Implementation Of Algorithm Research Articles

An Eco-Friendly and Low Cost IoT based Room Temperature Control by Fan Speed Regulation for Tropical Use

This work presents an Internet of Things (IoT) based room temperature monitoring and control system by fan speed regulation developed for use in rooms of tropical regions of West Africa. In this work, cutting-edge technologies were integrated, including IoT, and cloud- based monitoring to create a system capable of dynamically remote controlling fan speed based on real- time temperature data. The Dallas DS18B20 Waterproof Temperature Sensor serves as the cornerstone for accurate temperature monitoring. A Microcontroller (Node MCU ESP8266) with Wi-Fi Module facilitates IoT connectivity, allowing users to remotely monitor and control the system through the Blynk Cloud and the Blynk App. A 12V DC table fan, driven by a MOSFET which is being controlled through Pulse Width Modulation (PWM) by the microcontroller, enables fine- tuned speed adjustments. A 16x2 LCD display provides real-time feedback on current temperature and fan speed percentage, enhancing user awareness. The microcontroller programming involves the implementation of an adaptive algorithm for dynamic fan speed control based on the room temperature, user settings and some preset parameters conditioned for tropical region of West Africa. By dynamically adjusting fan speed based on real-time temperature data, the system optimizes energy consumption, providing sustainable and eco-friendly solutions. The circuit was designed and simulated in Proteus software, the code was written in Arduino IDE, tested on breadboard, implemented finally on Veroboard and all fitted inside a suitable box. After testing the system, it worked as expected and it was observed that the fan speed increases as the room temperature increases and vice versa. Also, the fan speed depends both on the room temperature and the set threshold value. It was also observed that the speed of the fan is at its maximum when the temperature is above 40°C. Thus, the developed system is good for room temperature control in the tropical region of West Africa.

Implementation of Ensemble Learning Algorithm - Stacking Regressor on PM2.5 Prediction Model

This study aims to develop a PM2.5 concentration prediction model using ensemble learning techniques, focusing on the application of stacking regressor. The developed model is compared with several other basic models, namely LSTM, Random Forest, XGBoost, and GBM, to evaluate its performance in terms of prediction accuracy. The results show that the stacking regressor model provides more accurate prediction results than these basic models. The LSTM model has a Mean Squared Error (MSE) value of 80.42 and a coefficient of determination (R²) of -0.29, which shows unsatisfactory performance despite being able to capture temporal patterns. Random Forest gave better results with an MSE of 10.90 and R² of 0.83, thanks to its ability to handle heterogeneous data. The XGBoost and GBM models showed similar performance, with MSE of 9.01 and 8.81 respectively and R² of 0.86. However, the stacking regressor model with the RidgeCV meta-learner achieved the best results with an MSE of 8.07 and R² of 0.87, indicating that this technique successfully combines the advantages of various base models to improve prediction accuracy. In conclusion, the stacking regressor model proved effective in improving PM2.5 prediction accuracy, making it a potential tool for air quality monitoring and supporting public health policy making. This research makes an important contribution to the development of ensemble learning-based air quality prediction methods, and the results can serve as a reference for future research

Improving Early Fault Detection in Machine Learning Systems Using Data Diversity-Driven Metamorphic Relation Prioritization

Metamorphic testing is a valuable approach to verifying machine learning programs where traditional oracles are unavailable or difficult to apply. This paper proposes a technique to prioritize metamorphic relations (MRs) in metamorphic testing for machine learning and deep learning systems, aiming to enhance early fault detection. We introduce five metrics based on diversity in source and follow-up test cases to prioritize MRs. The effectiveness of our proposed prioritization methods is evaluated on three machine learning and one deep learning algorithm implementation. We compare our approach against random-based, fault-based, and neuron activation coverage-based MR ordering. The results show that our data diversity-based prioritization performs comparably to fault-based prioritization, reducing fault detection time by up to 62% compared to random MR execution. Our proposed metrics outperformed neuron activation coverage-based prioritization, providing 5–550% higher fault detection effectiveness. Overall, our approach to prioritizing metamorphic relations leads to increased fault detection effectiveness and reduced average fault detection time. This improvement in efficiency can result in significant time and cost savings when applying metamorphic testing to machine learning and deep learning systems.

Machine learning-based input-augmented Koopman modeling and predictive control of nonlinear processes

Koopman-based modeling and model predictive control have been a promising alternative for optimal control of nonlinear processes. Good Koopman modeling performance significantly depends on an appropriate nonlinear mapping from the original state-space to a lifted state space. In this work, we propose an input-augmented Koopman modeling and model predictive control approach. Both the states and the known inputs are lifted using two deep neural networks (DNNs), and a Koopman model with nonlinearity in inputs is trained within the higher-dimensional state space. A Koopman-based model predictive control problem is formulated. To bypass non-convex optimization induced by the nonlinearity in the Koopman model, we further present an iterative implementation algorithm, which approximates the optimal control input via solving a convex optimization problem iteratively. The proposed method is applied to a chemical process and a biological water treatment process via simulations. The efficacy and advantages of the proposed modeling and control approach are demonstrated.

Implementation of Multiple Linear Regression Algorithm to Predict Air Temperature Based on Pollutant Levels in South Tangerang City

Implementation of Multiple Linear Regression Algorithm to Predict Air Temperature Based on Pollutant Levels in South Tangerang City

Development and Implementation of Clinical Decision Algorithms for Oncology.

Decision-making in healthcare often relies on narrative guidelines; however, these instruments are poorly accessible for supporting clinical decision-making. This study explores the application of rule-based decision logic in algorithmic modeling, emphasizing its great potential in clinical decision support and research. Integrating rule-based algorithms with existing information systems and real-world data poses a serious challenge. Integrating decision algorithms with information standards increases their effectiveness across various applications. This study outlines a method for constructing clinical decision trees (CDTs), highlighting their transparency and interpretability, using information standards as a design principle. We use the digitization of the Dutch breast cancer guideline through CDTs as a case study to exemplify their versatility and practical significance. The process step 'primary treatment' has been successfully translated from the narrative guidelines format to the anticipated ted computational format.

Multi-objective optimization of cable-road layouts in smart forestry

ABSTRACT Current cable-road layouts for timber harvesting in steep terrain are often based on either manual planning or automated layouts generated from low-resolution GIS data, limiting potential benefits and informed decision-making. In this paper, we present a novel approach to improve cable-road design using multi-objective optimization based on realistic cable-road representations. We systematically compare the effectiveness of single-objective and multi-objective optimization methods for generating layouts using these representations. We implement and evaluate the performance of a weighted single-objective approach, the AUGMECON2 and NSGA-II multi-objective methods in comparison to a layout manually created by a forestry expert, taking into account installation costs, harvesting volumes, residual stand damage and lateral yarding workload. In addition to implementing the first linear programming multi-objective optimization for realistic cable-road representations by adapting AUGMECON2, we also present the first implementation of a multi-objective genetic algorithm (NSGA-II) with simulated annealing for this purpose and evaluate their respective strengths. We find that the use of multi-objective optimization provides advantages in terms of cost-effective, balanced and adaptable cable-road layouts while allowing economic and environmental considerations to be incorporated into the design phase.

Global Sensitivity Analysis via Optimal Transport

We examine the construction of variable importance measures for multivariate responses using the theory of optimal transport. We start with the classical optimal transport formulation. We show that the resulting sensitivity indices are well-defined under input dependence, are equal to zero under statistical independence, and are maximal under fully functional dependence. Also, they satisfy a continuity property for information refinements. We show that the new indices encompass Wagner’s variance-based sensitivity measures. Moreover, they provide deeper insights into the effect of an input’s uncertainty, quantifying its impact on the output mean, variance, and higher-order moments. We then consider the entropic formulation of the optimal transport problem and show that the resulting global sensitivity measures satisfy the same properties, with the exception that, under statistical independence, they are minimal, but not necessarily equal to zero. We prove the consistency of a given-data estimation strategy and test the feasibility of algorithmic implementations based on alternative optimal transport solvers. Application to the assemble-to-order simulator reveals a significant difference in the key drivers of uncertainty between the case in which the quantity of interest is profit (univariate) or inventory (multivariate). The new importance measures contribute to meeting the increasing demand for methods that make black-box models more transparent to analysts and decision makers. This paper was accepted by Baris Ata, stochastic models and simulation. Funding: A. Figalli acknowledges the support of the ERC [Grant 721675] “Regularity and Stability in Partial Differential Equations (RSPDE)” and of the Lagrange Mathematics and Computation Research Center. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.01796 .

Variational Bayesian Approximation (VBA): Implementation and Comparison of Different Optimization Algorithms.

In any Bayesian computations, the first step is to derive the joint distribution of all the unknown variables given the observed data. Then, we have to do the computations. There are four general methods for performing computations: Joint MAP optimization; Posterior expectation computations that require integration methods; Sampling-based methods, such as MCMC, slice sampling, nested sampling, etc., for generating samples and numerically computing expectations; and finally, Variational Bayesian Approximation (VBA). In this last method, which is the focus of this paper, the objective is to search for an approximation for the joint posterior with a simpler one that allows for analytical computations. The main tool in VBA is to use the Kullback-Leibler Divergence (KLD) as a criterion to obtain that approximation. Even if, theoretically, this can be conducted formally, for practical reasons, we consider the case where the joint distribution is in the exponential family, and so is its approximation. In this case, the KLD becomes a function of the usual parameters or the natural parameters of the exponential family, where the problem becomes parametric optimization. Thus, we compare four optimization algorithms: general alternate functional optimization; parametric gradient-based with the normal and natural parameters; and the natural gradient algorithm. We then study their relative performances on three examples to demonstrate the implementation of each algorithm and their efficiency performance.

Optimization of algorithms for effective management of the measurement process in electrical impedance tomography and ultrasonic tomography

The article discusses the design of advanced embedded algorithms that aim to manage and control the measurement process using the Electrical Impedance Tomography and Ultrasonic Tomography methods. The project aims to develop solutions that optimize the performance of embedded devices that must operate on limited resources such as memory, computing power, or bandwidth. Minimizing energy consumption is an important aspect, especially for battery-powered devices. Algorithms must also be adapted to specific hardware constraints, such as low RAM or CPU limitations, which require complex engineering and optimization. Additionally, the project assumes the implementation of algorithms that consider security aspects, which is crucial in protecting data processed and transmitted by the device. It also requires the development of effective communication methods between the embedded device and other systems, including appropriate communication protocols.

Implementation and analysis of quantum-classical hybrid interactive image segmentation algorithm based on quantum annealer

Implementation and analysis of quantum-classical hybrid interactive image segmentation algorithm based on quantum annealer

Optimizing Urban Mobility: A Comprehensive Study on Intelligent Traffic Signal Control Systems

This paper explores the application of Adaptive Traffic Signal Control (ATSC) systems integrated with Information and Communication Technology (ICT) and the Internet of Things (IoT) to improve urban traffic management. ATSC systems utilize real-time traffic data from various sensors and cameras to adjust signal timings dynamically, aiming to reduce congestion and enhance flow efficiency. The study highlights how these systems process and react to live traffic conditions, discussing the implementation of machine learning models and optimization algorithms that refine signal timing strategies based on traffic density and flow patterns. Challenges like data accuracy, system integration, cybersecurity, and environmental impacts on sensor performance are examined, alongside the potential for improving urban mobility and reducing environmental effects through smarter traffic management solutions. The paper concludes by emphasizing the importance of continuous advancements in technology and infrastructure to support the scalability and effectiveness of ATSC systems in modern cities.

ACE: A general-purpose non-Markovian open quantum systems simulation toolkit based on process tensors.

We describe a general-purpose computational toolkit for simulating open quantum systems, which provides numerically exact solutions for composites of zero-dimensional quantum systems that may be strongly coupled to multiple, quite general non-Markovian environments. It is based on process tensor matrix product operators (PT-MPOs), which efficiently encapsulate environment influences. The code features implementations of several PT-MPO algorithms, in particular Automated Compression of Environments for general environments comprised of independent modes as well as schemes for generalized spin boson models. The latter includes a divide-and-conquer scheme for periodic PT-MPOs, which enable million time step simulations for realistic models. PT-MPOs can be precalculated and reused for efficiently probing different time-dependent system Hamiltonians. They can also be stacked together and combined to provide numerically complete solutions of small networks of open quantum systems. The code is written in C++ and is fully controllable by configuration files, for which we have developed a versatile and compact human-readable format.

Wild horse optimization algorithm implementation in 7-level packed U-cell multilevel inverter to mitigate total harmonic distortion

Introduction. Multilevel inverters (MLIs) are a popular industrial and, more especially, renewable energy application solution. This is because of its appetite for filters, low distortion class, and capacity to provide a multilayer output voltage that resembles a pure sine waveform. The novelty is in applying the wild horse optimization algorithm (WHOA) to adjust the sinusoidal pulse width modulation (SPWM) technique by producing the optimal reference signal parameters in a new multilevel inverter architecture known as the packed U-cell multilevel inverter (PUC-MLI). Purpose. This study helps with the idea of new inverter architecture and a modified pulse width modulation (MPWM) method to make the multilevel inverter smaller, cheaper, and with less total harmonic distortion (THD). Methods. We use the proposed approach to control a 7-level, single-phase PUC-MLI. The WHOA is used to discover the optimal parameters of the additional reference sine signal after being compared with SPWM to evaluate its performance in harmonic reduction. The simulation’s outcome was validated by building a PUC-MLI prototype. Results. Experimental results and simulations validate the effectiveness of the suggested approach. The WHOA-improved MPWM approach achieves a significant reduction in THD on the PUC-MLI output voltage, as indicated by the results. Practical value. THD in MLI output voltage will be reduced without spending any cost. The suggested solution works with many MLI topologies with varying output voltage levels. References 20, tables 6, figures 12.

Sensor for Real-Time Glucose Measurement in Aqueous Media based on Nanomaterials Incorporating an Artificial Neural Network Algorithm on a System-On-Chip

The aim of this paper is to present the development of a real-time measurement system for glucose in aqueous media. The proposed system incorporates two lines of research: i) design, synthesis, and implementation of a non-enzymatic electrochemical sensor of Multi-Walled Carbon Nanotubes with Copper nanoparticles (MWCNT-Cu) and ii) design and implementation of a machine learning algorithm based on an Artificial Neural Network Multilayer Perceptron (ANN-MLP), which is embedded in an ESP32 SoC (System on Chip). From the current data that is extracted in real-time during the oxidation-reduction process to which an aqueous medium is subjected, it feeds the algorithm embedded in the ESP32 SoC to estimate the glucose value. The experimental results show that the nanostructured sensor improves the resolution in the amperometric response by identifying an ideal place for data collection. For its part, the incorporation of the algorithm based on an ANN embedded in a SoC provides a level of 97.8 % accuracy in the measurements. It is concluded that incorporating machine learning algorithms embedded in low-cost SoC in complex experimental processes improves data manipulation, increases the reliability of results, and adds portability.

Structure and inference in hypergraphs with node attributes

Many networked datasets with units interacting in groups of two or more, encoded with hypergraphs, are accompanied by extra information about nodes, such as the role of an individual in a workplace. Here we show how these node attributes can be used to improve our understanding of the structure resulting from higher-order interactions. We consider the problem of community detection in hypergraphs and develop a principled model that combines higher-order interactions and node attributes to better represent the observed interactions and to detect communities more accurately than using either of these types of information alone. The method learns automatically from the input data the extent to which structure and attributes contribute to explain the data, down weighing or discarding attributes if not informative. Our algorithmic implementation is efficient and scales to large hypergraphs and interactions of large numbers of units. We apply our method to a variety of systems, showing strong performance in hyperedge prediction tasks and in selecting community divisions that correlate with attributes when these are informative, but discarding them otherwise. Our approach illustrates the advantage of using informative node attributes when available with higher-order data.

How to Protect of Reader Preference Privacy in Mobile Book Information Services: A Technical Method

Along with the wide popularization of mobile terminal devices such as smart phones, mobile libraries, which provide mobile book information services as the core business, have become an important part of people’s daily life. However, with the rapid development of emerging network technologies such as cloud computing, the server side of a mobile library has become more and more untrusted, and the problem of reader preference privacy associated with mobile book information services has become increasingly prominent. To this end, this paper proposes to construct a group of false requests of mobile book information service at a trusted client of a mobile library, which are then submitted to the untrusted server side together with a true request, so as to confuse the reader book preference privacy. Firstly, we define a theoretical model for the protection of reader preference privacy. It measures the confusion effect of false requests on a true request by introducing the concepts of book information entropy, location information entropy, and book location joint entropy. It measures the concealment effect of false requests on readers’ mobile book preferences by introducing the concepts of book category distance and location region distance. Secondly, we present a specific implementation algorithm of the theoretical model to generate a group of ideal false requests that meet the privacy constraints for readers’ true requests. Finally, theoretical analysis and experimental evaluation demonstrate the effectiveness of the proposed method, i.e., it can effectively improve the security of readers’ mobile book preference privacy on the untrusted server side, without compromising the availability of a mobile book information service.

Optimizing Cancer Patient Classification Forecasting With Bayesian Pattern Recognition

This research examines patterns in cancer treatment by analyzing electronic medical record (EMR) data, with the goal of optimizing healthcare provision and improving patient outcomes. The study aims to apply Bayesian prediction models and regression analysis to determine the posterior probability of comorbidities and forecast patient arrivals. The implemented algorithms allow for the customization of treatment techniques, resulting in enhanced effectiveness of therapy and improved decision-making in healthcare delivery. Utilizing Bayesian approaches to analyze EMR data provides insights into the intricacies of cancer treatment and related expenses. The application of this study could be useful to enhance healthcare information systems and informatics by using data-driven insights to improve cancer care practices and operational efficiency in hospital settings.

Visualization at exascale: Making it all work with VTK-m

The VTK-m software library enables scientific visualization on exascale-class supercomputers. Exascale machines are particularly challenging for software development in part because they use GPU accelerators to provide the vast majority of their computational throughput. Algorithmic designs for GPUs and GPU-centric computing often deviate from those that worked well on previous generations of high-performance computers that relied on traditional CPUs. Fortunately, VTK-m provides scientific visualization algorithms for GPUs and other accelerators. VTK-m also provides a framework that simplifies the implementation of new algorithms and adds a porting layer to work across multiple processor types. This paper describes the main challenges encountered when making scientific visualization available at exascale. We document the surprises and obstacles faced when moving from pre-exascale platforms to the final exascale designs and the performance on those systems including scaling studies on Frontier, an exascale machine with over 37,000 AMD GPUs. We also report on the integration of VTK-m with other exascale software technologies. Finally, we show how VTK-m helps scientific discovery for applications such as fusion and particle acceleration that leverage an exascale supercomputer.

Automatic detection of cardiac conditions from photos of electrocardiogram captured by smartphones

BackgroundResearchers have developed machine learning-based ECG diagnostic algorithms that match or even surpass cardiologist level of performance. However, most of them cannot be used in real-world, as older generation ECG...