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Groundwater Quality Prediction Using Proximal Hyperspectral Sensing, GIS, and machine learning algorithms

WATER AIR AND SOIL POLLUTION 2025年第6期236卷 1-25页

作者： Raheja, Hemant Goel, Arun Pal, Mahesh Natl Inst Technol Kurukshetra Dept Civil Engn Kurukshetra 136119 Haryana India

The primary objective of the present study is to assess the suitability of groundwater for drinking purposes using the Water Quality Index (WQI), Geographic Information System (GIS), and proximal sensing techniques. For this purpose, 272 groundwater samples collected before and after the monsoon period were analyzed for various hydrochemical parameter concentrations. To explore the relationship between water spectral reflectance and WQI, a spectroradiometer was used to measure the reflectance of each sample under laboratory conditions. Four machine learning procedures i.e., Support Vector Regression (SVR), M5P, Random Forest (RF), and Gaussian Process Regression (GPR) were used to predict WQI using resampled spectral reflectance. The WQI analysis revealed that approximately 30% of the groundwater samples fell within the poor to extremely poor-quality category in both periods, while 55.88% (Pre-monsoon) and 55.14% (Post-monsoon) of samples were classified under moderate quality, indicating marginal suitability for drinking purposes. The spectral curves of WQI indicated a lower reflectance for water samples with lower WQI values, whereas higher reflectance values were associated with higher WQI values. Performance evaluation of machine learning models demonstrated that SVR outperformed M5P, RF, and GPR, achieving the highest Correlation Coefficient (CC = 0.9964) and the lowest Root Mean Square Error (RMSE = 7.0313 mg/L) and Mean Absolute Error (MAE = 3.5056 mg/L) in the training phase, while maintaining similar performance in the testing phase (CC = 0.9964, RMSE = 7.6748 mg/L, MAE = 4.0297 mg/L). These findings highlight the potential of integrating spectral reflectance with machine learning models for accurate groundwater quality assessment and spatial mapping.

关键词： Water Quality Index (WQI) Spatial Distribution Spectral Reflectance machine learning algorithms

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Predicting host-pathogen interactions with machine learning algorithms: A scoping review

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INFECTION GENETICS AND EVOLUTION 2025年 130卷 105751页

作者： Sahragard, Rasool Arabfard, Masoud Najafi, Ali Baqiyatallah Univ Med Sci Biomed Technol Inst Mol Biol Res Ctr Tehran Iran Baqiyatallah Univ Med Sci Syst Biol & Poisonings Inst Chem Injuries Res Ctr Tehran Iran

Background: Diseases caused by pathogenic microorganisms pose a persistent global health challenge. Pathogens exploit host mechanisms through intricate molecular interactions. Understanding these host-pathogen interactions (HPIs), particularly protein-protein interactions (PPIs), is crucial for developing therapeutic strategies. While experimental approaches are essential, they are often labor-intensive and costly. Researchers have been able to predict HPIs more efficiently due to recent advances in artificial intelligence and machine learning. However, existing reviews lack a systematic evaluation of different machine learning methodologies and their effectiveness. Methods: This scoping review critically examines recent studies on machine learning-based Host-Pathogen Interaction (HPI) prediction, categorizing them by host and pathogen types, machine learning algorithms, and key evaluation metrics. The methodology is based on the study beginning with a preliminary search in reputable using key phrases related to host-pathogen interactions from 2019 to 2024. This process yielded 46 relevant articles, from which 30 were selected for review after evaluating titles and abstracts. Results: Our findings indicate that tree-based algorithms, particularly Random Forest and Gradient Boosting, are the most prevalent in Host-Pathogen Interaction (HPI) prediction. The filter articles were categorized by host and pathogen type and further subdivided into four subcategories based on the prediction type and machine learning algorithms: classic, tree-based, vector-based, and neural network algorithms. Convolutional and recurrent neural networks are among the deep learning models that demonstrate promising accuracy, but they require a lot of labeled data for effective training. Additionally, the analysis uncovers significant gaps in dataset standardization and model interpretability, which pose challenges to the broader applicability of these predictive models. Conclusion: In this re

关键词： Host-pathogen interactions Protein-protein interaction Prediction algorithms machine learning algorithms Bioinformatics

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Applying machine learning algorithms to explore the impact of combined noise and dust on hearing loss in occupationally exposed populations

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SCIENTIFIC REPORTS 2025年第1期15卷 1-10页

作者： Li, Yong Sun, Xin Qu, Yongtao Yang, Shuling Zhai, Yueyi Qu, Yan Hebei Med Univ Dept Otolaryngol Shijiazhuang Peoples R China Hebei Gen Hosp Dept Otolaryngol Shijiazhuang Peoples R China Hebei North Univ Zhangjiakou Peoples R China Hebei Med Univ Anim Lab Hosp 3 Shijiazhuang Peoples R China Hebei Med Univ Dept Otolaryngol Hosp 3 Shijiazhuang Peoples R China

This study aimed to explore the combined impacts of occupational noise and dust on hearing and extra-auditory functions and identify associated risk factors via machine learning techniques. Data from 14,145 workers (627 with occupational noise-induced hearing loss (ONIHL)) at Hebei Medical Examination Center (2017-2023) were analyzed. Workers with combined exposure and without specific contraindications or other hearing impairment causes were included. Demographic and clinical data were gathered. Chi-square and Mann-Whitney U tests examined variables, and multivariate logistic regression determined ONIHL risk factors. machine learning algorithms like Logistic Regression and Random Forest were developed, optimized, and evaluated. Results showed significant differences in gender, exposure, blood pressure, smoking, etc. between ONIHL and non-ONIHL groups. Male gender, combined exposure, diastolic blood pressure elevation, smoking, fasting blood glucose elevation, and age were positive predictors, while systolic blood pressure elevation was negative. The logistic model had the highest predictive ability (ROC = 0.714). Subgroup analysis revealed a significant positive correlation in specific subgroups. In summary, combined exposure increased ONIHL risk and affected health. machine learning effectively predicted ONIHL, but the study had limitations and needed further research.

关键词： Occupational exposure Noise and dust Hearing loss machine learning algorithms Risk factors

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Cloud Computing Network in Remote Sensing-Based Climate Detection Using machine learning algorithms

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Remote Sensing in Earth Systems Sciences 2025年第2期8卷 400-408页

作者： Srinivas, Jhade Raju, Ch VV Narasimha Sasikala, C. Bhaskar, Parumanchala Panyala, Amarendra Reddy Priya, Divya Department of Computer Science and Engineering - Data Science K G Reddy College of Engineering and Technology Moinabad Telangana Hyderabad India Information Technology Marri Laxman Reddy Institute of Technology and Management Dundigal Telangana Hyderabad 500043 India Dept of CSE Srinivasa Ramanujan Institute of Technology (Autonomous) Andhra Pradesh Ananthapuramu India Department of Computer Science and Engineering Santhiram Engineering College Nandyal India Department of Computer Science and Engineering (Data Science) Malla Reddy University Telangana Hyderabad India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Effective monitoring of the environment over a large area will require mobilization of a considerable amount of information. Otherwise, the use of traditional methods will prove to be costly and would take up so much time. Also, assessing the land cover changes between two times is one of the other methods which can be adopted to measure the environmental sustainability. Using ML techniques for predictive modeling in remote sensing has emerged as a useful approach in solving some of the environmental and climatic challenges. This research proposes a novel method in remote sensing image-based climate detection utilizing cloud computing and machine learning algorithms. Here, the cloud computing module is used in the collection of remote sensing images for the detection of climate changes. Then, the collected image is processed for noise removal as well as normalization. The processed image is segmented, and features are extracted utilizing a fuzzy K-clustering-based contour Gaussian model with graph cut Boltzmann CNN. Experimental analysis is carried out for various remote sensing-based climate change datasets in terms of training accuracy, random precision, AUC, recall, and F-measure. The proposed technique obtained 97% of training accuracy, 95% of random precision, 96% of recall, 93% of AUC, and 94% of F-measure. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Climate detection Cloud computing Contour Gaussian model machine learning algorithms Remote sensing images

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Sex classification accuracy through machine learning algorithms - morphometric variables of human ear and nose

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BMC RESEARCH NOTES 2025年第1期18卷 1-10页

作者： Kaur, Tej Krishan, Kewal Sharma, Akanksha Guleria, Ankita Sharma, Vishal Panjab Univ Inst Forens Sci & Criminol Chandigarh India Panjab Univ UGC Ctr Adv Study Dept Anthropol Sect 14 Chandigarh India

ObjectiveSex determination is an important parameter for personal identification in forensic and medico-legal examinations. The study aims at predicting sex accuracy from different parameters of ear and nose by using a novel approach of machine learning Library, 'PyCaret'.ResultsThe present research was carried out on 508 participants (264 males and 244 females) aged 18-35 years from north India. Various ear and nose measurements were recorded on each participant. PyCaret employs a train-eval-testing validation approach, yielding a comprehensive output of the model in the form of a table that consolidates the average scores of all models over ten folds, including the respective time values. These models were compared based on performance metrics, and time taken. The logistic regression classifier emerged as the top-performing model, achieving the highest scores of 86.75% for sex prediction accuracy. Nasal breadth has been concluded as the most significant variable in accurate sex prediction. The findings indicate that the majority of the ear and nose characteristics significantly contribute to sexual dimorphism. This novel approach for sex classification can be efficiently used in a variety of forensic examinations and crime scene investigation especially where there is a need for estimation of sex for personal identification.

关键词： Human anatomy and morphology Ear and nose morphology Forensic identification machine learning algorithms PyCaret Sex classification

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machine learning algorithms for Depression Detection and Their Comparison

Machine Learning Algorithms for Depression Detection and The...

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Cognitive Computing in Engineering, Communications, Sciences and Biomedical Health Informatics (IC3ECSBHI), International Conference on

作者： Furqan Yaqub Khan Danish Muzafar Khanday Abrar Qadir Mir Tawseef Ayoub Shaikh Department of Computer Science and Engineering National Institute of Technology Srinagar Jammu and Kashmir India Department of Information Technology Central University of Kashmir Srinagar Jammu and Kashmir India

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

Depression is one of the most common mental health disorders that afflict people and productivity in society. This paper explores automatic depression detection using user-generated content on social media. Using machine learning and deep learning models, we thus design an efficient classifier system trained on a balanced set of depressive and non-depressive social media posts. We put together in our study techniques that have been learned from the SVM, random forest classifier, multinomial naïve Bayes, and long short-term memory network as well as the advanced methods of word embeddings such as Word2Vec and BERT. The models are classifying with accuracy ranging between 70% and 81.79%, offering scalability for the early stages of identification for depression. This result brings up the potential of AI to help advance diagnostics in mental health.

关键词： Deep learning Support vector machines Productivity machine learning algorithms Social networking (online) Scalability User-generated content Mental health Depression Random forests

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machine learning algorithms and Nlp for Anxiety and Depression Detection Using Twitter Data

Machine Learning Algorithms and Nlp for Anxiety and Depressi...

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Cognitive Computing in Engineering, Communications, Sciences and Biomedical Health Informatics (IC3ECSBHI), International Conference on

作者： Tanya Sood Punya Garg Husanbir Pannu Sonam Dullat Computer Engineering Thapar Institute of Engineering & Technology Patiala India Electronics & Communication Thapar Institute of Engineering & Technology Patiala India CSED Thapar Institute of Engineering & Technology Patiala India Psychology Thapar Institute of Engineering & Technology Patiala India

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

According to a study published in the Journal of Medical Internet Research, individuals with depression are more likely to use language indicative of sadness, loneliness, and negative emotions on social media platforms. The rise of social media has given us new channels to better understand and combat mental health concerns like depression and anxiety. Many individuals with anxiety or depression may not seek help due to stigma or lack of awareness about their mental health condition. By identifying potential signs through social media, various machine learning methods can facilitate early detection and intervention, potentially preventing the escalation of mental health issues. Here, we apply eight machine learning methods, namely Logistic Regression, K-Nearest Neighbors, Support Vector machine, Random Forest, Naive Bayes, Decision Tree, Gradient Boosting machine and AdaBoost for the automated detection of depression and anxiety through Twitter data. We evaluate the performance of different models and compare their accuracies on the task using datasets (DATD_Rand_training and DATD_training) of Twitter posts for depression and anxiety detection. Various model evaluation parameters like precision, accuracy, F1 score, and recall are used to evaluate these machinelearning models. For the first dataset (DATD+Rand_training), the highest accuracy, precision, recall, and F1 scores achieved were 0.926,0.927,0.926 and 0.926, respectively, by the Random Forest Algorithm, and the highest AUC achieved was 0.963 by the Decision Tree Algorithm and Logistic Regression algorithm. For the second dataset (DATD_training) the highest accuracy, recall and F1-score were 0.912,9.12,0.871 by Logistic Regression and Support Vector machine, precision was 0.871 by K-Nearest Neighbours, AUC was 0.703 was Support Vector machine. These findings suggest that some models outperform others by a significant margin in detecting depression and anxiety from usergenerated tweets.

关键词： Support vector machines Logistic regression Accuracy machine learning algorithms Social networking (online) Anxiety disorders Mental health Depression Decision trees Random forests

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machine learning algorithms for Forecasting Air Quality Index: A Predictive Analysis in the Taj Trapezium Zone (TTZ) of Agra

Machine Learning Algorithms for Forecasting Air Quality Inde...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Swati Varshney Jitendra Nath Shrivastava Neha Gupta Invertis University Bareilly U.P India Department of CSE Invertis University Bareilly U.P India Symbiosis University of Applied Science Indore M.P India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Clean air is vital for sustaining life, and its quality directly impacts health. As industrialization progresses and populations grow, air pollution has become increasingly prevalent, emerging as a significant societal challenge. Air pollution has wide-ranging negative impacts on human health, including increased risk of early death and various ailments such as skin irritation., pulmonary infections, respiratory ailments, pneumonia, lung cancer, and cardiac complications. The purpose of this study is to use machine learning algorithms to predict the Taj Trapezium Zone’s air quality index.. Such predictions can inform preventive action to mitigate air pollution. The study compares four algorithmic approaches: Light Gradient Boosting machine (LightGBM), categorical boosting (Catboost), adaptive boosting (AdaBoost), and extreme gradient boosting (XGBoost). These algorithm’s performance is evaluated based on several parameters, including R-SQUARE, Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and ((MAE).

关键词： machine learning algorithms Urban areas Lung cancer Predictive models Boosting Prediction algorithms Air pollution Market research Indexes Forecasting

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machine learning algorithms for Autonomous Underwater Vehicle (AUV) Navigation

Machine Learning Algorithms for Autonomous Underwater Vehicl...

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Computational, Communication and Information Technology (ICCCIT), International Conference on

作者： Upma Jain Nandini Shirish Boob Munugapati Bhavana Yogendra Kumar Archana Sehgal Anandhi R J Department of Comp. Sc. & Engg. Graphic Era Deemed to be University Dehradun Saveetha School of EngG. Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu Department of Comp. Sc. & Engg MLR Institute of Technology Hyderabad Telangana Department of Electrical Engg. GLA University Mathura India Department of Comp. Sc. & Engg. Lovely Professional University Phagwara India Department of Information Science Engineering New Horizon College of Engineering Bangalore

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

Autonomous underwater vehicles (AUVs) play an extremely important role in the fields of oceanography, marine science and underwater infrastructure assessment. Of course, underwater navigation isn't easy either due to varying ocean currents, limited vision, and there's no GPS. This article uses machine learning techniques to describe how AUV navigation can be improved in underwater environments with unknown environment, then enable more reliable decision making and real time obstacle avoidance. This thesis investigates many ML approaches for route planning, sensor data fusion, and obstacle detection including supervised, unsupervised learning and reinforcement learning. With great significance, reinforcement learning algorithms can optimize trial and error learning based navigation techniques to be adapted to novel situations. Additionally, deep leaning algorithms combined with data from sensors such as sonar and cameras are able to enable precise underwater landscape perception and mapping. We apply these algorithms to virtual and physical AUV missions and demonstrate that they result in significant increases in navigation precision, energy efficiency, and mission time. This research's careful consideration of the potential of machine learning for transforming AUV navigation makes more intelligent and autonomous underwater operations possible in many marine sectors.

关键词： Autonomous underwater vehicles machine learning algorithms Scalability Transportation Reinforcement learning Sonar navigation Real-time systems Planning Oceanography Internet of Things

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machine learning algorithms Based on Variations of the Least Squares Method for Power Amplifier Distortion Compensation

Machine Learning Algorithms Based on Variations of the Least...

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Systems of Signals Generating and Processing in the Field of on Board Communications

作者： Igor V. Komarov Svetlana F. Gorgadze Moscow Technical University of Communications and Informatics Moscow Russia

ISBN: (数字)9798331532635

ISBN: (纸本)9798331532642

Variants of indirect learning based on the Least Squares Method (LSM) and the Recursive Least Squares Method (RLSM) are considered for digital predistortion of nonlinear distortions in complex composite signals with high peak-toaverage power ratios in power amplifiers. The advantage of the recursive algorithm is identified, providing approximately 9 dB lower levels of spurious out-of-band emissions for the amplified complex signal compared to the simple least squares algorithm. It is shown that for the polynomial model of the power amplifier used in the study, the recursive algorithm requires approximately 700 times more elementary mathematical operations to achieve the most efficient compensation of signal nonlinearities than the least squares algorithm.

关键词： machine learning algorithms Nonlinear distortion Computer architecture Approximation algorithms Predistortion Polynomials Mathematical models Hardware Least mean squares methods Convergence

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