Advanced Machine Learning Algorithms Research Articles

A Study on Wild and Domestic Animal Detection for Farm Protection by using Computer Vision

Crop protection against wild animal intrusion has become a pressing challenge with significant social and economic implications, particularly in agricultural-dependent nations like India. In response, an innovative AI-driven surveillance system is designed to detect and determine potential threats from animals to farm environments. The system accurately identifies and classifies animals in farm images by leveraging advanced computer vision techniques and machine learning algorithms, including Support Vector Machines, K-Means clustering, Random Forest, Decision Trees, and Logistic Regression. The model generalizes effectively by analyzing a diverse dataset comprising various animal species. Key features such as accuracy, precision, recall, F1-score, and confusion matrices are employed to assess model performance comprehensively. The results showcase high accuracy across multiple algorithms. The proposed system offers a promising solution to protect crops, minimize losses, and foster harmonious coexistence between farming and wildlife. The results demonstrate good accuracy for various algorithms: 92.75% for Logistic Regression, 86.47% for Decision Trees, 95.65% for Random Forests, and 94.20% for Support Vector Machines. This highlights how reliable the system is in classifying animals, providing a viable way to safeguard crops, reduce losses, and promote peaceful cohabitation between agriculture and wildlife.

Next-Gen Test Automation in Life Insurance: Self-Healing Frameworks

The exponential complexity of contemporary Life Insurance applications - shaped by stringent regulatory compliance requirements, dynamic customer - centric innovation, and expansive product diversification - has magnified the challenges of maintaining traditional test automation frameworks. These frameworks often falter under the weight of frequent application updates, evolving user interfaces (UI), and fluctuating backend integrations, leading to brittle test scripts and heightened maintenance costs. Self-healing test automation frameworks offer a cutting-edge, AI-driven solution to this paradigm. By leveraging advanced machine learning (ML) algorithms and intelligent heuristics, these frameworks autonomously detect, diagnose, and remediate test failures caused by changes in application elements, workflows, or environments. Through real-time adaptation to UI modifications, API updates, and evolving test environments, self-healing frameworks ensure uninterrupted testing cycles with minimal manual intervention, significantly improving efficiency and scalability. This paper provides a comprehensive exploration of the architectural design and operational mechanisms of self- healing test automation frameworks within the context of Life Insurance applications. Core focus areas include their ability to dynamically recalibrate element locators and adapt test scripts during runtime, thereby enhancing test coverage, optimizing maintenance workflows, and reducing the overall defect detection lifecycle. Through an in-depth analysis, the paper highlights the integration of AI-powered locator optimization engines, the deployment of predictive analytics for early anomaly detection, and the orchestration of continuous self-healing pipelines in CI/CD ecosystems. Detailed case studies from the Life Insurance domain illustrate tangible benefits such as accelerated regression cycles, substantial cost reductions, and improved system resilience. Keywords: Artificial Intelligence, Machine Learning, Self – Healing, Life Insurance Applications, Quality Assurance, Test Maintenance Efficiency

AI-Powered Weather System with Disaster Prediction

This project develops an AI-powered weather system that aims to improve disaster prediction and preparedness through advanced machine learning algorithms and real-time meteorological data. By collecting data from the OpenWeatherMap API, the system analyzes key environmental indicators, including rainfall, temperature, and wind speed, to detect risks of floods, droughts, cyclones, hailstorms, and wildfires. Through machine learning models like linear regression and threshold-based heuristics, the system achieves high predictive accuracy for natural disasters. This study demonstrates the effectiveness of AI in early warning systems, thus enhancing community preparedness and supporting timely interventions by authorities. The threshold-based heuristics method involved setting predetermined weather condition limits based on historical disaster data, where surpassing these thresholds indicates high disaster risk. Linear regression was used to forecast variables like rainfall and temperature, essential for early warning in disaster management. Both techniques were implemented in tandem, with linear regression providing probabilistic outputs that feed into the heuristic system to improve prediction accuracy

Dynamic multi-criteria scheduling algorithm for smart home tasks in fog-cloud IoT systems

The proliferation of Internet of Things (IoT) devices in smart homes has created a demand for efficient computational task management across complex networks. This paper introduces the Dynamic Multi-Criteria Scheduling (DMCS) algorithm, designed to enhance task scheduling in fog-cloud computing environments for smart home applications. DMCS dynamically allocates tasks based on criteria such as computational complexity, urgency, and data size, ensuring that time-sensitive tasks are processed swiftly on fog nodes while resource-intensive computations are handled by cloud data centers. The implementation of DMCS demonstrates significant improvements over conventional scheduling algorithms, reducing makespan, operational costs, and energy consumption. By effectively balancing immediate and delayed task execution, DMCS enhances system responsiveness and overall computational efficiency in smart home environments. However, DMCS also faces limitations, including computational overhead and scalability issues in larger networks. Future research will focus on integrating advanced machine learning algorithms to refine task classification, enhancing security measures, and expanding the framework’s applicability to various computing environments. Ultimately, DMCS aims to provide a robust and adaptive scheduling solution capable of meeting the complex requirements of modern IoT ecosystems and improving the efficiency of smart homes.

Enhanced long short-term memory architectures for chaotic systems modeling: An extensive study on the Lorenz system.

Despite recent advancements in machine learning algorithms, well-established models like the Long Short-Term Memory (LSTM) are still widely used for modeling tasks. This paper introduces an enhanced LSTM variant and explores its capabilities in multiple input single output chaotic system modeling, offering a large-scale analysis that focuses on LSTM gate-level architecture, the effects of noise, non-stationary and dynamic behavior modeling, system parameter drifts, and short- and long-term forecasting. The experimental evaluation is performed on datasets generated using MATLAB, where the Lorenz and Rössler system equations are implemented and simulated in various scenarios. The extended analysis reveals that a simplified, less complex LSTM-based architecture can be successfully employed for accurate chaotic system modeling without the need for complex deep learning methodologies. This new proposed model includes only three of the four standard LSTM gates, with other feedback modifications.

Can heart rate sequences from wearable devices predict day-long mental states in higher education students: a signal processing and machine learning case study at a UK university

The mental health of students in higher education has been a growing concern, with increasing evidence pointing to heightened risks of developing mental health condition. This research aims to explore whether day-long heart rate sequences, collected continuously through Apple Watch in an open environment without restrictions on daily routines, can effectively indicate mental states, particularly stress for university students. While heart rate (HR) is commonly used to monitor physical activity or responses to isolated stimuli in a controlled setting, such as stress-inducing tests, this study addresses the gap by analyzing heart rate fluctuations throughout a day, examining their potential to gauge overall stress levels in a more comprehensive and real-world context. The data for this research was collected at a public university in the UK. Using signal processing, both original heart rate sequences and their representations, via Fourier transformation and wavelet analysis, have been modeled using advanced machine learning algorithms. Having achieving statistically significant results over the baseline, this provides a understanding of how heart rate sequences alone may be used to characterize mental states through signal processing and machine learning, with the system poised for further testing as the ongoing data collection continues.

Optimizing IoT-driven smart grid stability prediction with dipper throated optimization algorithm for gradient boosting hyperparameters

With the surge in global population and economic expansion, there's been a marked increase in electricity demand. This necessitates the efficient distribution of electricity to both residential and industrial sectors to minimize energy loss. Smart Grids (SG) emerge as a promising solution to reduce power dissipation in distribution networks. The application of machine learning and artificial intelligence in SGs has significantly improved the precision of predicting consumer electricity needs. This paper presents a novel approach to improving the stability prediction of Internet of Things (IOT)-driven SGs using different advanced machine learning models. This study explores multiple advanced machine-learning techniques, including Gradient Boosting (GB), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Neural Networks, and the Decision Tree classifier, focusing on the stability prediction of SGs. This study explores the efficiency of hyperparameter-optimized GB models in predicting SG dynamic stability that encompasses the ability of the system to return to a stable operating point following a disturbance. Focusing on various models, it identifies the Dipper Throated Optimization Algorithm DTO+GB model as the standout, exhibiting unparalleled accuracy and reliability across critical performance metrics such as accuracy (99.32 %), sensitivity (99.16 %), and specificity (99.54 %). Diagnostic and regression analyses further emphasize its better predictive power and the need for hyperparameter optimization to improve the model. This paper highlights the capabilities of advanced machine learning algorithms in conjunction with tactical hyperparameter optimization in enhancing SG stability prediction, introducing a new baseline for future technological and methodological developments in this application.

A Data-Driven and Hybrid Approach for Real-Time Threshold-Based Landslide Prediction in Hilly Regions using Sensor Networks

ABSTRACT Landslides pose a constant threat to the Himalayan regions, causing severe harm to people and property, and disastrous consequences demand novel ways of early prediction and prevention. The study gives a comprehensive insight into real-time landslide prediction using data from several sensors. The study investigates the applicability of advanced machine learning algorithms such as Multiple Linear Regression (MLR), XGBoost (XGB), Random Forest (RF), and a hybrid MLR-LSTM model in predicting landslide disasters. Further, the performance of these models was evaluated by utilizing the evaluation metrics such as MSE, MAE, and RMSE. However, the hybrid MLR-LSTM model excels with MSE = 0.014, MAE = 0.140, and RMSE = 0.120. Additionally, the study demonstrates the hybrid model’s practical application by demonstrating the real-time alert generation method.

Optimizing Photocatalytic Dye Degradation: A Machine Learning and Metaheuristic Approach for Predicting Methylene Blue in Contaminated Water

Dye contamination in water sources has severe environmental and public health risks; therefore, it needs effective monitoring and remediation strategies. The aim of the study is to use machine learning techniques to develop predictive models that may be used to evaluate methylene blue dye degradation capacity in contaminated water. Ten different machine learning models, including AdaBoost, Bagging, CatBoost, Decision Tree, Extra Trees, Gradient Boosting, HistGradientBoosting, LightGBM, Random Forest, and XGBoost, were evaluated using CuWO₄@TiO₂ as a photocatalyst. R², MSE, RMSE, MAE, and MedAE were used to assess the performance of the models. Among all models, HistGradientBoosting had a very well-balanced performance. It reached a very high R² of 0.9998 on the training set and 0.9915 on the test set, coupled with low error metrics, showcasing its strong generalization capability. However, Gradient Boosting and CatBoost exhibited impressive predictive performance, while AdaBoost and Decision Tree models suffered from overfitting. The maximum prediction obtained in the case of the integral approach for MB dye degradation is 98.99%. Experimental validation indicated that the effectiveness under optimized conditions reached 98.5%. In the case of initial MB concentration at 10 mg/L, a dosage of CuWO4@TiO2 photocatalyst at 200.33 mg/L, light intensity at 150 mW/cm², contact time at 88.6 minutes at room temperature, and near-neutral pH 7.0. The final model metrics included an R² score of 0.9915, MedAE of 1.171, MSE of 5.634, MAE of 1.735, and RMSE of 2.374. This work points out the possibility of taking complete advantage of advanced machine learning algorithms along with metaheuristics optimization in improving photocatalytic processes, hence opening a bright avenue for real applications in water treatment.

AI in Financial Strategy: Continuous Monitoring for Enhanced Profitability

This article investigates the transformative impact of Artificial Intelligence (AI) in financial strategy through continuous monitoring systems designed to enhance organizational profitability. The article examines how AI-driven solutions analyze spending patterns, optimize resource allocation, and improve decision-making processes across various organizational contexts. Through comprehensive analysis of historical financial data and implementation of advanced machine learning algorithms, this research demonstrates significant improvements in cost reduction and budget optimization. The article reveals that organizations implementing AI-driven financial monitoring systems achieve substantial reductions in operational costs, enhanced accuracy in spending categorization, and improved financial decision-making capabilities. The article presents a systematic framework for implementing AI-based financial monitoring systems, including detailed methodologies for pattern recognition, expenditure analysis, and resource utilization assessment. Additionally, it explores the practical implications of AI integration in financial strategy, addressing both the opportunities and challenges organizations face during implementation. The article contributes to the growing body of knowledge on AI applications in financial management by providing actionable insights for organizations seeking to leverage artificial intelligence for improved financial performance and strategic decision-making.

Multichannel EMG-based gesture recognition utilizing advanced machine learning techniques: A random forest classifier for high-precision signal classification

This research examines how advanced machine learning algorithms can be used to classify multichannel electromyographic (EMG) signals with a high level of accuracy to assist in recognizing hand gestures. The goal is to create a robust and scalable system for gesture-based virtual control using EMG signals with potential applications in assistive technologies, rehabilitation, and human-computer interaction. Data were gathered using a MYO Thalmic bracelet containing eight EMG sensors on thirty-six subjects, and a Random Forest classifier was trained to identify seven distinct types of hand gestures (rest, fist clench, wrist flexion/extension, and radial/ulnar deviations). The machine learning pipeline included extensive preprocessing (i.e., EMG signal normalization and signal feature extraction; root mean square, waveform length, and zero crossing rate) and several hyperparameter tuning procedures to improve model performance. The Random Forest model (100 decision trees) achieved an overall classification accuracy of 98.68%, with a range of accuracies for each class (e.g., 95.2% wrist flexion and 91.8% ulnar deviation) when evaluated using cross-validation (i.e., average F1-score = 0.92, precision = 0.94, recall = .91). Overall, the study provides strong evidence for the effectiveness of ensemble learning methods at analyzing complex, multidimensional EMG signals. The high classification accuracy reported, in particular, demonstrates that the system could function for real-time recognition of hand gestures in a virtual environment. Ultimately, the initial work sets the stage for future exploration of a model that may be integrated with actuation models to control prosthetic limbs, virtual actors/avatars, and robotic devices. By demonstrating a scalable and efficient method of gesture recognition using EMG signals, these early findings enable future pathways and possibilities to design innovative, assistive solutions for digital systems that increase accessibility and interaction for users who are motor impaired or have a limited range of motion.

Facial Feature Localization and Analysis Using Machine Learning

The facial feature localization system with machine learning (ML) is an innovative solution designed to accurately detect and locate key facial landmarks. By integrating advanced ML algorithms, the system processes images or video streams in real time to identify the positions of facial features such as the eyes, nose, mouth, and jawline. The ML component analyses the data to precisely locate these features, enabling applications in areas like facial expression analysis and biometric verification. This approach enhances accuracy by adapting to various lighting conditions, face orientations, and individual facial variations. The system’s scalability and flexibility further extend its utility, supporting diverse applications across industries like healthcare, security, and human-computer interaction. Overall, this project represents a significant advancement in facial feature localization, using IoT and ML to enable more effective and accurate facial analysis. By adapting to various lighting conditions, face orientations, and individual facial variations" to "by adapting to diverse lighting conditions, different face orientations, and individual facial variations, ensuring reliable performance in various environments.

Conceptual framework for data-driven reservoir characterization: Integrating machine learning in petrophysical analysis

Reservoir characterization plays a critical role in optimizing oil and gas production by accurately determining subsurface properties such as porosity, permeability, and fluid saturation. Traditionally, this process has relied on the interpretation of well logs, seismic data, and core samples using manual or physics-based methods. However, these approaches often face limitations when dealing with complex datasets and heterogeneous reservoir properties. This review develops a conceptual framework for integrating machine learning (ML) into petrophysical analysis to revolutionize reservoir characterization. By utilizing advanced ML algorithms such as supervised learning, unsupervised learning, and deep learning, this model demonstrates the potential of data-driven methods to improve the accuracy and efficiency of reservoir analysis. The framework focuses on how ML techniques can automate the interpretation of well logs, enhance seismic data processing, and predict rock properties from core samples, leading to more accurate identification of reservoir boundaries and productive zones. This approach not only reduces interpretation errors but also accelerates decision-making in reservoir management. Moreover, machine learning can handle the vast amounts of data generated in reservoir studies, enabling real-time analysis and adaptive decision support. The study also addresses challenges such as data quality, model interpretability, and computational scalability, offering insights into future applications of ML in reservoir characterization. By combining petrophysical expertise with machine learning capabilities, this framework aims to significantly advance the field of reservoir characterization and contribute to more efficient resource management in the oil and gas industry.

Detection of Parkinson’s Disease Using MRI and Spiral Images

Early diagnosis of Parkinson’s disease (PD), a neurodegenerative disease characterized by movement disorders, can be challenging. The project aims to develop a machine learning-based model for early diagnosis of Parkinson’s disease using MRI images and spiral models. Using advanced image processing techniques and machine learning algorithms, we analyze MRI scans to identify changes in the brain associated with Parkinson’s disease. In addition, the spiral test is a simple drawing technique used to identify poor motor skills, another early indicator of the disease. This work involves collecting and preprocessing MRI and spiral images and extracting features from both data types. To make the best decision, various machine learning models were used and compared, including convolutional neural networks (CNN) for MRI images and traditional methods for spiral graphs. Performance metrics such as accuracy, precision, recall, and F1 score were used to evaluate the model’s performance distinguishing PD patients from healthy individuals. This approach provides a non-invasive early diagnosis method for PD, which can help in timely treatment and control of the disease. The integration of MRI and motion analysis provides a more comprehensive diagnostic tool with useful clinical outcomes

Exploring Data Science Techniques for Fraud Detection

Fraud detection has become increasingly critical as online transactions and digital interactions proliferate across industries. This paper explores advanced data science methodologies and machine learning algorithms for identifying fraudulent activities in real-time, addressing challenges like data imbalance, feature engineering, and model evaluation. It provides insights into traditional and emerging fraud detection techniques and emphasizes future directions for research and implementation. Key findings and ethical considerations are also discussed.

Integrating predictive analytics into strategic decision-making: A model for boosting profitability and longevity in small businesses across the United States

The integration of predictive analytics into strategic decision-making has emerged as a transformative approach for small businesses in the United States, offering enhanced pathways to profitability and longevity. This study proposes a conceptual model that explores how predictive analytics can empower small businesses to make data-driven decisions, optimize resources, and mitigate risks. By leveraging advanced machine learning algorithms, data mining, and statistical techniques, small businesses can identify trends, predict customer behavior, and uncover growth opportunities. The proposed model focuses on integrating predictive analytics across key operational areas: financial planning, marketing, inventory management, and customer relationship management. In financial planning, predictive analytics enables more accurate forecasting of revenues and expenses, supporting budget optimization and investment decisions. In marketing, businesses can analyze consumer data to design targeted campaigns, improving conversion rates and customer retention. Inventory management benefits from predictive insights by anticipating demand patterns, reducing overstocking or stockouts. Moreover, customer relationship management is enhanced through personalized recommendations and proactive engagement strategies. This research emphasizes the accessibility of predictive analytics tools, particularly through cloud-based platforms, enabling small businesses to adopt these technologies without incurring substantial costs. Additionally, the study highlights challenges such as data quality, privacy concerns, and the need for technical expertise, proposing strategies to overcome these barriers. Case studies of small businesses that have successfully implemented predictive analytics demonstrate significant outcomes, including increased operational efficiency, reduced costs, and sustained competitive advantage. The findings underscore the importance of fostering a data-driven culture and investing in training to enhance employees’ analytical capabilities. Policymakers and industry stakeholders are encouraged to support initiatives that promote the adoption of predictive analytics in small businesses through funding, education, and technical assistance programs.

Smart Machine Learning Analysis and Blockchain Healthcare Platform

The Smart Machine Learning Medical Analysis and Blockchain Healthcare Platform (SMLMAP) represents a transformative approach to medical diagnostics and data management by integrating advanced machine learning algorithms with blockchain technology. This platform is designed to accurately diagnose seven critical diseases, achieving impressive diagnostic outcomes across various conditions, including diabetes, breast cancer, heart disease, kidney disease, liver disease, malaria, and pneumonia. SMLMAP employs Random Forest algorithms for text data analysis and Convolutional Neural Networks (CNNs) for image data, ensuring rapid and precise diagnostic results. In addition to enhancing diagnostic capabilities, SMLMAP addresses significant privacy and security concerns inherent in healthcare data management. The integration of blockchain technology facilitates secure, transparent, and tamper-proof storage of patient records, empowering individuals with greater control over their medical information. This dual approach not only improves the accuracy of diagnoses but also fosters trust between patients and healthcare providers by safeguarding sensitive data against unauthorized access. Key functionalities of SMLMAP include the ability to schedule doctor appointments conveniently through the platform, ensuring that patients can receive timely medical attention. Additionally, the application provides users with a weekly healthcare magazine, delivering valuable information on health tips, disease management, and wellness advice to promote informed health decisions. By leveraging the strengths of machine learning and blockchain, SMLMAP paves the way for a more efficient, accurate, and secure healthcare ecosystem. This innovative platform not only enhances the quality of medical diagnostics but also represents a significant step forward in addressing the critical challenges of data privacy and security in the healthcare sector. The findings underscore the potential of SMLMAP to revolutionize patient care and data management in the evolving landscape of healthcare technology.

Beyond the Average: Machine Learning for Personalized Causal Inference in Econometrics

In the realm of econometrics, the estimation of average treatment effects (ATE) has traditionally dominated causal inference, often oversimplifying the complex, heterogeneous nature of individual responses to interventions. This study introduces a nuanced approach, "Beyond the Average: Personalized Causal Inference in Econometrics with Machine Learning, " which leverages advanced machine learning (ML) algorithms to shift the focus towards personalized causal effects (PCE), thereby uncovering the variability in treatment effects across individuals. Utilizing a synthetic dataset designed to reflect realistic economic behaviors and responses, we employed Gradient Boosting Machines (GBM) and Causal Forests among other ML techniques to estimate conditional average treatment effects (CATE), providing insights into the heterogeneity of treatment impacts. Our methodology encompassed comprehensive data preprocessing, feature selection based on economic theory and ML insights, and rigorous model validation processes. The results reveal significant heterogeneity in treatment effects, challenging the conventional reliance on ATE and highlighting the importance of considering individual characteristics in policy design and evaluation. Specifically, younger individuals and those with lower income and education levels exhibited markedly different responses to the financial literacy intervention, suggesting that personalized approaches could significantly enhance the effectiveness of such programs. This study not only demonstrates the feasibility and value of applying ML to econometric analysis for personalized causal inference but also lays the groundwork for future research aimed at integrating these methodologies into practical policy - making. By moving beyond the average and embracing the complexity of individual differences, econometric analysis can offer more targeted, effective, and equitable solutions to societal challenges.

Predicting permeability in sandstone reservoirs from mercury injection capillary pressure data using advanced machine learning algorithms

Predicting permeability in sandstone reservoirs from mercury injection capillary pressure data using advanced machine learning algorithms

Preliminary investigations into optimizing detector configuration for nondestructive assay of fresh nuclear fuel

Like other components of the nuclear fuel cycle, fresh nuclear fuel has potential safeguard and security concerns. Non-destructive assay (NDA) measurements need to be examined and made available to nuclear industry partners to decrease safeguard and oversight concerns for nuclear power plants and facilities. This study analyzed four proposed evaluation criteria: quantity of detectors required, speed and accuracy of detector verification, overall efficiency of neutron and gamma detections, and suitability of observables to advanced machine learning algorithms. Four different detectors have been arranged to be evaluated simultaneously: four Saint-Gobain 3He proportional counters, an Eljen EJ-309 liquid scintillator, a Saint-Gobain / Bicron NaI(Tl) scintillation detector, and a Cs2LiYCl6:Ce3+(CLYC) scintillation detector. Mock-up BWR and PWR fresh fuel configurations were developed by creating 4x4 and 3x3 natural uranium assemblies and aided in assessments for detector configurations. Additionally, 252Cf was included to increase the number of neutrons for evaluation. The results of this work will be used to develop a versatile, multi-detector system for fresh fuel verification. The exploration of detector types, data modalities, and their significance as input for advanced machine learning and data analysis algorithms is being explored to understand the obstacles and opportunities in the development of a consolidated low-cost, commercial-off-the-shelf system that nuclear power plants, facilities, and/or inspectors can use for non-destructive verification of fresh nuclear fuel and assembly templating for quick nuclear material inventory verification.