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3rd International Conference on Communications, Information, Electronic and Energy systems, CIEES 2022

作者： Bosakova-Ardenska, A.D. Danev, A. Gogova, T. University of Food Technologies Dept. of Computer Systems And Technologies Plovdiv Bulgaria University of Food Technologies Dept. of Technology of Cereals Fodder Bread And Confectionery Products Plovdiv Bulgaria

ISBN: (数字)9781665491495

ISBN: (纸本)9781665491495

It is developed a plugin for ImageJ that performs global thresholding through four innovative thresholding algorithms, which are designed last five years. These algorithms are HisMedian, SubMedian-1, SubMedian-2, and HisAnalysis. The algorithm SubMedian-2 uses secondary median value in the sub-histogram as a threshold and it is applied for white bread porosity evaluation. The algorithm HisAnalysis uses as a threshold a color with maximal frequency in image histogram and it is applied for brown bread porosity evaluation. It is implemented also an algorithm (MedHASM-2) that combines methods for threshold finding which are used by HisAnalysis and SubMedian-2 algorithms. This paper presents a research of possibilities for application of global thresholding algorithms in wheat bread type "Dobrudzha"porosity evaluation. Four samples of different brands of wheat bread are used for experiments. The porosity of tested samples is measured through physicochemical analysis. The results are compared with results of image processing through correlation analysis. The highest correlation coefficients are calculated for results of thresholding with algorithms HisAnalysis and MedHASM-2. Based on the results of statistical data processing it is proposed an equation for calculation of the bread porosity using the ratio of white pixels to all in binary images. The average error for bread porosity calculated with the proposed equation is about 1% using the algorithm MedHASM-2 and about 3% using the algorithm HisAnalysis. © 2022 IEEE.

关键词： Porosity

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On the (in)Security and Weaknesses of Commonly Used Applications on Large-Scale Distributed systems 24

On the (in)Security and Weaknesses of Commonly Used Applicat...

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24th International Conference on Control systems and computer Science, CSCS 2023

作者： Abate, Andrea Francesco Castiglione, Aniello Cimmino, Lucia De Angelis, Davide Flauto, Sara Volpe, Alberto University of Salerno Department of Computer Science Fisciano Italy University of Salerno Dept. of Management and Innovation Systems Fisciano Italy University of Naples Parthenope Department of Science and Technology Naples Italy

ISBN: (纸本)9798350313390

The modern use of communication, the information they contain and their protection at all stages (creation, storage, sending and receiving) have resulted in infrastructures that exponentially grow year by year and that must be accurately protected if we want to communicate with our interlocutors without others interfering (on purpose or unintentionally), intercepting, modifying or damaging our communication fraudulently. Today most of the information flowing over the Internet is transported over large-scale distributed systems by the secure version of the HTTP protocol (the HTTPS protocol). The use of Cryptography within the HTTPS protocol provides a fair level of security that makes users sleep peacefully and, even worse/better (depending on the point-of-view), trust the entire communication flow passing through the large-distributed systems. The paper shows how it is possible to analyze web applications that use the TLS protocol for protecting the communications, with the main purpose of doing reverse engineering on it and try to do a kind of Vulnerability Assessmentaimed at improving the security of such web applications. Authors discovered serious flaws in several large-scale distributed systems/applications: those flaws were exploited for reading and modifying messages sent over a secure HTTPS communication channel or, on the other side, that can be used for understanding the inner functioning of a given application. Authors show that it is quite common to see login/passwords in clear-text going back and forth over the network. Last but not least, the proposed approach undermines the basis of the mutual trust between institutions when such trust is based on the principle of the federated identity within institutions. © 2023 IEEE.

关键词： Network security

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Predicting Lifespan of Ground-to-Air Multipath Components in mmWave UAV Channels

arXiv

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arXiv 2025年

作者： Khawaja, Wahab Jacobsen, Rune H. Hussain, Sajid Guvenc, Ismail Dept. Electrical and Computer Engineering Aarhus University Aarhus8000 Denmark Dept. Computer Systems Engineering Mirpur Univ. Science and Technology AJK Mirpur10250 Pakistan

In mobile ground-to-air (GA) propagation channels, the birth and death of multipath components (MPCs) are frequently observed, and the wide-sense stationary uncorrelated scattering (WSSUS) assumption does not always hold. Several methods exist for tracking the birth and death of MPCs, however, to the best of knowledge of authors, there is no existing literature that addresses the prediction of the lifespan of the MPCs in non-WSSUS GA propagation channels. In this work, we consider the GA channel as non-WSSUS and individual MPCs across receiver positions are represented as time series based on the Euclidean distance between channel parameters of the MPCs. These time series representations, referred to as path bins, are analyzed using a semi-Markov chain model. The channel parameter variations and dependencies between path bins are used to predict the lifespan of path bins using weighted sum method, machine learning classifiers, and deep neural networks. For comparison, the birth and death of path bins are also modeled using a Poisson distribution and a Markov chain. Simulation results demonstrate that deep neural networks offer highly accurate predictions for the lifespan (including death) of MPC path bins in the considered GA propagation scenario. © 2025, CC BY.

关键词： Markov chains

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Improving the performance of Dragonfly networks through restrictive Proxy routing strategies

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computer Networks 2025年 267卷

作者： Navaridas, Javier Pascual, Jose A. Dept. of Computer Architecture and Technology Faculty of Computer Science University of the Basque Country UPV/EHU Paseo Manuel de Lardizabal 1 Donostia-San Sebastián20018 Spain

Dragonfly has become the network of choice for large-scale high-performance computing systems and, indeed, it dominates the top positions of supercomputer rankings. The reason for this is that it offers a sweet spot in terms of cost, simplicity, performance, fault-tolerance and power consumption. In this work, we propose a collection of routing strategies which restrict proxies to be adjacent to either the local or the remote router. This way, it features shorter paths than the standard Valiant routing. We carry out an extensive simulation-based evaluation to assess their performance. Our experiments found latency reductions of up to 76% and throughput improvements of up to 26% when compared with standard Valiant routing when using synthetic traffic from independent sources at different scales. Furthermore, when using realistic application-inspired workloads, we found the strategies required between 5% and 20% less time to perform communications. In general, we observe that selecting proxies that are adjacent to the sender is more beneficial than those adjacent to the destination because the latter tends to generate backpressure in the last level of the interconnect. Interestingly, we found that the most restrictive proxy routing strategies obtain the best results in all scenarios and show that counterintuitively, the lower the path diversity, the more balanced the use of network resources. Our study includes investigating the interplay between routing and Dragonfly parameters and provide optimal parameters for proxy-based routing algorithms. Finally, we discuss some practical considerations related to the deployment of our strategies. © 2025 The Authors

关键词： Interconnection networks (circuit switching)

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Rain Predictive Model using Machine learning Techniques

Rain Predictive Model using Machine learning Techniques

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2023 International Multi-Disciplinary Conference in Emerging Research Trends, IMCERT 2023

作者： Muneer, Muhammad Shahbaz Nabeel Mustafa, Syed Muhammad Zehra, Syeda Sundus Maqsood, Haniya NED University of Engineering and Technology Dpertment of Computational Finance Karachi Pakistan Sir Syed Uuniversity of Engineering and Technology Dept. of Software Engineering Karachi Pakistan NED University of Engineering and Technology Department of Computer and Information Systems Engineering Karachi Pakistan

ISBN: (纸本)9798350347708

Climate is rapidly changing around the world. Over time, there have been significant changes in the weather. Rainfall is now erratic due to climate change. The frequency of extreme weather events like droughts and floods has increased due to climate change, necessitating the need for more precise and timely rainfall forecasts. For strategic reasons including agriculture, water resource management, and architectural design, rain forecasting is crucial. The naturally occurring non-stationary component in the rainfall time series impairs model performance for practical hydrologists and drought risk assessors. We present a rain predicting model based on machine learning to address the forecasting issue. In our work, we predict the possibility of rain the next day on the basis of last 10 years' data. The variables that were calculated during the experiments were humidity, pressure, evaporation, sunshine, rainfall, and so on. Random Forest gave the 90% accuracy with 0.904 Area under Curve, highest out of all the algorithms. The model's performance will significantly aid in the rain forecast. © 2023 IEEE.

关键词： Rain

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Advanced Tool Learning and Selection System (ATLASS): A Closed-Loop Framework Using LLM

arXiv

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arXiv 2025年

作者： Haque, Mohd Ariful Williams, Justin Siddique, Sunzida Islam, Md. Hujaifa Ali, Hasmot Gupta, Kishor Datta George, Roy Dept. of Cyber Physical Systems Clark Atlanta University United States Dept. of Computer Science and Engineering Daffodil International University Bangladesh Dept. of Computer Science and Engineering Ahsanullah University of Science and Technology Bangladesh

The combination of LLM agents with external tools enables models to solve complex tasks beyond their knowledge base. Human-designed tools are inflexible and restricted to solutions within the scope of pre-existing tools created by experts. To address this problem, we propose ATLASS, an advanced tool learning and selection system designed as a closed-loop framework. It enables the LLM to solve problems by dynamically generating external tools on demand. In this framework, agents play a crucial role in orchestrating tool selection, execution, and refinement, ensuring adaptive problem-solving capabilities. The operation of ATLASS follows three phases: The first phase, Understanding Tool Requirements, involves the Agents determining whether tools are required and specifying their functionality;the second phase, Tool Retrieval/Generation, involves the Agents retrieving or generating tools based on their availability;and the third phase, Task Solving, involves combining all the component tools necessary to complete the initial task. The Tool Dataset stores the generated tools, ensuring reusability and minimizing inference cost. Current LLM-based tool generation systems have difficulty creating complex tools that need APIs or external packages. In ATLASS, we solve the problem by automatically setting up the environment, fetching relevant API documentation online, and using a Python interpreter to create a reliable, versatile tool that works in a wider range of situations. OpenAI GPT-4.0 is used as the LLM agent, and safety and ethical concerns are handled through human feedback before executing generated code. By addressing the limitations of predefined toolsets and enhancing adaptability, ATLASS serves as a real-world solution that empowers users with dynamically generated tools for complex problem-solving. © 2025, CC BY-SA.

关键词： Reusability

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Enhancing Diabetes Prediction using Hybrid Feature Selection and Ensemble Learning with AdaBoost 8

Enhancing Diabetes Prediction using Hybrid Feature Selection...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Thuraka, Bharadwaj Pasupuleti, Vikram Kodete, Chandra Shikhi Chigurupati, Ravi Swaroop Tirumanadham, N. S. Koti Mani Kumar Shariff, Vahiduddin Northwest Missouri State University School of Computer Science and Information Systems MaryvilleMO64468 United States Eastern Illinois University School of Technology CharlestonIL61920 United States Dept. of Cse Bhimavaram India Sir C R Reddy College of Engineering Dept. of Cse Eluru India

ISBN: (纸本)9798350376425

This research relies on a feature selection method termed LasNO to analyse the progress of diabetes prediction. The ANOVA integrated with Lasso regularization identifies significant predictors like Age, BMI, DiabetesPedigreeFunction, and (Glucose). An AdaBoost model with the following characteristics was developed, and the results were exceptional performance metrics: 97.02% accuracy, 87.65% precision, and 82.5% recall. The results of the primary focus on glucose levels to differentiate between events with and without diabetes have been affirmed from the visual analyses. It also calls into attention that due to the appropriate cross-validation, LasNO is very successful on improving the interpretability and the possibility of generalization of the model. Such findings make it clear that efforts towards early detection of the condition and subsequent timely implementation of measures to contain the illness can go quite a long way in enhancing the quality of the lives of people that contract the disease. Further research is likely to be devoted to the assessing of the improved and broadened validation of the dataset as a critical approach for the enhancement and expansion of the model for use in clinics. As a result, this study aims to advance diabetes theragnostic possibilities and healthcare recommendations for better action based on ML approaches enhancing the accuracy of predictions. In this study, approaches to feature selection used for improving the performance while also making the models more understandable are the focus. The idea is to ensure that medical practitioners have reliable tools to undertake early detection and management of diabetes. Thus, the main objective of dealing with patients' Health-Related Quality of Life is to simultaneously increase the quality of patients' living while decreasing the costs of their treatment. © 2024 IEEE.

关键词： Prediction models

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Comparative Analysis of Robotic Arm Efficiency: Evaluating PID and Fuzzy-PID Control using Printed Mechanical Structure and ESP8266 Integration 6

Comparative Analysis of Robotic Arm Efficiency: Evaluating P...

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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Crisnapati, Padma Nyoman Susila, I. Made Darma Aryanto, I. Komang Agus Ady Krohkaew, Jaturapith Diaz, Ricky Aurelius Nurtanto Pande, I. Made Suandana Astika Department of Information Systems Faculty of Informatics and Computers Institute Technology and Business STIKOM Bali Denpasar Indonesia Department of Computer Systems Faculty of Informatics and Computers Institute Technology and Business STIKOM Bali Denpasar Indonesia Department of Information Technology Faculty of Informatics and Computers Institute Technology and Business STIKOM Bali Denpasar Indonesia Dept. of Big Data Management and Analytics Faculty of Science and Technology Rajamangala University of Technology Thanyaburi Thanyaburi Thailand

ISBN: (纸本)9798331539603

This study investigates the comparative effectiveness of manual PID tuning versus Fuzzy-PID control in enhancing the performance of a robot arm with two degrees of freedom (2-DOF). The experimental approach involves conducting two scenarios: manual tuning of PID parameters in Scenario 1 and utilizing a fuzzy algorithm for PID parameter tuning in Scenario 2. The novelty of this research lies in the new 3D mechanical design of the 2-DOF robot arm and the utilization of the ESP8266 microcontroller for implementing Fuzzy-PID control. Performance evaluation is conducted using Root Mean Square Error (RMSE) and Root Mean Square Percentage Error (RMSPE) calculations. The results reveal that Fuzzy-PID control significantly reduces errors compared to manual PID tuning, with lower RMSE and RMSPE values indicating more precise and stable control. These findings underscore the potential of integrating fuzzy logic with PID control to enhance system adaptability and flexibility. Additionally, the ESP8266 is capable of handling the computations required for fuzzy-PID control while being cost-effective. © 2024 IEEE.

关键词： Robotic arms

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Evaluating the Solutions to Predict the Impact of Lung Cancer with an Advanced Intelligent Computing Method 5

Evaluating the Solutions to Predict the Impact of Lung Cance...

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5th International Conference on Smart systems and Inventive technology, ICSSIT 2023

作者： Sundar, R. Ramadass, Sudhir Meeha, D. Subramanian, Balambigai Shankar, S. Siva Parasa, Gayatri Madanapalle Institute of Technology and Science Dept. of Computer Science and Engineering India Sterck Systems Chennai India Dr. Rangarajan Dr. Sakunthala Engg College Vel Tech Multi Tech Dept. of Information Technology Avadi Chennai India Dept. of Ece Kongu Engineering College Perundurai India Kg Reddy College of Engineering and Technology Dept. of Cse -AIandML Telangana Chilukuru India Koneru Lakshmaiah Education Foundation Dept. of Cse Vaddeswaram Andhra Pradesh Guntur India

ISBN: (纸本)9781665474672

Using symptoms as a basis for diagnosing lung cancer, Lung cancer detection was accomplished using several different machine-learning regression strategies. By comparing the efficacy of several regression algorithms for predicting lung cancer, considering factors including age, gender, chest discomfort, shortness of breath, alcohol intake, chronic illness, trouble swallowing, anxiety, and peer pressure. Lung cancer predictions and evaluations are made using regression methods such as the linear algorithm, polynomial regression, logistic regression, logarithmic regression, and multiple regression. With a predictive accuracy of 96%, multiple regression is superior to other regression techniques when identifying future lung cancer cases. The r-squared value, which can be calculated using several regression machine learning approaches, may also be used to evaluate the association between the various symptoms and lung cancer. Lung cancer is diagnosed using the r-squared value, which is calculated using several algorithms and takes into symptoms, including chronic illness. © 2023 IEEE.

关键词： Forecasting

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Fuzzy-Based Solar Magnetogram Image Retrieval

Fuzzy-Based Solar Magnetogram Image Retrieval

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IEEE International Conference on Fuzzy systems (FUZZ-IEEE)

作者： Rafal Grycuk Marcin Korytkowski Rafal Scherer Dept. of Intelligent Computer Systems Czestochowa University of Technology Czestochowa Poland

We propose a fast retrieval of full-disk images of the Sun magnetograms collected by the Solar Dynamics Observatory spacecraft. The images are produced at a high rate which entails the need to have a method for efficient searching in this vast collection. The method uses a hand-crafted integer-valued short descriptor of magnetograms and fuzzy rules created by boosting meta-learning. Fuzzy rule base is used as a database index. The speed-up comes from the short descriptor and fuzzy rule indexing. The efficiency and accuracy of the method is proved by the experiments.

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