Algorithm Technique Research Articles

‘It’s a who-you-know thing’: interactional fraud prevention in non-bank check cashing

Abstract This article demonstrates how presentations of self and the information conveyed within them are a conduit for risk evaluation within the marketplace. Using the case of check cashing, it investigates customer screening within the Detroit area’s most prevalent non-bank check cashing businesses, which are not standalone check cashing outlets but rather corner convenience and liquor stores known as ‘party stores’. Without algorithmic techniques based on hard or quantitative data, how do owners screen customers to prevent fraud? Drawing on 35 months of ethnographic fieldwork, this article uncovers two strategies toward this end: (a) gathering private information about customers through small talk; and (b) assessing check cashers’ presentations of self. Analyzing check cashing and fraud in this setting speaks to theoretical perspectives of risk evaluation by presenting a case of interactional fraud prevention, which highlights the interpersonal, interactional mechanisms through which soft information is exchanged and assessed in ex ante screening.

Identification and Control of Flexible Joint Robots Based on a Composite-Learning Optimal Bounded Ellipsoid Algorithm and Prescribe Performance Control Technique

This paper presents an indirect adaptive neural network (NN) control algorithm tailored for flexible joint robots (FJRs), aimed at achieving desired transient and steady-state performance. To simplify the controller design process, the original higher-order system is decomposed into two lower-order subsystems using the singular perturbation technique (SPT). NNs are then employed to reconstruct the aggregated uncertainties. An adaptive prescribed performance control (PPC) strategy and a continuous terminal sliding mode control strategy are introduced for the reduced slow subsystem and fast subsystem, respectively, to guarantee a specified convergence speed and steady-state accuracy for the closed-loop system. Additionally, a composite-learning optimal bounded ellipsoid algorithm (OBE)-based identification scheme is proposed to update the NN weights, where the tracking errors of the reduced slow and fast subsystems are integrated into the learning algorithm to enhance the identification and tracking performance. The stability of the closed-loop system is rigorously established using the Lyapunov approach. Simulations demonstrate the effectiveness of the proposed identification and control schemes.

Parameters optimization of solar PV cell using genetic-iterative algorithm

Abstract Accurately determining optimal parameters from current-voltage (IV) data in solar photovoltaic (PV) models is crucial for effective system simulation and control. In this study, we propose a novel approach that combines genetic algorithm and iterative techniques maximizing their strengths, and exploiting the influence of each parameter on the IV curve to categorize them into groups. This adaptable method can adjust the interval of each parameter to different scenarios during optimization. We evaluated the method across various solar cell models including both the ’SDM’ and ’SDM-based PMM’, achieving notable accuracy and reliability compared to other advanced meta-heuristic algorithms. The results indicate a value of 7.3870e-5 for the SDM and 9.3365e-4 for the PMM (STM6-40/36). The proposed algorithm demonstrates notable accuracy and reliability, highlighting its usefulness in accurately determining parameters in solar PV models.

BRAIN NEOPLASM IDENTIFICATION USING DEEP LEARNING

This study offers a powerful brain tumor detection system that makes use of deep learning techniques, especially the VGG16 learning algorithm and Convolutional Neural Network techniques (CNN). The ultimate objective of the recommended strategy is to enhance the accuracy and efficiency of identifying tumors in magnetic resonance imaging (MRI) images.. Utilizing CNN allows the model to automatically extract intricate features from input images, while VGG16, renowned for its deep architecture, contributes to a more intricate understanding of complex patterns. The integration of these algorithms empowers the system to discern subtle nuances indicative of brain tumors, providing a reliable and swift diagnostic tool for medical practitioners. Experimental results demonstrate the system's effectiveness in achieving high accuracy rates, showcasing its potential as an invaluable asset in early tumor detection and subsequent medical intervention. KEYWORDS: CNN, Brain tumor, Machine learning, Medical imaging

Analyzing groundwater storage anomalies in data‐scarce areas of Ethiopia's Rift Valley Basin using artificial neural network

AbstractGroundwater storage anomalies (GWSA) study requires the use of hydro‐climatic models coupled with Gravity Recovery and Climate Experiment climate (GRACE) and GRACE Follow‐On (GRACE‐FO) Terrestrial Water Storage experiments. Understanding groundwater storage anomalies in the research area is difficult due to the restricted hydro‐meteorological data availability, especially, groundwater levels, changes in groundwater storage, and budgetary and technical constraints. No such investigation of GWS anomalies prior to this study in this Basin was conducted. The research focuses on GWSA investigations in the basin employing Global Land Data Assimilation Systems and Artificial Neural Networks‐Back Propagation Algorithm techniques are required. The ultimate objective of this study was to reduce the GRACE GWS from 1° to 0.25° and close the discontinuity between GRACE/GRACE‐FO observations. The results illustrated that GRACE/GRACE‐FO GWS throughout the basin were overall declining. The GWS anomaly significantly decreased between 2011 and 2013, with the highest monthly depletion in March 2011 (−158 mm). In contrast, from mid‐June to mid‐October, the maximum positive value varied from 20.80 mm in the southernmost basin to 39.49 mm in the central‐southeastern basin. The basin's eastern, central‐southeastern, northern, and southern areas displayed positive monthly GWS trends. The entire dry season has been influenced by negative anomalies. Hence, the identification of GWS variations and a comprehensive understanding of their effects provided valuable scientific insights. Identifying and analyzing anomalies in groundwater storage (GWS) can provide valuable information. This data can be efficiently used to develop appropriate regulatory frameworks, which may contribute to the long‐term viability of the availability of groundwater.

Using Improved Hybrid Grey Wolf Algorithm Based on Artificial Bee Colony Algorithm Onlooker and Scout Bee Operators for Solving Optimization Problems

Grey Wolf optimization (GWO) is a newly developed stochastic meta-heuristic technique motivated by nature. It shows potential in diverse optimization challenges. It replicates grey wolf hunting behaviour and social hierarchy, exploring the solution space similar to their natural process. The algorithm efficiently explores and converges to the optimal solution. However, a drawback of the standard GWO is its limited exploitation capability due to its exploration-focused iterations. This may hinder finding the optimal solution nearby, leading to lower local convergence rates and degraded solution quality. To address this, the GWO-Employed-Onlooker model suggests incorporating the onlooker and scout bee operators from the artificial bee colony algorithm (ABC) during the position-changing stage of the grey wolves. This enhances exploitation capability, resulting in improved local convergence rates and better solution quality. The proposed method’s performance is evaluated on various optimization functions and compared their convergence rate to standard GWO, Genetic Algorithm (GA), Firefly Algorithm (FA), ABC, and Ant Colony Optimization (ACO) techniques. The results demonstrate that the proposed strategy GWO-Employed-Onlooker is better, indicating that it is valuable in solving optimization problems.

Classification of ECG Signals Using Machine Learning Techniques

Cardiovascular diseases are one of the leading causes of mortality in contemporary society. With the growth in the accumulation of medical data, new opportunities have arisen to enhance diagnostic accuracy using machine learning techniques. Heart diseases present symptoms that can be similar to other disorders or be mistaken for signs of aging. Furthermore, diagnosing based on electrocardiogram (ECG) signals can be challenging due to the variability in signal length and characteristics. This article has developed a methodology for classifying ECG signals using the k-Nearest Neighbor (kNN) algorithm and statistical techniques. 9000 ECG signal samples from the PhysioNet database were processed. The signals were normalized to a length of 9000 samples, and relevant features for classification, such as median, standard deviation, skewness, among others, were extracted. Multiple kNN models with different parameters were trained and evaluated on a test set. The models exhibited high performance in classifying normal signals but faced difficulties in correctly classifying signals with arrhythmias. The weighted kNN algorithm demonstrated the best accuracy, although all models showed a tendency to misclassify abnormal signals due to data imbalance. While significant accuracy was achieved in ECG signal classification, there is still room for improvement. Future strategies could involve extracting more relevant features, addressing data imbalance, and fine-tuning model hyperparameters. Integrating domain knowledge from the medical field and advanced signal processing techniques could further enhance classification accuracy.     Received: 3 January 2024 / Accepted: 7 April 2024 / Published: 5 May 2024

Rapeseed Seed Coat Color Classification Based on the Visibility Graph Algorithm and Hyperspectral Technique

Information technology and statistical modeling have made significant contributions to smart agriculture. Machine vision and hyperspectral technologies, with their non-destructive and real-time capabilities, have been extensively utilized in the non-destructive diagnosis and quality monitoring of crops and seeds, becoming essential tools in traditional agriculture. This work applies these techniques to address the color classification of rapeseed, which is of great significance in the field of rapeseed growth diagnosis research. To bridge the gap between machine vision and hyperspectral technology, a framework is developed that includes seed color calibration, spectral feature extraction and fusion, and the recognition modeling of three seed colors using four machine learning methods. Three categories of rapeseed coat colors are calibrated based on visual perception and vector-square distance methods. A fast-weighted visibility graph method is employed to map the spectral reflectance sequences to complex networks, and five global network attributes are extracted to fuse the full-band reflectance as model input. The experimental results demonstrate that the classification recognition rate of the fused feature reaches 0.943 under the XGBoost model, confirming the effectiveness of the network features as a complement to the spectral reflectance. The high recognition accuracy and simple operation process of the framework support the further application of hyperspectral technology to analyze the quality of rapeseed.

Enhanced Road Safety through Integrated Speed Breaker, Pothole, and Lane Detection System

Abstract: Amidst the rapid evolution of autonomous vehicle technologies, ensuring road safety remains a paramount challenge. Effective detection of lanes and potential hazards, including speed breakers and potholes, is critical for safe autonomous driving. In this study, we introduce an innovative Lane, Speed Breaker, and Pothole Detection System (LSPDS) utilizing YOLOv4 Tiny, a state-of-the-art object detection algorithm and computer vision techniques. Our system integrates computer vision and machine learning techniques for analysis of road conditions. By employing camera sensors, we capture the road scene and apply image processing algorithms to identify lanes, speed breakers, and potholes. Moreover, the system incorporates Firebase for user authentication and SMS services for real-time alerts. YOLOv4 Tiny is employed for accurate detection and classification of these features within the captured images, thereby enhancing the perception capabilities of autonomous vehicles.

A review on motion sickness of autonomous driving vehicles

The objective of this study is to investigate the symptoms, types, etiology, and assessment methods of motion sickness in autonomous vehicles in order to gain a comprehensive understanding of its occurrence mechanism and emphasize the significance of enhancing autonomous vehicle algorithms for improved ride comfort. Thus, this paper provides a synthesis and discussion of various theories while exploring strategies for mitigating motion sickness from three perspectives: passengers, vehicles, and external equipment. Firstly, it summarizes the clinical manifestations and classification of motion sickness while conducting an in-depth analysis of associated factors. Secondly, it evaluates different approaches for quantitatively measuring the severity and extent of motion sickness. Subsequently, it analyzes the reasons behind increased motion sickness caused by autonomous vehicles and emphasizes the importance of algorithmic improvements to enhance travel comfort. Finally, mitigation strategies are proposed considering passengers' needs as well as advancements in accurate motion prediction models and optimization techniques for autonomous planning and control algorithms that can effectively reduce the risk of motion sickness. As application scenarios for autonomous technology continue to expand, meeting user requirements while ensuring safety has become a benchmark for assessing technical proficiency. Therefore, promoting unmanned travel services necessitates a thorough analysis of existing issues related to autonomous technology along with prioritizing algorithm design enhancements through effective means to achieve an enhanced user experience.

Regional Lineament Pattern and Morphotectonic Analysis: The Investigation of Geological Structures and Present-Time Relative Tectonic Activity in the Tin Granite Area of Belitung Island, Indonesia

Regional Lineament Pattern and Morphotectonic Analysis: The Investigation of Geological Structures and Present-Time Relative Tectonic Activity in the Tin Granite Area of Belitung Island, Indonesia

Novel Entropy for Enhanced Thermal Imaging and Uncertainty Quantification

This paper addresses the critical need for precise thermal modeling in electronics, where temperature significantly impacts system reliability. We emphasize the necessity of accurate temperature measurement and uncertainty quantification in thermal imaging, a vital tool across multiple industries. Current mathematical models and uncertainty measures, such as Rényi and Shannon entropies, are inadequate for the detailed informational content required in thermal images. Our work introduces a novel entropy that effectively captures the informational content of thermal images by combining local and global data, surpassing existing metrics. Validated by rigorous experimentation, this method enhances thermal images’ reliability and information preservation. We also present two enhancement frameworks that integrate an optimized genetic algorithm and image fusion techniques, improving image quality by reducing artifacts and enhancing contrast. These advancements offer significant contributions to thermal imaging and uncertainty quantification, with broad applications in various sectors.

CHRONIC KIDNEY DISEASE PREDICTION USING MACHINE LEARNING

The term ―chronic kidney disease‖ means lasting damage to the kidneys that can get worse over time. If the damage is very bad, your kidneys may stop working. This is called kidney failure, or end-stage Kidney disease (ESRD). Kidney disease patients have the potential to get into the chronic phase and chronic kidney disease (CKD) is a decrease in kidney function gradually. So, doctors can diagnose kidney disease patients. So, predicting whether patients with Kidney disease have entered a phase of chronic kidney disease or not by showing the best accuracy result of comparing supervised classification machine learning algorithms. The aim is to investigate machine learning- based techniques for CKD forecasting by predicting results in the best accuracy. The analysis of the dataset by supervised machine learning technique (SMLT) to capture several information like, variable identification, univariate analysis, bi-variate and multi-variate analysis, missing value treatments and analyze the data validation, data cleaning/preparation, and data visualization will be done on the entire given dataset. Additionally, to compare and discuss the performance of various machine learning algorithms from the given hospital dataset with an evaluation classification report, identify the confusion matrix and to categorizing data from priority and the result shows that the effectiveness of the proposed machine learning algorithm technique can be compared with the best accuracy with precision, Recall and F1 Score. Key Words: Chronic kidney, Damage, Univariate, bi-variate, Multi-variate.

Foundation models are platform models: Prompting and the political economy of AI

A recent innovation in the field of machine learning has been the creation of very large pre-trained models, also referred to as ‘foundation models’, that draw on much larger and broader sets of data than typical deep learning systems and can be applied to a wide variety of tasks. Underpinning text-based systems such as OpenAI's ChatGPT and image generators such as Midjourney, these models have received extraordinary amounts of public attention, in part due to their reliance on prompting as the main technique to direct and apply them. This paper thus uses prompting as an entry point into the critical study of foundation models and their implications. The paper proceeds as follows: In the first section, we introduce foundation models in more detail, outline some of the main critiques, and present our general approach. We then discuss prompting as an algorithmic technique, show how it makes foundation models programmable, and explain how it enables different audiences to use these models as (computational) platforms. In the third section, we link the material properties of the technologies under scrutiny to questions of political economy, discussing, in turn, deep user interactions, reordered cost structures, and centralization and lock-in. We conclude by arguing that foundation models and prompting further strengthen Big Tech's dominance over the field of computing and, through their broad applicability, many other economic sectors, challenging our capacities for critical appraisal and regulatory response.

AOSMA-MLP: A Novel Method for Hybrid Metaheuristics Artificial Neural Networks and a New Approach for Prediction of Geothermal Reservoir Temperature

To ascertain the optimal and most efficient reservoir temperature of a geothermal source, long-term field studies and analyses utilizing specialized devices are essential. Although these requirements increase project costs and induce delays, utilizing machine learning techniques based on hydrogeochemical data can minimize losses by accurately predicting reservoir temperatures. In recent years, applying hybrid methods to real-world challenges has become increasingly prevalent over traditional machine learning methodologies. This study introduces a novel machine learning approach, named AOSMA-MLP, integrating the adaptive opposition slime mould algorithm (AOSMA) and multilayer perceptron (MLP) techniques, specifically designed for predicting the reservoir temperature of geothermal resources. Additionally, this work compares the basic artificial neural network and widely recognized algorithms in the literature, such as the whale optimization algorithm, ant lion algorithm, and SMA, under equal conditions using various evaluation regression metrics. The results demonstrated that AOSMA-MLP outperforms basic MLP and other metaheuristic-based MLPs, with the AOSMA-trained MLP achieving the highest performance, indicated by an R2 value of 0.8514. The proposed AOSMA-MLP approach shows significant potential for yielding effective outcomes in various regression problems.

Reconfiguration of distribution network-based wind energy resource allocation considering time-varying load using hybrid optimization method

This study proposed a method for optimizing a radial distribution network by integrating wind turbine allocation, considering fluctuating load demands, through the use of a hybrid Grey Wolf Optimizer-Genetic Algorithm (HGWOGA). This approach aims to decrease the network’s energy loss costs. By incorporating genetic algorithm techniques, the method enhances the Grey Wolf Optimizer’s efficiency, speeding up convergence and avoiding local optima. The strategy determines the network’s open lines and the placement and capacity of wind turbines, adhering to radiality and operational constraints. It categorizes load levels into residential, commercial, and industrial, providing a comprehensive analysis of energy losses and their cost implications under various scenarios, including constant and dynamic loads. The study suggests that managing time-varying demand offers a more accurate depiction of network challenges, enabling effective reconfiguration throughout different demand phases. Moreover, HGWOGA demonstrates its ability to find the global optimum efficiently, even with reduced population sizes—a feat not achievable with the Grey Wolf Optimizer alone. Comparative analyses reveal HGWOGA’s effectiveness in curbing network energy loss costs better than previous methodologies. By simultaneously applying network reconfiguration and wind turbine allocation, as opposed to merely reconfiguring the network, this approach notably reduces power loss, diminishes the cost of losses, and enhances the voltage profile. This synergistic strategy leverages the dynamic allocation of wind turbines within the network, optimizing energy flow and distribution efficiency, thereby offering a substantial improvement over conventional network reconfiguration methods.

Optimization of wear parameters for ECAP-processed ZK30 alloy using response surface and machine learning approaches: a comparative study

The present research applies different statistical analysis and machine learning (ML) approaches to predict and optimize the processing parameters on the wear behavior of ZK30 alloy processed through equal channel angular pressing (ECAP) technique. Firstly, The ECAPed ZK30 billets have been examined at as-annealed (AA), 1-pass, and 4-passes of route Bc (4Bc). Then, the wear output responses in terms of volume loss (VL) and coefficient of friction (COF) have been experimentally investigated by varying load pressure (P) and speed (V) using design of experiments (DOE). In the second step, statistical analysis of variance (ANOVA), 3D response surface plots, and ML have been employed to predict the output responses. Subsequently, genetic algorithm (GA), hybrid DOE–GA, and multi-objective genetic algorithm techniques have been used to optimize the input variables. The experimental results of ECAP process reveal a significant reduction in the average grain size by 92.7% as it processed through 4Bc compared to AA counterpart. Furthermore, 4Bc exhibited a significant improvement in the VL by 99.8% compared to AA counterpart. Both regression and ML prediction models establish a significant correlation between the projected and the actual data, indicating that the experimental and predicted values agreed exceptionally well. The minimal VL at different ECAP passes was obtained at the highest condition of the wear test. Also, the minimal COF for all ECAP passes was obtained at maximum wear load. However, the optimal speed in the wear process decreased with the number of billets passes for minimum COF. The validation of predicted ML models and VL regression under different wear conditions have an accuracy range of 70–99.7%, respectively.

Resource-constrained project scheduling with multiple states: Bi-objective optimization model and case study of aircraft maintenance

This paper introduces a new resource-constrained project scheduling problem in which the project has multiple possible states and whether an activity can be performed at a certain time depends on the state at that time. We are motivated by aircraft maintenance projects where activities need to be performed in particular aircraft states, such as power on/off and jack on/off. We formulate a bi-objective optimization model for this problem, which determines the schedule of activities and the selection of states over the planning horizon, subject to resource constraints, precedence relationship, and state restrictions. The model concerns both the minimization of the project duration and the minimization of the number of state changes over time. A heuristic algorithm, which utilizes genetic algorithm techniques, is developed to obtain Pareto efficient solutions of the model. Its effectiveness is demonstrated through a comparison with CPLEX and an adaptation of NSGA-II. Furthermore, we conduct a case study about aircraft maintenance C-check projects in a major airline. The case study demonstrates that our optimization-based approach is applicable and beneficial to the project scheduling practice with the consideration of states.

Linear Parameter Varying Observer-Based Adaptive Dynamic Surface Sliding Mode Control for PMSM

This paper presents an adaptive dynamic surface sliding mode control technique to address the issue of system parameter changes in permanent magnet synchronous motor (PMSM) position servo systems. The proposed method involves adopting a linear parameter varying (LPV) observer-based parameter identification algorithm and adaptive control technique. Initially, a mathematical model of the PMSM is established, and the system parameters are divided into nominal and perturbation values. This allows for the reconstruction of the system model into a state space equation that incorporates the unknown perturbation parameters. To accurately estimate these unknown parameters, an LPV observer is designed based on the reconstructed model. Additionally, an adaptive dynamic surface sliding mode control technique is explored to achieve the desired tracking performance. Meanwhile, an exponential reaching law is introduced to expedite the dynamic behavior of the system and mitigate chattering. Finally, a suitable Lyapunov function is selected to ensure the overall stability of the system. The simulation results demonstrate the effectiveness of the parameter identification and control algorithm in achieving good identification and tracking control ability for PMSM systems.

Prediction of acute toxicity for Chlorella vulgaris caused by tire wear particle-derived compounds using quantitative structure-activity relationship models

Tire wear particles (TWPs) enter aquatic ecosystems through various pathways, such as rainwater and urban runoff. Additives in TWPs can harm aquatic organisms in these ecosystems. Therefore, it is essential to investigate their toxicity to aquatic organisms. In our study, we initially recorded the median effective concentrations of 21 TWP-derived compounds on Chlorella vulgaris growth, ranging from 0.04 to 8.60 mg/L. Subsequently, through an extensive review of the literature, we incorporated 112 compounds with specific toxicity endpoints to construct the QSAR model using genetic algorithm and multiple linear regression techniques, followed by the construction of the consensus model and the quantitative read-across structure-activity relationship (q-RASAR) model. Meanwhile, we employed rigorous internal and external validation measures to assess the performance of the model. The results indicated that the developed q-RASAR model exhibited strong adaptation, robustness, and reliable prediction, with q-RASAR indicators of Q2LOO = 0.7673, R2tr = 0.8079, R2test = 0.8610, Q2Fn = 0.8285−0.8614, and CCCtest = 0.9222. Based on an external dataset containing 128 emerging TWP-derived compounds, the model's applicability domain coverage was 90.6 %. The q-RASAR model predicted that the structure of diphenylamine was associated with higher toxicity, possibly liked to the SpMax2_Bhm and LogBCF descriptors. The established model reliably provides prediction and fills a critical data gap. These findings highlight the potential risks posed by emerging TWP-derived compounds to aquatic organisms.