biology and Chemistry 2022年第0期98卷 107638-107638页

作者： Sattar, Mohsin Majid, Abdul Kausar, Nabeela Bilal, Muhammad Kashif, Muhammad Biomedical Informatics Research Lab Department of Computer and Information Sciences Pakistan Institute of Engineering and Applied Sciences Islamabad Pakistan MIS Division Pakistan Institute of Nuclear Science and Technology Islamabad Pakistan Department of Computing and Technology Iqra University Islamabad Pakistan

Lung cancer is one of the leading causes of cancer related deaths. Early diagnosis of lung cancer using automatic feature selection from large number of features is a challenging task. Conventionally, cancer diagnosis approaches use physical features that appear in later stages, while harmful effects have already been occurred due to abnormal somatic mutations. In order to extract useful novel patterns to efficiently predict cancer at early stages, we analyzed lung cancer related mutated genes that reveal useful information in protein amino acid sequences. For this, we developed a new evolutionary learning technique with biologically inspired multi-gene genetic programming algorithm using discriminant information of protein amino acids. The proposed model efficiently selects 23 discriminant features out of 1500 features. Then it combines the selected features and related primitive functions optimally for prediction of lung cancer. Hence, an efficient predictive model is constructed that helps in understanding the complex heterogeneous nature of lung cancer. The proposed system achieved area under ROC curve and accuracy values of 98.79% and 95.67%, respectively outperforming related lung cancer prediction approaches. © 2022 Elsevier Ltd

关键词： Proteins

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Regression models for predicting the effect of trash rack on flow properties at power intakes

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engineering Applications of Computational Fluid Mechanics 2024年第1期18卷

作者： Li, Shuguang Qasem, Sultan Noman Karami, Hojat Salwana, Ely Rezaei, Alireza Shahmirzadi, Danyal Band, Shahab S. School of Computer Science and Technology Shandong Technology and Business University Yantai China Computer Science Department Imam Muhammad Ibn Saud Islamic University Riyadh Saudi Arabia Computer Science Department Faculty of Applied Science Taiz University Taiz Yemen Civil Engineering Department Semnan University Semnan Iran Institute of Visual Informatics Universiti Kebangsaan Malaysia Selangor Malaysia Graduate School of Engineering Science and Technology National Yunlin University of Science and Technology Douliou Taiwan International Graduate School of AI National Yunlin University of Science and Technology Yunlin Taiwan

Vortex flow characteristics in a reservoir and horizontal water intake have been predicted by using regression models in this numerical research. In this paper, three standalone machine learning models–Random Forest (RF), K-nearest neighbours (KNN), Gradient Boosting (GB)–and a proposed hybrid model based on Lévy Jaya Algorithm (LJA) and GB (LJA-GB) are employed to estimate the effect of trash racks on flow properties at power intakes. The experimental data which are prepared for the proposed study in this paper were obtained through a rectangular laboratory tank 8.3 m³ with various submergence depths and Froude numbers on nine trash racks with 63.7%–84.1% opening, made out of 2, 2.5, 4, and 6 mm thick copper wire. The outcomes revealed that the proposed LJA-GB model shows the best overall performance among the four models used for estimation. Thus, the LJA-GB model has the lowest mean absolute error (MAE) (0.3344), mean squared error (MSE) (0.1784), and root mean squared error (RMSE) (0.4223) values and highest R-squared ((Formula presented.)) (0.9899) and Willmott’s index (WI) values (0.9508) in the testing stage metrics for (Formula presented.) estimation and MAE (0.0061), MSE (0.0001), RMSE (0.0073), (Formula presented.) (0.9971), WI (0.9727) for (Formula presented.) estimation. Whereas the RF and KNN models exhibited poor performance in both stages of estimation. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： experimental modelling Hydro power plant regression models vortex strength

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