Powerful Machine Research Articles

Analysis of Land-Use/Cover-Type Extraction Results of Tamarix Shrub Forest of China Based on Remote Sensing Technology

The endmember spectrum method can improve image classification quality based on the spectral features of pure pixels in remote sensing images. The CART (Classification and Regression Tree) is a powerful machine learning algorithm that can also be used for remote sensing image classification. In this study, the Tamarix chinensis forest in the Changyi National Marine Ecological Special Reserve in Shandong Province was taken as the research object, and the endmember spectrum method and the CART decision tree method were used to compare and analyze the results of land-use/cover-type classification extraction. In the extraction process, the land use/cover types of the Tamarix forest in the study area were first divided into forested land types such as high-density forest land, medium-density forest land, and low-density forest land, as well as non-forested land types such as water bodies, roads, dams, buildings, and bare soil. Through analysis, the following conclusions could be drawn: while the overall forest cover of the Tamarix forest is high, there is still some room for further afforestation and ecological restoration in the protection area; from the results of land-use/cover extraction results based on the endmember spectrum method in the study area, it can be seen that this method has better results when extracting well-grown forested land, such as high-density Tamarix chinensis forests and medium-density Tamarix chinensis forests, and poorer results when extracting non-forested land, such as low-density tamarisk forests, roads, buildings, dams, and water bodies; from the results of land use/cover extraction based on a CART decision tree in the study area, it can be seen that this method is more effective when extracting non-forested land, such as roads, buildings, dams, and water bodies, but less effective when extracting forested land, such as high-density Tamarix chinensis forests, medium-density Tamarix chinensis forests, and low-density Tamarix chinensis forests. The relevant research results and conclusions of this study can provide some reference for the classification and extraction of large-scale shrub forest cover types based on remote sensing images.

Smart Solutions for Sustainable Agriculture: Design and Development of IoT-Powered Soil Health Identification Systems

This research examines smart solutions for agricultural sustainability using IoT-powered soil health detection systems. Innovative solutions are needed to manage resources and protect the environment as the world population grows and agricultural resources are under strain. IoT-enabled soil health monitoring has great potential. The suggested system uses IoT sensors to measure soil moisture, pH, nutrient content, and temperature in real time. This data is analyzed using powerful data analytics and machine learning algorithms to provide farmers soil health information. Monitoring soil quality allows farmers to make educated irrigation, fertilization, and crop selection choices, optimizing resource use and yields while minimizing environmental effect. The study also examines sensor selection, data transmission methods, and data security for IoT-powered soil health detection systems. Widespread use of these technologies might boost agricultural output, lower input costs, and enhance food security. Finally, IoT-powered soil health detection systems are a major step toward agricultural sustainability. These devices enable precision farming that is ecologically friendly and profitable by giving real-time soil data. However, effective implementation would need agricultural value chain stakeholders to collaborate and continued research to address growing issues and possibilities.

Maize migration mitigates the negative impact of climate change on China’s maize yield

Crop migration as an adaptation to modulate climate change’s impact on crop yields presents both benefits and risks. We explored how maize migration in China modulates yield responses to climate change and quantified the potential economic benefits of maize migration as an adaptation strategy. We employed a panel data model to identify and measure the factors driving the relocation of maize area, linear regression to quantify the effects of maize migration on climate exposure and irrigated area, and an econometric model to estimate the effects of maize migration on yield. The results show that rise in temperature has a significant negative effect on maize area and that precipitation has a significant positive effect. The migration of maize area is driven by socio-economic factors including agricultural gross domestic product, power of farming machines, and fertilizer input. Moreover, expanded irrigation reduces the adverse effects of high temperatures on maize yield, thereby influencing adaptive crop migrations. The beneficial effects of maize migration are primarily achieved by reducing the adverse effects of extreme heat and strengthening the positive effects of irrigation. However, the extent of this adaptation is jointly affected by agricultural policies, irrigation infrastructure, and economic factors. Current market-oriented agricultural policies may be effective in guiding spatial shifts in maize distribution to align with climate-driven changes, potentially decreasing the vulnerability of China’s maize yield to the impact of climate change. China’s food security policies need to consider climate-driven spatial shifts in crop cultivation and enhance food subsidy policies to highlight the benefits of investment in climate change adaptation, such as adjusting cropping acreage and irrigation to farmers in North China.

Towards intelligent emergency control for large-scale power systems: Convergence of learning, physics, computing and control

This paper has delved into the pressing need for intelligent emergency control in large-scale power systems, which are experiencing significant transformations and are operating closer to their limits with more uncertainties. Learning-based control methods are promising and have shown effectiveness for intelligent power system control. However, when they are applied to large-scale power systems, there are multifaceted challenges such as scalability, adaptiveness, and security posed by the complex power system landscape, which demand comprehensive solutions. The paper first proposes and instantiates a convergence framework for integrating power systems physics, machine learning, advanced computing, and grid control to realize intelligent grid control at a large scale. Our developed methods and platform based on the convergence framework have been applied to a large (more than 3000 buses) Texas power system, and tested with 56000 scenarios. Our work achieved a 26% reduction in load shedding on average and outperformed existing rule-based control in 99.7% of the test scenarios. The results demonstrated the potential of the proposed convergence framework and DRL-based intelligent control for the future grid.

INFORMATION SYSTEM FOR DATA PROCESSING IN SPORTS USING THE RANDOM FOREST METHOD

A huge amount of data is collected and generated in modern sports. This data can be used to improve athletes' performance, make more informed coaching and strategic decisions, and increase fan engagement. However, processing, analyzing, and interpreting this data can be challenging. This article is devoted to the development of an information system for data processing in the sports sector using the random forest method. The system aims to ensure efficient collection, processing, and analysis of large amounts of data generated during sports competitions, training, and interaction with fans and other stakeholders. Research methods. This article proposes an information system (IS) for data processing in the sports industry using the Random Forest (RF) method. As one of the machine learning methods, it is well suited for working with large amounts of data and complex classification and prediction tasks. The proposed IS consists of three main components. The data collection module accumulates data from various sources such as sensors, GPS trackers, websites, and social networks. The data processing module cleans, normalizes, and transforms the data to prepare it for analysis. The data analysis module uses the RF method to analyze data, predict outcomes, identify patterns, and make decisions. The conducted research has shown that the proposed IS can be an effective tool for predicting the results of sports competitions with high accuracy, identifying patterns in the data that can be useful for coaches and athletes to improve their training and strategy, personalizing training programs and recommendations for athletes, increasing the level of fan engagement by providing them with personalized content and forecasts. The proposed IS based on the random forest method is a powerful tool for processing and analyzing data in the sports industry. Its use can lead to improved athletes' performance, more informed coaching and strategic decisions, and increased fan engagement. One of the most powerful and accurate machine learning methods, the random forest method, allows for reliable analysis and forecasting based on various types of data, including player statistics, match results, physiological indicators, and fan behavior data. The article describes the stages of creating an information system: from data collection to data processing, storage, and analysis.

ИССЛЕДОВАНИЕ ТЕХНИЧЕСКИХ РЕШЕНИЙ ПО РЕАЛИЗАЦИИ НАГРУЗОЧНОГО МОМЕНТА ПРИ ИСПЫТАНИИ ЭЛЕКТРИЧЕСКИХ МАШИН

The main methods of testing electrical machines are considered. It has been determined that the tradi-tional method of implementing the load moment has significant limitations. It is proposed to use an electromagnetic brake based on a hysteresis machine. This method of organizing the load torque is universal and can be used to test machines of different power and rotation speed

An improved method for diagnosis of Parkinson’s disease using deep learning models enhanced with metaheuristic algorithm

Parkinson's disease (PD) is challenging for clinicians to accurately diagnose in the early stages. Quantitative measures of brain health can be obtained safely and non-invasively using medical imaging techniques like magnetic resonance imaging (MRI) and single photon emission computed tomography (SPECT). For accurate diagnosis of PD, powerful machine learning and deep learning models as well as the effectiveness of medical imaging tools for assessing neurological health are required. This study proposes four deep learning models with a hybrid model for the early detection of PD. For the simulation study, two standard datasets are chosen. Further to improve the performance of the models, grey wolf optimization (GWO) is used to automatically fine-tune the hyperparameters of the models. The GWO-VGG16, GWO-DenseNet, GWO-DenseNet + LSTM, GWO-InceptionV3 and GWO-VGG16 + InceptionV3 are applied to the T1,T2-weighted and SPECT DaTscan datasets. All the models performed well and obtained near or above 99% accuracy. The highest accuracy of 99.94% and AUC of 99.99% is achieved by the hybrid model (GWO-VGG16 + InceptionV3) for T1,T2-weighted dataset and 100% accuracy and 99.92% AUC is recorded for GWO-VGG16 + InceptionV3 models using SPECT DaTscan dataset.

On-device Online Learning and Semantic Management of TinyML Systems

Recent advances in Tiny Machine Learning (TinyML) empower low-footprint embedded devices for real-time on-device Machine Learning (ML). While many acknowledge the potential benefits of TinyML, its practical implementation presents unique challenges. This study aims to bridge the gap between prototyping single TinyML models and developing reliable TinyML systems in production: (1) Embedded devices operate in dynamically changing conditions. Existing TinyML solutions primarily focus on inference, with models trained offline on powerful machines and deployed as static objects. However, static models may underperform in the real world due to evolving input data distributions. We propose online learning to enable training on constrained devices, adapting local models toward the latest field conditions. (2) Nevertheless, current on-device learning methods struggle with heterogeneous deployment conditions and the scarcity of labeled data when applied across numerous devices. We introduce federated meta-learning incorporating online learning to enhance model generalization, facilitating rapid learning. This approach ensures optimal performance among distributed devices by knowledge sharing. (3) Moreover, TinyML’s pivotal advantage is widespread adoption. Embedded devices and TinyML models prioritize extreme efficiency, leading to diverse characteristics ranging from memory and sensors to model architectures. Given their diversity and non-standardized representations, managing these resources becomes challenging as TinyML systems scale up. We present semantic management for the joint management of models and devices at scale. We demonstrate our methods through a basic regression example and then assess them in three real-world TinyML applications: handwritten character image classification, keyword audio classification, and smart building presence detection. The results confirm the effectiveness of our approaches from various perspectives, such as accuracy improvement, resource savings, and engineering effort reduction.

Performance analysis of Bayesian optimised gradient-boosted decision trees for digital elevation model (DEM) error correction: interim results

Abstract. Gradient-Boosted Decision Trees (GBDTs), particularly when tuned with Bayesian optimisation, are powerful machine learning techniques known for their effectiveness in handling complex, non-linear data. However, the performance of these models can be significantly influenced by the characteristics of the terrain being analysed. In this study, we assess the performance of three Bayesian-optimised GBDTs (XGBoost, LightGBM and CatBoost) using digital elevation model (DEM) error correction as a case study. The performance of the models is investigated across five landscapes in Cape Town South Africa: urban/industrial, agricultural, mountain, peninsula and grassland/shrubland. The models were trained using a selection of datasets (elevation, terrain parameters and land cover). The comparison entailed an analysis of the model execution times, regression error metrics, and level of improvement in the corrected DEMs. Generally, the optimised models performed considerably well and demonstrated excellent predictive capability. CatBoost emerged with the best results in the level of improvement recorded in the corrected DEMs, while LightGBM was the fastest of all models in the execution time for Bayesian optimisation and model training. These findings offer valuable insights for applying machine learning and hyperparameter tuning in remote sensing.

Unlocking Multilingual Communication: Exploring the Potential of a Chat Application

Language barriers limit cross-cultural communication and collaboration in an increasingly interconnected world. Innovative multilingual chat apps use cutting-edge technologies to bridge language gaps and stimulate meaningful connections. This abstract describes a complicated multilingual chat application leveraging the MERN stack and AWS Translation services to revolutionize digital cross-cultural communication. Real-time translation in multilingual chat apps solves the problems. These algorithms recognize cross-linguistic interactions to provide inclusive digital environments where individuals of different languages can converse. Multilingual chat apps need MERN. React, Node.js, MongoDB, and Express.js provide a complete real-time communications platform. MongoDB scales, while Express.js and Node.js optimize server-side tasks. Multilingual interfaces are responsive to React’s declarative components. The final section addresses AWS Translation and multilingual chat app integration. Neural machine translation specialist AWS Translation delivers real-time translation across several languages. AWS Translation enables multilingual communication with precise, contextually relevant translations using powerful machine learning techniques. Finally, the abstract discusses how multilingual chat apps will change global communication. These apps foster cross-cultural understanding and worldwide cooperation. Developers aspire to create inclusive digital environments that celebrate language diversity and promote cross-cultural engagement utilizing technology and innovation.

Diabetes Prediction System Using SVM Alogrithm

Diabetes Mellitus is a metabolic disease caused by high blood sugar, which can lead to serious health problems if not properly controlled. Early prediction and timely intervention are crucial for preventing and managing diabetes. This paper presents a Diabetic Prediction System utilizing the Support Vector Machine (SVM) algorithm, a powerful machine learning technique known for its effectiveness in classification tasks. The proposed system lever- ages a dataset comprising relevant features such as age, body mass index (BMI), family history, and blood pressure to train the SVM model. Data were preprocessed to control for missing values, normalize features, and reduce bias. The SVM algorithm is employed for classification, as it excels in handling high-dimensional data and is capable of finding optimal hyperplanes to separate different classes. The system undergoes a comprehensive evaluation using performance metrics such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve. The results demonstrate the efficacy of the SVM algorithm in accurately predicting the likelihood of diabetes based on the input features.

FAKE PROFILE DETECTION ON SOCIAL NETWORKING WEBSITES USING MACHINE LEARNING

In an age where social media has become an integral part of our lives, the challenge of detecting fake accounts on platforms like Instagram has gained significant importance. This project, titled "Instagram Fake Account Detection using Machine Learning," employs Python as its primary tool to tackle this problem. It leverages two powerful machine learning algorithms, the Random Forest Classifier and the Decision Tree Classifier, to accomplish this task. The Random Forest Classifier demonstrates remarkable performance, achieving a 100% accuracy on the training dataset and an impressive 93% accuracy on the test dataset. Meanwhile, the Decision Tree Classifier exhibits its effectiveness with a training accuracy of 92% and a test accuracy of 92%. The dataset employed in this project is composed of 576 records, each characterized by 12 distinct features. These features encompass critical aspects of Instagram profiles, including the presence of a profile picture, the ratio of numerical characters in usernames, the breakdown of full names into word tokens, the ratio of numerical characters in full names, the equality between usernames and full names, the length of user bios, the existence of external URLs, the privacy status of accounts, the number of posts, the count of followers, the number of accounts followed, and the ultimate classification of an account as "Fake" or "Not." By harnessing the capabilities of Python and these advanced machine learning models, this project endeavors to provide a robust and efficient solution for the identification of fake Instagram accounts. In doing so, it contributes to the preservation of the platform's integrity and the security of its users.

Fake Profile Detection on Social Networking Websites Using Machine Learning

In an age where social media has become an integral part of our lives, the challenge of detecting fake accounts on platforms like Instagram has gained significant importance. This project, titled "Instagram Fake Account Detection using Machine Learning," employs Python as its primary tool to tackle this problem. It leverages two powerful machine learning algorithms, the Random Forest Classifier and the Decision Tree Classifier, to accomplish this task. The Random Forest Classifier demonstrates remarkable performance, achieving a 100% accuracy on the training dataset and an impressive 93% accuracy on the test dataset. Meanwhile, the Decision Tree Classifier exhibits its effectiveness with a training accuracy of 92% and a test accuracy of 92%. The dataset employed in this project is composed of 576 records, each characterized by 12 distinct features. These features encompass critical aspects of Instagram profiles, including the presence of a profile picture, the ratio of numerical characters in usernames, the breakdown of full names into word tokens, the ratio of numerical characters in full names, the equality between usernames and full names, the length of user bios, the existence of external URLs, the privacy status of accounts, the number of posts, the count of followers, the number of accounts followed, and the ultimate classification of an account as "Fake" or "Not." By harnessing the capabilities of Python and these advanced machine learning models, this project endeavors to provide a robust and efficient solution for the identification of fake Instagram accounts. In doing so, it contributes to the preservation of the platform's integrity and the security of its users.

Accurate ab initio calculation and neural network prediction of the atomic properties of Os LXXIII, Ir LXXIV, Pt LXXV, and Au LXXVI

In this paper, we present an ab initio theoretical calculation of the atomic parameters, including energy levels, wavelengths, lifetimes, and transition parameters, associated with the 1s22snl and 1s22pnl (n= 2–4, l≤n−1) configurations of Be-like sequence: Os LXXIII, Ir LXXIV, Pt LXXV, and Au LXXVI. Our analysis is based on the relativistic wavefunctions derived from the multi-configuration Dirac–Fock (MCDF) method, which are implemented in the relativistic atomic structure package GRASP2018. The correlations within the (principal quantum number) n= 10 complex are accounted for. The Breit interaction and quantum electrodynamical effects are added in the relativistic configuration interaction (RCI) calculation. Our results are compared with the existing data. The uncertainties of the dipole transition line strengths are assessed. In addition, we propose a feed forward neural network to predict the energy levels of Au LXXVI. This is a powerful machine learning tool capable of learning from existing data and predicting unknown data. The network is trained using theoretical energy levels of Os LXXIII, Ir LXXIV, and Pt LXXV obtained from the MCDF method. Our neural network exhibits average relative deviations of approximately 0.01% and 0.02% for trained and predicted energy levels, respectively, when compared to the MCDF results. This level of deviation suggests that our results are reasonably accurate. The data set presented in this study is useful for modeling fusion plasmas.

Bibliometric Analysis and Visualization of Scientific Literature on Heart Disease Classification Using a Logistic Regression Model.

With the advancement in artificial intelligence, the use of machine learning algorithms for clinical prediction has increased tremendously. Logistic regression is one of the powerful machine learning algorithms that can be used to predict the probability of a variable. Logistic regression is very popular among medical researchers owing to its simplicity, interpretability, and solid statistical foundation. This study aims to investigate the research productivity of heart disease classification using a logistic regression model to analyze the current patterns and potential future trends through bibliometric analysis. Additionally, it aims to highlight the impact and quality of research in the area, identify prominent research groups,the countries actively contributing to the field, which will help the researchers andhealthcare professionals to pinpoint research gaps, influential authors, and make informed decisions and invest resources accordingly. The data is collected from a database of Scopus spanning from 2019 to 2023. We have used two bibliometric software, Biblioshiny(Aria and Cuccurullo, 2017) and VOSviewer (Centre for Science and Technology Studies (CWTS), Leiden University,the Netherlands), to analyze the bibliographic data regarding the citation count, contribution of authors, publication count, the contribution of institutions, etc.There are 2331 documents under study which were fed into both software to analyze the data. With 700 documents, China topped the list of most productive countries indicating the vast contribution of the country followed by India and the United States. Contributions of the Harvard Medical School, Boston, MA, United States are found to be the greatest with six papers. The most productive author is Wang Y with 73 documents. Analysis of trending topics reveals that the field progressing towards using support vector machines (SVM), k-nearest neighbours (KNN), and naïveBayes algorithms. The article has only considered data from Scopus excluding literature indexed in other databases which limits the potential coverage of the data. Also, the work focuses on recent developments excluding older literature from 2019 which could be a limitation. Furthermore, since the study is a bibliometric analysis targeting the use of logistic regression for heart disease prediction, powerfultechniques such as SVM, decision trees, random forests, neural networksand deep learning have not been included, which could be another limitation.

Hybridizing traditional and next-generation reservoir computing to accurately and efficiently forecast dynamical systems.

Reservoir computers (RCs) are powerful machine learning architectures for time series prediction. Recently, next generation reservoir computers (NGRCs) have been introduced, offering distinct advantages over RCs, such as reduced computational expense and lower training data requirements. However, NGRCs have their own practical difficulties, including sensitivity to sampling time and type of nonlinearities in the data. Here, we introduce a hybrid RC-NGRC approach for time series forecasting of dynamical systems. We show that our hybrid approach can produce accurate short-term predictions and capture the long-term statistics of chaotic dynamical systems in situations where the RC and NGRC components alone are insufficient, e.g., due to constraints from limited computational resources, sub-optimal hyperparameters, sparsely sampled training data, etc. Under these conditions, we show for multiple model chaotic systems that the hybrid RC-NGRC method with a small reservoir can achieve prediction performance approaching that of a traditional RC with a much larger reservoir, illustrating that the hybrid approach can offer significant gains in computational efficiency over traditional RCs while simultaneously addressing some of the limitations of NGRCs. Our results suggest that the hybrid RC-NGRC approach may be particularly beneficial in cases when computational efficiency is a high priority and an NGRC alone is not adequate.

Predicting non-specific myocardial fibrosis in clinical setting in a large cohort of young and veteran athletes using a powerful machine learning model

Predicting non-specific myocardial fibrosis in clinical setting in a large cohort of young and veteran athletes using a powerful machine learning model

Performance Analysis: Object Detection System for Low Illumination

Abstract: Abnormal activity detection plays a very important role in surveillance applications. The existing research on surveillance for daytime has achieved better performance by detecting and tracking objects using deep learning algorithms. However, it is difficult to achieve the same performance for night vision mainly due to low illumination. Deep learning is a powerful machine learning technique in which the object detector automatically learns image features required for detection tasks. It is required to generate a model that detects objects under low illumination. The approach is to use thermal infrared images and detect external objects, if any, and classify whether it is human or animal in an isolation area. With the rapid growth of deep learning, more efficient techniques will be implemented to solve the problems of object detection using neural networks and deep learning

Machine Learning Enabled Inventory Prediction

Effective inventory management is a critical aspect of business operations, ensuring optimal stock levels, minimizing costs, and meeting customer demand. Accurate inventory prediction plays a pivotal role in achieving these objectives. This project explores the application of the XGBoost algorithm, a powerful machine learning technique, for inventory prediction. XGBoost's ability to handle complex nonlinear relationships and its robust performance make it a promising approach for this task. The project aims to develop an inventory prediction system using the XGBoost algorithm, leveraging historical sales data and other relevant factors to forecast future inventory levels. This project demonstrates the potential of machine learning, specifically the XGBoost algorithm, to revolutionize inventory management practices, enabling businesses to achieve greater efficiency and profitability. Keyword : Machine Learning, Non-linear Relations, Cost Minimization, Stock levels, Profitability, Inventory Prediction.

Task Scheduling with Dynamic Load Balancing for Cloud Computing

Using powerful cryptographic techniques and complex machine learning algorithms, this study explores an enhanced way for dynamic job scheduling, data recovery, and workflow management in cloud computing. To improve data recovery procedures and optimize job scheduling, we investigate the combination of the Random Forest, Q-learning from artificial neural networks (ANNs), and the J48 decision tree algorithms. To ensure data redundancy and integrity, we also use the Kochi matrix for effective data chunk generation and backup. The Advanced Encryption Standard (AES) is used to protect data. Our all-encompassing approach is to increase the effectiveness of resource allocation, hasten data recovery, and offer strong security in cloud environments. The efficiency of the suggested methodologies in improving overall system performance, security, and reliability is highlighted by the experimental findings, which show notable advancements over conventional approaches. Key Words: Load Balancing, Machine Learning, ANN, Random Forest, AES, Q-learning