Advanced Machine Learning Algorithms Research Articles

Monitoring the Workflow of Earthquake by Using Machine Learning

This study explores the innovative application of machine learning techniques in monitoring and analyzing the workflow associated with earthquakes. The primary objective is to enhance the accuracy and efficiency of earthquake detection, prediction, and response mechanisms the development of machine learning technology enables more robust real-time earthquake monitoring through automated implementations. However, the application of machine learning to earthquake location problems faces challenges in regions with limited available training data. This paper proposes a machine learning-based approach to monitor seismic workflows efficiently. We apply various Machine Learning algorithms, including deep learning neural networks and decision trees, to process and interpret vast amounts of seismic data. These algorithms were trained on historical earthquake records to identify patterns and anomalies that precede seismic events. Our approach not only focused on the detection of seismic activities but also on the prediction of potential aftershock locations and magnitudes. Earthquake monitoring and response systems play a crucial role in mitigating potential damages and ensuring rapid response to seismic events. Traditional methods rely on sensor networks and manual analysis, often leading to delays in detection and response. The system leverages real-time data from seismic sensors, incorporating advanced machine learning algorithms for data analysis and pattern recognition. By employing deep learning models, such as convolution neural networks (CNNs) and recurrent neural networks (RNNs), the system can detect and classify seismic activities accurately. Additionally, anomaly detection techniques enable the identification of unusual seismic patterns that might indicate impending earthquakes or aftershocks. The system integrates geographical information systems (GIS) to map the seismic activities spatially, aiding in the visualization and understanding of seismic patterns across regions. The machine learning models are trained on historical seismic data to enhance their predictive capabilities and adaptability to varying seismic behaviors. This proposed system offers a real-time monitoring solution that can assist in early earthquake detection, accurate event classification, and timely response coordination. Its ability to continuously learn from incoming data ensures adaptability to changing seismic behaviors, thereby improving overall efficiency and reliability in earthquake monitoring workflows.

Automated Model Selection Using Bayesian Optimization and the Asynchronous Successive Halving Algorithm for Predicting Daily Minimum and Maximum Temperatures

This study addresses the crucial role of temperature forecasting, particularly in agricultural contexts, where daily maximum (Tmax) and minimum (Tmin) temperatures significantly impact crop growth and irrigation planning. While machine learning (ML) models offer a promising avenue for temperature forecasts, the challenge lies in efficiently training multiple models and optimizing their parameters. This research addresses a research gap by proposing advanced ML algorithms for multi-step-ahead Tmax and Tmin forecasting across various weather stations in Bangladesh. The study employs Bayesian optimization and the asynchronous successive halving algorithm (ASHA) to automatically select top-performing ML models by tuning hyperparameters. While both the Bayesian and ASHA optimizations yield satisfactory results, ASHA requires less computational time for convergence. Notably, different top-performing models emerge for Tmax and Tmin across various forecast horizons. The evaluation metrics on the test dataset confirm higher accuracy, efficiency coefficients, and agreement indices, along with lower error values for both Tmax and Tmin forecasts at different weather stations. Notably, the forecasting accuracy decreases with longer horizons, emphasizing the superiority of one-step-ahead predictions. The automated model selection approach using Bayesian and ASHA optimization algorithms proves promising for enhancing the precision of multi-step-ahead temperature forecasting, with potential applications in diverse geographical locations.

Prognostic value of Geriatric Nutritional Risk Index and systemic immune-inflammatory index in elderly patients with acute coronary syndromes

The objective of this study was to evaluate the predictive value of the Geriatric Nutritional Risk Index (GNRI) combined with the Systemic Immunoinflammatory Index (SII) for the risk of major adverse cardiovascular events (MACE) following percutaneous coronary intervention in elderly patients with acute coronary syndrome (ACS). We retrospectively reviewed the medical records of 1202 elderly patients with acute coronary syndromes divided into MACE and non-MACE groups according to whether they had a MACE. The sensitivity analysis utilized advanced machine learning algorithms to preliminarily identify the critical role of GNRI versus SII in predicting MACE risk. We conducted a detailed analysis using a restricted cubic spline approach to investigate the nonlinear relationship between GNRI, SII, and MACE risk further. We constructed a clinical prediction model based on three key factors: GNRI, SII, and Age. To validate the accuracy and usefulness of this model, we compared it to the widely used GRACE score using subject work and recall curves. Additionally, we compared the predictive value of models and GRACE scores in assessing the risk of MACE using the Integrated Discriminant Improvement Index (IDI) and the Net Reclassification Index (NRI). This study included 827 patients. The GNRI scores were lower in the MACE group than in the non-MACE group, while the SII scores were higher in the MACE group (P < 0.001). The multifactorial analysis revealed a low GNRI (OR = 2.863, 95% CI: 2.026–4.047, P = 0.001), High SII (OR = 3.102, 95% CI: 2.213–4.348, P = 0.001). The area under the curve (AUC) for the predictive model was 0.778 (95% CI: 0.744–0.813, P = 0.001), while the AUC for the GRACE score was 0.744 (95% CI: 0.708–0.779, P = 0.001). NRI was calculated to be 0.5569, with NRI + at 0.1860 and NRI- at 0.3708. The IDI was found to be 0.0571, with a P-value of less than 0.001. These results suggest that the newly developed prediction model is more suitable for use with the population in this study than the GRACE score. The model constructed using GNRI and SII demonstrated good standardization and clinical impact, as evidenced by the standard, DCA, and clinical impact curves. The study shows that combining GNRI and SII can be a simple, cost-effective, and valuable way to predict the risk of MACE within one year in elderly acute coronary syndromes.

Proportional impact prediction model of coating material on nitrate leaching of slow-release Urea Super Granules (USG) using machine learning and RSM technique

An accurate assessment of nitrate leaching is important for efficient fertiliser utilisation and groundwater pollution reduction. However, past studies could not efficiently model nitrate leaching due to utilisation of conventional algorithms. To address the issue, the current research employed advanced machine learning algorithms, viz., Support Vector Machine, Artificial Neural Network, Random Forest, M5 Tree (M5P), Reduced Error Pruning Tree (REPTree) and Response Surface Methodology (RSM) to predict and optimize nitrate leaching. In this study, Urea Super Granules (USG) with three different coatings were used for the experiment in the soil columns, containing 1 kg soil with fertiliser placed in between. Statistical parameters, namely correlation coefficient, Mean Absolute Error, Willmott index, Root Mean Square Error and Nash–Sutcliffe efficiency were used to evaluate the performance of the ML techniques. In addition, a comparison was made in the test set among the machine learning models in which, RSM outperformed the rest of the models irrespective of coating type. Neem oil/ Acacia oil(ml): clay/sulfer (g): age (days) for minimum nitrate leaching was found to be 2.61: 1.67: 2.4 for coating of USG with bentonite clay and neem oil without heating, 2.18: 2: 1 for bentonite clay and neem oil with heating and 1.69: 1.64: 2.18 for coating USG with sulfer and acacia oil. The research would provide guidelines to researchers and policymakers to select the appropriate tool for precise prediction of nitrate leaching, which would optimise the yield and the benefit–cost ratio.

Unlocking the neural mechanisms of consumer loan evaluations: an fNIRS and ML-based consumer neuroscience study.

This study conducts a comprehensive exploration of the neurocognitive processes underlying consumer credit decision-making using cutting-edge techniques from neuroscience and machine learning (ML). Employing functional Near-Infrared Spectroscopy (fNIRS), the research examines the hemodynamic responses of participants while evaluating diverse credit offers. The experimental phase of this study investigates the hemodynamic responses collected from 39 healthy participants with respect to different loan offers. This study integrates fNIRS data with advanced ML algorithms, specifically Extreme Gradient Boosting, CatBoost, Extra Tree Classifier, and Light Gradient Boosted Machine, to predict participants' credit decisions based on prefrontal cortex (PFC) activation patterns. Findings reveal distinctive PFC regions correlating with credit behaviors, including the dorsolateral prefrontal cortex (dlPFC) associated with strategic decision-making, the orbitofrontal cortex (OFC) linked to emotional valuations, and the ventromedial prefrontal cortex (vmPFC) reflecting brand integration and reward processing. Notably, the right dorsomedial prefrontal cortex (dmPFC) and the right vmPFC contribute to positive credit preferences. This interdisciplinary approach bridges neuroscience, machine learning and finance, offering unprecedented insights into the neural mechanisms guiding financial choices regarding different loan offers. The study's predictive model holds promise for refining financial services and illuminating human financial behavior within the burgeoning field of neurofinance. The work exemplifies the potential of interdisciplinary research to enhance our understanding of human financial decision-making.

A non-invasive 25-Gene PLNM-Score urine test for detection of prostate cancer pelvic lymph node metastasis.

Prostate cancer patients with pelvic lymph node metastasis (PLNM) have poor prognosis. Based on EAU guidelines, patients with >5% risk of PLNM by nomograms often receive pelvic lymph node dissection (PLND) during prostatectomy. However, nomograms have limited accuracy, so large numbers of false positive patients receive unnecessary surgery with potentially serious side effects. It is important to accurately identify PLNM, yet current tests, including imaging tools are inaccurate. Therefore, we intended to develop a gene expression-based algorithm for detecting PLNM. An advanced random forest machine learning algorithm screening was conducted to develop a classifier for identifying PLNM using urine samples collected from a multi-center retrospective cohort (n = 413) as training set and validated in an independent multi-center prospective cohort (n = 243). Univariate and multivariate discriminant analyses were performed to measure the ability of the algorithm classifier to detect PLNM and compare it with the Memorial Sloan Kettering Cancer Center (MSKCC) nomogram score. An algorithm named 25 G PLNM-Score was developed and found to accurately distinguish PLNM and non-PLNM with AUC of 0.93 (95% CI: 0.85-1.01) and 0.93 (95% CI: 0.87-0.99) in the retrospective and prospective urine cohorts respectively. Kaplan-Meier plots showed large and significant difference in biochemical recurrence-free survival and distant metastasis-free survival in the patients stratified by the 25 G PLNM-Score (log rank P < 0.001 and P < 0.0001, respectively). It spared 96% and 80% of unnecessary PLND with only 0.51% and 1% of PLNM missing in the retrospective and prospective cohorts respectively. In contrast, the MSKCC score only spared 15% of PLND with 0% of PLNM missing. The novel 25 G PLNM-Score is the first highly accurate and non-invasive machine learning algorithm-based urine test to identify PLNM before PLND, with potential clinical benefits of avoiding unnecessary PLND and improving treatment decision-making.

Psychological Assessment of Autism Spectrum Disorders using Machine Learning

This research aims to advance the field of autism spectrum disorder (ASD) assessment by proposing a machine learning-based approach tailored for school and community settings. Leveraging a diverse dataset encompassing behavioral, physiological, and neuroimaging data, we apply advanced machine learning algorithms to develop predictive models for early ASD detection. Our study integrates expert interviews to validate the clinical utility of these models and explores ethical considerations surrounding data privacy and bias. Preliminary results show promising accuracy in ASD identification. This research contributes to a more accessible and objective ASD assessment, with implications for early intervention and inclusive support, particularly in educational and community contexts.

A hybrid combination of CNN Attention with optimized random forest with grey wolf optimizer to discriminate between Arabic hateful, abusive tweets

Arabic hateful speech recognition has long been a major area of focus in Natural Language Processing (NLP) research. In light of recent advancements in transformer models and deep learning, researchers are now turning to transfer learning techniques based on existing models such as BERT for Arabic hateful speech recognition. To detect Arabic hateful contexts, using advanced machine learning algorithms and NLP techniques is essential. These techniques can help to detect different forms of hateful contexts in Arabic by analyzing the text for lexical, semantic, and syntactic features. In this research, we proposed a new hybrid approach that combines deep and machine learning models to detect hateful and abusive content in Arabic. The proposed model consists of a combination of convolutional neural networks and attention layers that are trained to differentiate between normal, abusive, and hateful contexts in Arabic. In the first step, we used a pre-trained model to extract features from the hateful Arabic context. After that, we used an optimized random forest combined with particle swarm optimization and grey wolf optimizer to classify the extracted features. Finally, we evaluated the performance of the model to detect hateful Arabic contexts. To evaluate the proposed method we used 5846 and 6023 tweets with 3 categories of hateful, abusive, and normal Arabic contexts. The experimental result indicates 97.16% accuracy, 97.15% F1-score, 97.17% precision, and 97.13% sensitivity using CNN Attention + optimized random forest by the grey wolf optimizer on 5846 tweets. 97.83% accuracy, 97.83% F1-score, 97.84% precision, and 97.83% sensitivity have been reported CNN Attention + optimized random forest by the grey wolf optimizer on 6023 tweets.

Wildfire spreading prediction using multimodal data and deep neural network approach

Predicting wildfire spread behavior is an extremely important task for many countries. On a small scale, it is possible to ensure constant monitoring of the natural landscape through ground means. However, on the scale of large countries, this becomes practically impossible due to remote and vast forest territories. The most promising source of data in this case that can provide global monitoring is remote sensing data. Currently, the main challenge is the development of an effective pipeline that combines geospatial data collection and the application of advanced machine learning algorithms. Most approaches focus on short-term fire spreading prediction and utilize data from unmanned aerial vehicles (UAVs) for this purpose. In this study, we address the challenge of predicting fire spread on a large scale and consider a forecasting horizon ranging from 1 to 5 days. We train a neural network model based on the MA-Net architecture to predict wildfire spread based on environmental and climate data, taking into account spatial distribution features. Estimating the importance of features is another critical issue in fire behavior prediction, so we analyze their contribution to the model’s results. According to the experimental results, the most significant features are wind direction and land cover parameters. The F1-score for the predicted burned area varies from 0.64 to 0.68 depending on the day of prediction (from 1 to 5 days). The study was conducted in northern Russian regions and shows promise for further transfer and adaptation to other regions. This geospatial data-based artificial intelligence (AI) approach can be beneficial for supporting emergency systems and facilitating rapid decision-making.

Trust in ESG reporting: The intelligent Veri-Green solution for incentivized verification

In today's corporate environment, Environmental, Social, and Governance (ESG) reports crucially reflect an organization's commitment to sustainability, environmental preservation, and social responsibility. As corporations share these detailed reports, the responsibility to validate and assure adherence to respected ESG benchmarks critically lies with third-party assurance organizations. However, the essential verification process often encounters challenges related to authenticity, credibility, and fairness, underscoring the need for a new solution. The selection of verifiers is a crucial aspect of this process, as their expertise and impartiality directly impact the validity and trustworthiness of the verification. Consequently, “Veri-Green,” an innovative blockchain-based incentive mechanism, has been introduced to improve the ESG data verification process. Considering potential risks in verification systems, such as reputational damage due to oversight or inadvertent approval of inaccurate data, and data security risks involving the management of sensitive organizational information, the verifier selection process needs to be thoroughly considered and designed. Through the utilization of advanced machine learning algorithms, potential verification candidates are precisely identified, followed by the deployment of the Vickrey Clarke Groves (VCG) auction mechanism. This approach ensures the strategic selection of verifiers and cultivates an ecosystem marked by truthfulness, rationality, and computational efficiency throughout the ESG data verification process. In this framework, verifiers are not only encouraged but also properly incentivized, developing a more transparent and equitable verification process, thereby driving the ESG agenda towards a future defined by genuine, impactful corporate responsibility and sustainability.

Performance Analysis of Share Market by Using ML Technique

This research paper delves into the intricate world of stock market analysis, exploring the application of machine learning techniques to enhance performance evaluation. The study adopts multifaceted approach, combining historical stock market data, advanced machine learning algorithms, and rigorous back testing to derive insights into market dynamics. The findings aim to contribute to the growing body of literature on financial analytics, providing valuable implications for investors and financial professionals.

Monitoring the workflow of Earthquake by using Machine Learning

The main goal is to improve the accuracy and efficiency of earthquake detection, prediction and response through automation and, over time, to improve machine learning techniques for earthquake monitoring. However, the application of machine learning to earthquake location problems faces challenges in areas with limited data. This article presents an engine-based approach to monitor earthquake performance. We use a variety of Machine Learning algorithms, including deep learning neural networks and decision trees, for processing and interpret large amounts of seismic data. These algorithms are trained on historical earthquake records to identify patterns and anomalies prior to seismic events. Our approach focuses not only on detecting seismic events, but also on predicting the location and magnitude of earthquakes. Earthquake monitoring and response systems play an important role in reducing potential losses and providing rapid response to seismic events. Conventional methods rely on sensor networks and manual analysis, often with delays in detection and response. The system receives real-time data from seismic sensors, incorporating advanced machine learning algorithms for data analysis and pattern recognition. The system can detect and describe seismic events using deep learning models such as convolutional neural networks (CNN) and random neural networks (RNN).In addition, anomaly detection techniques allow the identification of unusual seismic patterns that may indicate impending earthquakes or earthquakes. The system integrates geographic information systems (GIS) to comprehensively map seismic activity, helping to visualize and understand seismic patterns in the region. Machine learning models are trained on historical seismic data to improve their predictive capabilities and adapt to different seismic behaviors. This proposed system provides a real-time monitoring solution that can assist in early earthquake detection, accurate event classification, and timely response coordination. The ability to continuously learn from incoming data enables adaptation to change in seismic activity, thereby increasing the efficiency and reliability of earthquake monitoring operations.

Harnessing IoT and machine learning for sustainable agriculture: Predictive crop yield modeling in smart farming

&lt;p&gt;The integration of Internet of Things (IoT) technology in agriculture has transformed traditional farming methods, enabling the emergence of smart agriculture systems. This research paper focuses on utilizing IoT and machine learning techniques to forecast crop yields based on climatic and soil conditions. The study utilizes a dataset sourced from Kaggle, which includes information on 22 distinct crops, such as Maize, Wheat, Mango, Watermelon, and others. The dataset encompasses crucial climatic factors like temperature, humidity, and rainfall, as well as essential soil conditions necessary for optimal crop growth. By employing advanced machine learning algorithms on this dataset, the objective is to develop accurate models capable of predicting crop yields. This, in turn, assists farmers in making well-informed decisions regarding crop management and optimizing agricultural productivity. The outcomes of this research have significant implications for the agricultural industry, providing valuable insights into crop yield estimation and supporting the implementation of sustainable farming practices.&lt;/p&gt;

An investigation of an athlete injury likelihood monitoring system using the random forest algorithm and DWT.

The main goal of sports science is to monitor sports injuries. Nevertheless, the existing sports injury monitoring projects have many expensive instruments and excessively extended monitoring periods, which makes it difficult to expand sports injury monitoring on a large scale. The advancement of machine learning algorithms opens up new avenues for the tracking of sports injuries. A training set of sports injuries was created using the Discrete Wavelet Transform (DWT) and Random Forest algorithms. Next, a basic analytic framework was created based on the lower-body movement of runners, and an athlete's injury likelihood monitoring system was established. First off, the wearable gyroscope device can efficiently plot the motion displacement curve and monitor the three-dimensional mechanics of the athlete's hips, thighs, and calves. Secondly, the system has a higher computational efficiency and an advantage over other classifier-based systems in terms of testing and training times. The suggested system framework identifies athletes' injury propensity, providing preventive recommendations based on displacement curves, and offering a low total cost and high testing accuracy, making it easy to implement and cost-effective. All things considered, the sports injury monitoring device is very accurate and reasonably priced, making it appropriate for widespread use.

Statistical Analysis and Accuracy Assessment of Improved Machine Learning Based Opinion Mining Framework

Sentiment analysis, also known as opinion mining, plays a crucial role in understanding and extracting valuable insights from textual data in various domains, including social media, customer feedback, and product reviews. This research presents an in-depth examination of an improved machine learning-based sentiment analysis framework, focusing on its statistical analysis and accuracy assessment. The research begins by introducing the framework's architecture, which incorporates advanced machine learning algorithms and natural language processing techniques. These enhancements aim to provide a more nuanced and context-aware sentiment analysis, addressing the limitations of traditional approaches. To evaluate the performance of the proposed framework, a comprehensive statistical analysis is conducted. Various statistical metrics, such as precision, recall, F1-score, and accuracy, are employed to assess its effectiveness in classifying text sentiments accurately. Additionally, the study explores the impact of different feature engineering and pre-processing techniques on model performance. The results of this study demonstrate the significant improvements achieved by the enhanced sentiment analysis framework in terms of accuracy and reliability. The statistical analysis confirms its superior performance in capturing subtle sentiment nuances, making it a valuable tool for applications requiring precise sentiment understanding. In conclusion, this research contributes to the field of sentiment analysis by presenting an improved machine learning-based framework and conducting a rigorous statistical assessment of its accuracy. The findings provide valuable insights for researchers and practitioners seeking to enhance sentiment analysis techniques and apply them effectively in various domains..

Abstract C020: Unconvering the hidden immunosuppressive landscape in pancreatic ductal adenocarcinoma

Abstract Pancreatic ductal adenocarcinoma (PDA) is one of the deadliest forms of cancer with an extremely low survival rate. Despite the success of therapeutic T cells in hematologic cancers, many solid tumors, including PDA, remain unresponsive with such therapies, partly due to its mechanically complex, dense, and immunosuppressive stroma. Therefore, understanding the dynamic interplay between therapeutic T cells and the tumor microenvironment (TME) is crucial for revealing engineering targets for next-generation immunotherapies. We approach this problem with genetically engineered KPC (KrasLSL-G12D/+; Trp53LSL-R172H/+; Pdx-1-Cre) mouse models that recapitulates PDA disease progression and stromal dynamics. Within live tumor slices from primary tumors, we are defining how stromal components such as dense collagen fiber networks and tumor-associated macrophages (TAMs) orchestrate an immunosuppressive landscape. We employ multiphoton laser scanning microscopy (MPLSM) to generate multiphoton excitation of fluorescence (cell dyes and immunofluorescence) and second harmonic generation (SHG) for collagen fibers to acquire multidimensional data (x, y, z, c, t) of T cell migration and interaction within the TME. We subsequently reveal T cell migration and interaction as a function of its physical and biological ECM components using advanced computer vision and machine learning algorithms based on the Clustered Random Convolutional Kernel Transform (cROCKET), revealing a hidden landscape in the feature space. Perturbations to the TME in KPC mice with CCR2 inhibitor CCX598 (in vivo) and clodronate liposomes (ex vivo) deplete CD11b+ myeloid cells, resulting in altered behaviors of the infiltrating T cells. Anti-PD-1 monoclonal antibody neutralizes the PD-1/PD-L1 axis in T cells, resulting in enhanced migration and infiltration in tumor slices. Citation Format: Guhan Qian, Hongrong Zhang, Aine Knott, Jon Zettervall, Ingunn Stromnes, Paolo Provenzano. Unconvering the hidden immunosuppressive landscape in pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Pancreatic Cancer; 2023 Sep 27-30; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(2 Suppl):Abstract nr C020.

Fast Rock Detection in Visually Contaminated Mining Environments Using Machine Learning and Deep Learning Techniques

Advances in machine learning algorithms have allowed object detection and classification to become booming areas. The detection of objects, such as rocks, in mining operations is affected by fog, snow, suspended particles, and high lighting. These environmental conditions can stop the development of mining work, which entails a considerable increase in operating costs. It is vital to select a machine learning algorithm that is accurate, fast, and contributes to lower operational costs because of the aforementioned environmental situations. In this study, the Viola-Jones algorithm, Aggregate Channel Features (ACF), Faster Regions with Convolutional Neural Networks (Faster R-CNN), Single-Shot Detector (SSD), and You Only Look Once (YOLO) version 4 were analyzed, considering the precision metrics, recall, AP50, and average detection time. In our preliminary tests, we have observed that the differences between YOLO v4 and the latest versions are not substantial for the specific problem of rock detection addressed in our article. Therefore, YOLO v4 is an appropriate and representative choice for evaluating the effectiveness of existing methods in our study. The YOLO v4 algorithm performed the best overall, whereas the SSD algorithm performed the fastest. The results indicate that the YOLO v4 algorithm is a promising candidate for detecting rocks with visual contamination in mining operations.

Diabetes Prediction using Data Mining Techniques

This project delves into the realm of data mining for diabetes prediction, with a focus on enriching the well- known Pima Indian Diabetes dataset by incorporating vital health metrics, particularly heart rate, derived from fitness trackers. The initiative encompasses three integral components: firstly, the exploration and enhancement of the dataset through the integration of heart rate; secondly, the application of diverse data mining techniques, ranging from logistic regression and random forest to support vector machines and naive Bayes, along with advanced machine learning algorithms and neural networks, for a comprehensive comparative analysis of their efficacy in diabetes prediction; and finally, the development of a user-friendly web application disseminating diabetes-related information, detailing project methodologies, and offering users the ability to assess their diabetes risk through instant feedback based on inputted health details. This multi-faceted approach not only contributes to the field of healthcare data analytics by evaluating diverse prediction methods but also aligns with contemporary trends in wearable technology integration for health monitoring, bridging the gap between data analytics and public health awareness. The findings offer valuable insights for healthcare professionals and researchers striving for robust methods in diabetes prediction and prevention.

Synergizing Machine Learning Algorithm with Triboelectric Nanogenerators for Advanced Self-Powered Sensing Systems.

The advancement of the Internet of Things (IoT) has increased the demand for large-scale intelligent sensing systems. The periodic replacement of power sources for ubiquitous sensing systems leads to significant resource waste and environmental pollution. Human staffing costs associated with replacement also increase the economic burden. The triboelectric nanogenerators (TENGs) provide both an energy harvesting scheme and the possibility of self-powered sensing. Based on contact electrification from different materials, TENGs provide a rich material selection to collect complex and diverse data. As the data collected by TENGs become increasingly numerous and complex, different approaches to machine learning (ML) and deep learning (DL) algorithms have been proposed to efficiently process output signals. In this paper, the latest advances in ML algorithms assisting solid-solid TENG and liquid-solid TENG sensors are reviewed based on the sample size and complexity of the data. The pros and cons of various algorithms are analyzed and application scenarios of various TENG sensing systems are presented. The prospects of synergizing hardware (TENG sensors) with software (ML algorithms) in a complex environment and their main challenges for future developments are discussed.

Proactive Customer Support: Re-Architecting A Customer Support/Relationship Management Software System Leveraging Predictive Analysis/AI and Machine Learning

This scholarly article explores the transformative evolution of customer relationship management (CRM) systems by integrating predictive analysis, artificial intelligence (AI), and machine learning. Traditional CRM systems exhibit weaknesses in areas such as customer privacy exploitation and differential treatment, necessitating a reevaluation of their foundational principles. Integrating advanced analytics and machine learning algorithms emerges as a strategic avenue for modernizing CRM, allowing organizations to anticipate customer needs, address issues proactively, and foster meaningful relationships. The paper details the re-architecting of CRM systems, emphasizing considerations for organizations transitioning from traditional to modern approaches. Scalability, flexibility, and adaptability are essential principles for ensuring CRM systems evolve with dynamic customer needs. The article also addresses the challenges and ethical considerations of integrating predictive analysis and AI into CRM, emphasizing responsible data practices and transparent decision-making processes. Furthermore, the paper projects future trends in CRM evolution, outlining the significance of automation, personalization, reliance on AI, and social media integration. The convergence of these trends is poised to redefine customer experiences, creating a landscape where hyper personalization and seamless omnichannel interactions are the norm. In conclusion, the transformative evolution of CRM systems extends beyond technological upgrades to encompass a strategic shift in organizational culture and customer-centricity. By embracing the potential of predictive analysis, AI, and machine learning, organizations can navigate challenges, ensuring CRM remains committed to genuine customer satisfaction, trust, and enduring partnerships.