Logistic Regression Algorithm Research Articles

Data-driven learning-based classification model for mitigating false data injection attacks on dynamic line rating systems

The increasing need to explore electric power grid expansion technologies like dynamic line rating (DLR) systems and their dependence on real-time weather data for system planning necessitates research into false data injection attacks (FDIA) on cyber-physical power systems (CPS). This study aims to develop a robust machine learning model to mitigate FDIA in DLR systems, focusing on statistical data processing, feature ranking, selection, training, validation and evaluation. It synthesises z-score and other statistical analyses with minimum redundancy maximum relevance (MR-MR) feature ranking and selection algorithm to improve model performance and generalisation of binary generalised linear model logistic regression (BGLM-LR) and other machine learning classification algorithms. The resulting models formed with BGLM-LR, Gaussian naïve Bayes (GNB), linear support vector machine (LSVM), wide neural network (WNN), and decision tree (DT) were trained and tested with 10-year hourly DLR history data features. The evaluation of the models on unseen data revealed enhanced validation and testing accuracies after the MR-MR feature ranking and selection. BGLM-LR, GNB, LSVM, and WNN showed promising performance for mitigating FDIA. However, the study identifies DT’s limitations as overfitting and lacking generalisation in FDIA mitigation. z-score-MR-MR-BGLM-LR and z-score-MR-MR-LSVM models exhibited outstanding performances with zero false negative rates highlighting the significance of feature ranking and selection. Still, the z-score-MR-MR-BGLM-LR combination exhibits the highest marginal improvement from training to testing, the lowest training and validation time and a perfect area under curve (AUC) of the receiver operating characteristics making it the best choice in mitigating FDIA when computational resources are limited.

MRI-Based Vertebral Bone Quality Score Can Predict the Imminent New Vertebral Fracture After Vertebral Augmentation.

The incidence of imminent new vertebral fracture (NVF) is notably high after vertebral augmentation (VA), but accurately assessing the imminent risk of NVF remains a great challenge. The aim of this study was to investigate whether the MRI-based vertebral bone quality (VBQ) score can predict the risk of imminent NVF after VA within a 2-year period. A total of 135 patients age 50 years and older who suffered from painful osteoporotic vertebral compression fracture and treated with VA were enrolled in this retrospective study. Each patient's VBQ scores were calculated from T1-weighted, T2-weighted, and short tau inversion recovery sequences of preoperative lumbar MRI. The clinical factors and VBQ score were integrated to create a predictive model by using the logistic regression algorithm and visualize by nomogram. Receiver operating characteristic curve, calibration curve, and decision curve analyses were used to evaluate the predictive performance of the nomogram. The mean VBQ-T1WI and VBQ-T2WI scores of the NVF group were 4.61 ± 0.55 and 0.89 ± 0.14, respectively, which were significantly higher than those of the without NVF group (3.99 ± 0.54 and 0.79 ± 0.12, respectively, P < .001), as well as the VBQ-combined score (0.75 ± 1.30 vs -0.80 ± 1.26, P < .001), which is the combination of VBQ-T1WI and VBQ-T2WI scores. On multivariate analysis, the predictors of imminent NVF included age (odds ratio [OR] = 1.064, 95% CI = 1.009-1.122, P = .022), previous vertebral fracture (OR = 2.089, 95% CI = 0.888-4.915, P = .091), and VBQ-combined score (OR = 2.239, 95% CI = 1.529-3.279, P < .001). The nomogram achieved superior performance with an area under the receiver operating characteristic curve of 0.838 (95% CI: 0.773-0.904) in predicting the imminent NVF compared to the clinical factors or VBQ-combined score alone. The VBQ score obtained from lumbar MRI can be used to assess the VBQ and predict the imminent NVF after VA in patients with osteoporotic vertebral compression fracture.

Capability of Machine Learning Algorithms to Classify Safe and Unsafe Postures during Weight Lifting Tasks Using Inertial Sensors.

Occupational ergonomics aims to optimize the work environment and to enhance both productivity and worker well-being. Work-related exposure assessment, such as lifting loads, is a crucial aspect of this discipline, as it involves the evaluation of physical stressors and their impact on workers' health and safety, in order to prevent the development of musculoskeletal pathologies. In this study, we explore the feasibility of machine learning (ML) algorithms, fed with time- and frequency-domain features extracted from inertial signals (linear acceleration and angular velocity), to automatically and accurately discriminate safe and unsafe postures during weight lifting tasks. The signals were acquired by means of one inertial measurement unit (IMU) placed on the sternums of 15 subjects, and subsequently segmented to extract several time- and frequency-domain features. A supervised dataset, including the extracted features, was used to feed several ML models and to assess their prediction power. Interesting results in terms of evaluation metrics for a binary safe/unsafe posture classification were obtained with the logistic regression algorithm, which outperformed the others, with accuracy and area under the receiver operating characteristic curve values of up to 96% and 99%, respectively. This result indicates the feasibility of the proposed methodology-based on a single inertial sensor and artificial intelligence-to discriminate safe/unsafe postures associated with load lifting activities. Future investigation in a wider study population and using additional lifting scenarios could confirm the potentiality of the proposed methodology, supporting its applicability in the occupational ergonomics field.

Bilirubin and postpartum depression: an observational and Mendelian randomization study.

Postpartum depression (PPD) is one of the most common complications of delivery and is usually disregarded. Several risk factors of PPD have been identified, but its pathogenesis has not been completely understood. Serum bilirubin has been found to be a predictor of depression, whose relationship with PPD has not been investigated. Observational research was performed followed by a two-sample Mendelian randomization (MR) analysis. From 2017 to 2020, the clinical data of pregnant women were retrospectively extracted. Logistic regression and random forest algorithm were employed to assess the risk factors of PPD, including the serum levels of total bilirubin and direct bilirubin. To further explore their potential causality, univariable and multivariable Mendelian randomization (MVMR) were conducted. Sensitivity analyses for MR were performed to test the robustness of causal inference. A total of 1,810 patients were included in the PPD cohort, of which 631 (34.87%) were diagnosed with PPD. Compared with the control group, PPD patients had a significantly lower level of total bilirubin (9.2 μmol/L, IQR 7.7, 11.0 in PPD; 9.7 μmol/L, IQR 8.0, 12.0 in control, P < 0.001) and direct bilirubin (2.0 μmol/L, IQR 1.6, 2.6 in PPD; 2.2 μmol/L, IQR 1.7, 2.9 in control, P < 0.003). The prediction model identified eight independent predictive factors of PPD, in which elevated total bilirubin served as a protective factor (OR = 0.94, 95% CI 0.90-0.99, P = 0.024). In the MR analyses, genetically predicted total bilirubin was associated with decreased risk of PPD (IVW: OR = 0.86, 95% CI 0.76-0.97, P = 0.006), which remained consistent after adjusting educational attainment, income, and gestational diabetes mellitus. Conversely, there is a lack of solid evidence to support the causal relationship between PPD and bilirubin. Our results suggested that decreased total bilirubin was associated with the incidence of PPD. Future studies are warranted to investigate its potential mechanisms and illuminate the pathogenesis of PPD.

Comparison and analysis of the effect of various machine learning algorithms on cancer prediction

Cancer is a serious disease that can have a big impact on the physical and mental health of patients. The incidence and mortality rate of cancer are increasing globally. Therefore, predicting the occurrence and treatment effect of cancer has become a hot research topic in the medical field. Machine learning algorithms can use vast amounts of data and algorithmic models to predict aspects of cancer occurrence, progression, and treatment effectiveness. This algorithm can learn and find patterns from large amounts of medical data, thereby improving the diagnosis and treatment of cancer. In this paper, naive Bayes model, logistic regression model, random forest model, KNN model and Ridge regression model were used to predict the data of cancer patients. The accuracy of naive Bayes model and logistic regression model is the highest, reaching 95%. The accuracy of random forest model and KNN model is 94%. The worst performing model was Ridge regression, with an accuracy of 93%. Naive Bayes algorithm and logistic regression algorithm perform well in solving binary classification problems because they are both probability-based classification algorithms. In this problem, the number of features is large, so the ridge regression algorithm can effectively process the data, but due to the small amount of data, it is easy to appear underfitting, so the performance is poor. In this paper, a variety of machine learning algorithms are used to predict cancer patients, and the accuracy, accuracy, recall rate and f1 parameters of each model are calculated and compared, which provides a basis for subsequent research.

Students’ Sentiment Analysis Using Natural Language Toolkit in Machine Learning for Module Evaluation

This paper presents a combination of natural language toolkit (NLTK) in machine learning for sentiment analysis used for module evaluation. The module evaluation is typically done at the end of each module. Dataset of 300 students evaluating each module is conducted with excellent, very good, good, fair, and poor sentiments, delivers valuable perceptions into the overall teaching and lecturing quality and decision making for enlightening methodology of teaching and approaches. This paper demonstrates sentiment analysis model trained using logistic regression algorithm in Machine Learning to evaluate the sentiments given by students in their module evaluation. A study comparison has been done between the proposed model and other sentiment analysis for module evaluation. The results of experiments have been analyzed for decision-making.

Performance Comparison of different Disease Detection using Stacked Ensemble Learning Model

Malignancy risks and genetic disorders have long been challenging due to procedures that lack precision and predictability, thereby complicating the precise identification of diseases and their root causes. Machine learning classifiers have emerged as more suitable and effective tools. Various machine learning classifiers have been utilized to examine different genetic disorders, and the results from these classifiers have been further compared to determine their superiority. In this study, a variety of classifiers, including the SVM, KNN, decision tree, random forest, and logistic regression algorithms, are examined. These classifiers utilize specific training variables to analyze how input values correspond to the respective class. After successfully implementing each classifier, we proceeded to employ Stacking, an ensemble machine learning technique that aggregates predictions from individual classifiers on the same dataset. Four datasets, including the breast cancer, diabetes, Parkinson’s, and genomic datasets, were successfully implemented using the aforementioned methods, and the results obtained showed how the input values correspond to the class using a few training variables. SVM classifier was shown to be the most effective of the five described classifiers, having the highest accuracy in most of the cases. It provided accuracies of 97.43%, 97.46%, 97.45%, and 97.44% for each of the genome cancer, diabetes, Parkinson’s, and breast cancer datasets. The KNN and Random Forest models also came out to be very effective, with accuracy around 95% and 91%, respectively, for various disease datasets. The Logistic Regression and Decision Tree models also worked well. However, the ensemble method of Stacking proved to be highly efficient above all other base models and generated accuracies above 97.5% for all the aforementioned diseases.

Combined analysis of bulk RNA and single-cell RNA sequencing to identify pyroptosis-related markers and the role of dendritic cells in chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) is characterized by dyspnea caused by airflow limitation. Further development may lead to decreased lung function and other lung diseases. Pyroptosis is a type of programmed cell death that involves multiple pathways. For example, the pathway induced by the NLR family pyrin domain containing 3 (NLRP3) inflammasome is closely associated with COPD exacerbation. Therefore, in this study, various machine learning algorithms were applied to screen for diagnostically relevant pyroptosis-related genes from the GEO dataset, and the results were verified using external datasets. The results showed that deep neural networks and logistic regression algorithms had the highest AUC of 0.91 and 0.74 in the internal and external test sets, respectively. Here, we explored the immune landscape of COPD using diagnosis-related genes. We found that the infiltrating abundance of dendritic cells significantly differed between the COPD and control groups. Finally, the communication patterns of each cell type were explored based on scRNA-seq data. The critical role of significant pathways involved in communication between DCS and other cell populations in the occurrence and progression of COPD was identified.

Dual axis solar tracking system for agriculture applications using machine learning

The three most basic amenities required for human survival are food, shelter and clothing. In today's tech-savvy generation, these have experienced a great deal of scientific advancement. Unfortunately, agriculture is still more man power-oriented. So they have to rely on the hit and trial method to learn from experience which leads to waste of time. In proposed work includes an automated system using dual axis solar tracking system and gives crop recommendation for different types of soil to yield maximum. The suggested system is a dual-axis solar tracker based on machine learning that is intended to considerably increase the effectiveness of energy harvesting. The approach makes use of the logistic regression algorithm (LR) to do this. This novel strategy tries to maximize the solar panel's ability to produce energy, leading to increased energy yields. The quality of soil is predicted by using suitable sensors for crop recommendation. The data’s are temperature, humidity, pH of soil, nitrogen, phosphorous &amp;amp; potassium in soil and rainfall in soil are considered. For crop recommendation six algorithms- SVM, KNN, Native Bays, Logistic Regression, Decision Tree classifier, Random Forest Classifier are applied and tested. It is found that random forest classifier gave us excellent results.

Performance evaluation of machine learning algorithms and impact of activation functions in artificial neural network classifier for bearing fault diagnosis

Machine learning algorithms are used to identify the bearing condition. In this work, different machine learning techniques such as decision tree, logistic regression, support vector machine (SVM), and artificial neural network (ANN) are used and compared to find healthy and faulty conditions of the bearing. The identification of the condition of the bearing is based on vibrations recorded using Fast Fourier Transform Analyzer. The vibration data recorded for the bearings have been used to categorize the condition of the bearing as healthy or faulty by applying machine learning techniques. The dataset of healthy and faulty bearings is collected using a four-channel Fast Fourier Transform Analyzer (FFT) analyzer. However, the statistical feature extraction technique has been used to evaluate the accuracy and performance of artificial neural network, support vector machine, logistic regression, and decision tree algorithms based on their classification accuracy and total costs. The result of the work reveals that the performance of the activation function based artificial neural network (ANN-AF) and SVM algorithm is better than logistic regression and decision tree models. However, it is observed that the use of appropriate activation functions within the ANN-AF technique improves the accuracy of the machine learning model.

Modeling Self Help Groups’ Impact on Livelihoods in Murang’a East Sub-County: A Logistic Regression Approach

According to the World Bank (2022), approximately 8.9 million people, or 17% of Kenya’s population, live below the poverty line of 1.9 USD on a daily basis, majority of them in the rural areas. This research aimed to analyze the impact of self-help groups on the livelihoods of rural areas of Kenya, with the goal of promoting sustainable livelihoods and reducing poverty. To achieve this, the study employed machine learning specifically the logistic regression algorithm to model the impact of self-help groups on livelihoods in Murang’a East sub-county. The study used primary data obtained through the issuance of structured questionnaires to SHG members, on their wealth status since joining the self-help groups on areas such as ability to save, access to credit services and acquiring assets, both income generating and household. A total of 969 members of self-help groups were issued with the questionnaire. The study’s findings helped identify the key predictors of members’ livelihoods and provided insights into how self-help groups influence them. The results of logistic regression indicated that 91.33% of the members had seen a significant improvement on their wealth status since joining self-help groups and the significant predictor variables were income generating assets, access to basic commodities and access to loans. The model’s accuracy was 88.04%. The ethical considerations in this study included ensuring no coercion or pressure to participate in the study and confidentiality and privacy of the respondents.

Prediction of successful weaning from renal replacement therapy in critically ill patients based on machine learning

Background Predicting the successful weaning of acute kidney injury (AKI) patients from renal replacement therapy (RRT) has emerged as a research focus, and we successfully built predictive models for RRT withdrawal in patients with severe AKI by machine learning. Methods This retrospective single-center study utilized data from our general intensive care unit (ICU) Database, focusing on patients diagnosed with severe AKI who underwent RRT. We evaluated RRT weaning success based on patients being free of RRT in the subsequent week and their overall survival. Multiple logistic regression (MLR) and machine learning algorithms were adopted to construct the prediction models. Results A total of 976 patients were included, with 349 patients successfully weaned off RRT. Longer RRT duration (7.0 vs. 9.6 d, p = 0.002, OR = 0.94), higher serum cystatin C levels (1.2 vs. 3.2 mg/L, p < 0.001, OR = 0.46), and the presence of septic shock (28.1% vs. 41.5%, p < 0.001, OR = 0.63) were associated with reduced likelihood of RRT weaning. Conversely, a positive furosemide stress test (FST) (60.2% vs. 40.7%, p < 0.001, OR = 2.75) and higher total urine volume 3 d before RRT withdrawal (755 vs. 125 mL/d, p < 0.001, OR = 2.12) were associated with an increased likelihood of successful weaning from RRT. Next, we demonstrated that machine learning models, especially Random Forest and XGBoost, achieving an AUROC of 0.95. The XGBoost model exhibited superior accuracy, yielding an AUROC of 0.849. Conclusion High-risk factors for unsuccessful RRT weaning in severe AKI patients include prolonged RRT duration. Machine learning prediction models, when compared to models based on multivariate logistic regression using these indicators, offer distinct advantages in predictive accuracy.

Predicting cheating among married individuals based on sexual satisfaction and personality traits: A review

This article discusses the prediction of cheating among married individuals based on two significant factors: sexual satisfaction and personality traits. Sexual satisfaction is an essential component of a healthy and fulfilling relationship, and individuals who experience a lack of fulfillment in their sexual lives may seek satisfaction outside their marriage. Personality traits play a crucial role in determining an individual's propensity for cheating, with several traits commonly associated with a higher likelihood of engaging in infidelity, including low levels of conscientiousness, high levels of impulsivity, narcissism, and a desire for novelty and excitement. While sexual satisfaction and personality traits each contribute to the prediction of cheating, their interaction can further enhance our understanding. Developing reliable predictive models for cheating based on sexual satisfaction and personality traits is a complex task, with researchers using various methods to discern patterns and make accurate predictions. Data analysis techniques, such as logistic regression and machine learning algorithms, have been applied to collected data on married individuals and their tendencies to cheat, achieving moderate success in predictive accuracy. However, it is essential to acknowledge the limitations of these predictions, such as individual circumstances, relationship dynamics, and other external variables that are not accounted for in these models. Privacy concerns and ethical considerations surrounding the monitoring of individuals' personal lives must also be taken into account. These predictive models should be used with caution and in conjunction with comprehensive counseling and support systems to maintain the integrity of relationships.

A chirality-sensitive approach to predict chemical transfer across the human placental barrier

The placenta is a membrane that separates the fetus from the maternal circulation, and in addition to protecting the fetus, plays a key role in fetal growth and development. With increasing drug use in pregnancy, it is imperative that reliable models of estimating placental permeability and safety be established. In vitro methods and animal models such as rodent placenta are limited in application since the species-specific nature of the placenta prevents meaningful extrapolations to humans. In this regard, in silico approaches such as quantitative structure-property relationships (QSPRs) are useful alternatives. However, despite evidence that drug transport across the placenta is stereoselective (i.e., governed by the spatial arrangement of the atoms in a molecule), many QSPR models for placental transfer have been built using 2D descriptors that do not account for chirality and stereochemistry. In this study, we apply a chirality-sensitive and proven QSPR methodology titled “EigenValue ANalySis” (EVANS) to build QSPR models for placental transfer. We deploy EVANS along with robust machine learning algorithms to build (i) regression models on a dataset of environmental chemicals (dataset PD I) followed by (ii) classification models on a set of drug-like compounds (dataset PD II). The best models were found to achieve state-of-the-art performance, with the support vector machine algorithm returning rtrain2=0.85,rtest2=0.75 for PD I, and the logistic regression algorithm giving accuracy 0.88 and F1 score 0.93 for PD II. The best models were interpreted with the Shapley Additive Explanations paradigm, and it was found that autocorrelation descriptors are crucial for modelling placental permeability. In conclusion, we demonstrate the need of a chirality-sensitive approach for modelling placental transfer of chemicals, and present two predictive QSPR models that may reliably be used for prediction of placental transfer.

Prediction and feature analysis of breast cancer based on machine learning technology

Breast cancer, whose incidence rate is increasing year by year, is one of the malignant tumours with the highest incidence rate in women. Every year, an increment of about 1300000 people suffers from breast cancer and 400000 people die from it globally. For the sake of those who may be at risk of breast cancer, it is of critical importance to establish a model that can make predictions of breast cancer. This study utilizes the Random Forest algorithm and Logistic Regression algorithm to construct an analysis model. The study is conducted on a breast cancer dataset that contains data derived from Wisconsin. Specifically, the research conducts feature selection and manages to work out the relationship between various features and tumour types and selects the 5 most significant features. Based on the data of those 5 features, the accuracy of the two models is compared and the Logistic Regression Model is further optimized to reach a higher prediction accuracy. This study is highly significant in the medical community since the model it created can help with breast cancer prediction, allowing for early intervention and a higher survival rate for possible breast cancer patients.

Machine Learning Techniques for Cyberattack Prevention in IoT Systems: A Comparative Perspective of Cybersecurity and Cyberdefense in Colombia

This study investigates the application of machine learning techniques for cyberattack prevention in Internet of Things (IoT) systems, focusing on the specific context of cyberattacks in Colombia. The research presents a comparative perspective on cyberattacks in Colombia, aiming to identify the most effective machine learning methods for mitigating and preventing such threats. The study evaluates the performance of logistic regression, naïve Bayes, perceptron, and k-nearest neighbors algorithms in the context of cyberattack prevention. Results reveal the strengths and weaknesses of these techniques in addressing the unique challenges posed by cyberattackers in Colombia’s IoT infrastructure. The findings provide valuable insights for enhancing cybersecurity measures in the region and contribute to the broader field of IoT security.

Crop Recommendation System Using Machine Learning

Abstract: Agricultural Crop Recommendation System relies on a multitude of input factors. This proposes a hybrid approach for suggesting suitable crops in specific areas, taking into account various attributes like soil type, rainfall levels, temperature value, Nitrogen value, Phosphorus value, Potassium value, humidity value and PH value of the soil. Utilizing technology to recommend crops for agriculture empowers farmers to enhance crop yields by suggesting the most suitable crops based on geographic and climatic conditions with respect to soil. The fundamental concept of this paper revolves around implementing a crop selection method to address numerous agricultural and farmer-related challenges. Using a group of different machine learning models to make better predictions is like having a team of experts give you advice. Each expert has their own knowledge and experience, and by listening to all of them, you can make a better decision. Accuracy of different ML algorithms in crop recommendation system are such as that Support Vector Machine algorithm gives 99.54% accurate results, Neural networks algorithm gives 91% accurate results, Decision Tree algorithm gives us 87% of accuracy. While K- Nearest algorithm, Logistic Regression and Naive Bayes algorithm gives us 75%-85% accurate results. We’re gonna use machine learning algorithms to make the project more accurate and efficient enough to provide the real time results. In this crop recommendation system using machine learning Support Vector Classifier and Decision tree algorithms are being used as an expectation of most accurate outcomes of the model. Keywords: Support Vector Machine, Feature selection, ,,Crop suitability indices, Training dataset, Testing dataset, Model evaluation.

Predicting Contralateral Surgery for Trapeziometacarpal Arthrosis Within 5 Years

Symptomatic trapeziometacarpal (TMC) joint arthritis is a common cause of hand pain. It is unknown how many patients ultimately elect to have bilateral surgery for TMC arthritis. In this study, we assessed the frequency and predictive factors for contralateral TMC surgery in patients who underwent prior TMC surgery. We identified 712 patients who underwent primary surgery for TMC arthritis with a follow-up period of 5 years. We collected demographic, surgical, and follow-up data. Prediction models for contralateral surgery using a training and testing data set were created with multivariable logistic regression and random forest classifier algorithms. At the time of initial surgery, 230 patients had bilateral thumb pain (32%), but only 153 patients ultimately had an operation for TMC arthritis on the contralateral side within 5 years (21% of 712 total patients and 67% of 230 patients with bilateral pain). Common predictive factors between both models for contralateral surgery were younger age (odds ratio [OR]= 0.95; 95% confidence interval [CI], 0.93-0.98), bilateral thumb pain (OR= 3.76; 95% CI, 2.52-5.65), and anxiety disorders (OR= 1.84; 95% CI, 1.11-3.03). In our study, we found that the rate of contralateral surgery was 21% in patients who underwent prior TMC surgery. Predictive factors for future contralateral surgery included younger age, bilateral thumb pain, and anxiety disorder at the time of initial surgery. Prognostic II.

Fake Instagram Account Detection using ML Algorithms

Abstract—Nowadays, the majority of people utilise social networking sites on a daily basis. Numerous people create profiles on social networking websites every day and connect with others there, regardless of their location or time. False identities are used in additional malicious operations in addition to playing a significant part in advanced persistent threats. Users of social networking sites can benefit from them, but they also have to worry about the security of their personal information. We must first determine the user's social network accounts before we can determine who is endorsing threats in these platforms. Social media usage has dramatically increased in recent years, according to statistics. Social networking sites have made things easier as they allow us to connect to people effortlessly and converse with them without the requirement for physical meetups. One of the major issues with Online Social Networks (OSNs) is fake interaction, which is used to artificially boost an account's popularity. Because phoney involvement causes businesses to lose money, inaccurate audience targeting in advertising, inaccurate product prediction systems, and an undesirable social network atmosphere, its detection is essential. Based on the classification, it is required to distinguish between real and phoney profiles on social media.Several categorization techniques have traditionally been used to identify phoney social media accounts. However, there are ways to improve social media's ability to detect phoney profiles. The suggested effort uses technology and machine learning to boost the percentage of predicted phoney profiles. Chi-square technique is used in feature selection models to find the best data. The several machine learning methods, including the Logistic Regression and Random Forest algorithms, are used in the classification approach. The classification outcome based on recall, sensitivity, specificity, f1-score, accuracy, and precision. Keywords—Machine learning, online social networks, Instagram, Social media, Natural language process.

Hyperspectral Response of the Soybean Crop as a Function of Target Spot (Corynespora cassiicola) Using Machine Learning to Classify Severity Levels

Plants respond to biotic and abiotic pressures by changing their biophysical and biochemical aspects, such as reducing their biomass and developing chlorosis, which can be readily identified using remote-sensing techniques applied to the VIS/NIR/SWIR spectrum range. In the current scenario of agriculture, production efficiency is fundamental for farmers, but diseases such as target spot continue to harm soybean yield. Remote sensing, especially hyperspectral sensing, can detect these diseases, but has disadvantages such as cost and complexity, thus favoring the use of UAVs in these activities, as they are more economical. The objectives of this study were: (i) to identify the most appropriate input variable (bands, vegetation indices and all reflectance ranges) for the metrics assessed in machine learning models; (ii) to verify whether there is a statistical difference in the response of NDVI (normalized difference vegetation index), grain weight and yield when subjected to different levels of severity; and (iii) to identify whether there is a relationship between the spectral bands and vegetation indices with the levels of target spot severity, grain weight and yield. The field experiment was carried out in the 2022/23 crop season and involved different fungicide treatments to obtain different levels of disease severity. A spectroradiometer and UAV (unmanned aerial vehicle) imagery were used to collect spectral data from the leaves. Data were subjected to machine learning analysis using different algorithms. LR (logistic regression) and SVM (support vector machine) algorithms performed better in classifying target spot severity levels when spectral data were used. Multivariate canonical analysis showed that healthy leaves stood out at specific wavelengths, while diseased leaves showed different spectral patterns. Disease detection using hyperspectral sensors enabled detailed information acquisition. Our findings reveal that remote sensing, especially using hyperspectral sensors and machine learning techniques, can be effective in the early detection and monitoring of target spot in the soybean crop, enabling fast decision-making for the control and prevention of yield losses.