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Sustainable Development Goals (SDG) are at the forefront of government initiatives across the world. The SDGs are primarily concerned with promoting sustainable growth via ensuring wellbeing, economic growth, environmental legislation, and academic advancement. One of the most prominent goals of the SDG is to provide learners with high-quality education (SDG 4). This paper aims to look at the perspectives of the Sustainable Development Goals improvised to provide quality education. We also analyze the existing state of multiple initiatives implemented by the Indian government in the pathway to achieving objectives of quality education (SDG 4). Additionally, a case study is considered for understanding the association among the observed indicators of SDG4. For this purpose, exploratory data analysis, and numerical association rule mining in combination with QuantMiner genetic algorithm approaches have been applied. The outcomes reveal the presence of a significant degree of association among these parameters pointing out the fact that understanding the impact of one (or more) indicator on other related indicators is critical for achieving SDG 4 goals (or factors). These findings will assist governing bodies in taking preventive measures while modifying existing policies and ensuring the effective enactment of SDG 4 goals, which also will subsequently aid in the resolution of issues related to other SDGs.

This article revisits the Mincer earnings function and presents comparable estimates of the average monetary returns associated with an additional year of education across different regions worldwide. In contrast to the traditional Ordinary Least Squares (OLS) method commonly employed in the literature, this study applied a cutting-edge approach known as Support Vector Regression (SVR), which belongs to the family of machine learning (ML) algorithms. SVR is specifically chosen to address the bias arising from underfitting inherent in OLS. The analysis focuses on recent data spanning from 2010 to 2018, ensuring temporal homogeneity across the examined regions. The findings reveal that each additional year of education, on average, yields a private rate of returns of 10.4%. Notably, Sub-Saharan Africa exhibits the highest returns to education at 17.8%, while Europe demonstrates the lowest returns at 7.2%. Moreover, higher education is associated with the highest returns across the regions, with a rate of 12%, whereas primary education yields returns of 10%. Interestingly, women generally experience higher returns than men, with rates of 10.6 and 10.1%, respectively. Over time, the returns to education exhibit a modest decline, decreasing at a rate of approximately 0.1% per year, while the average duration of education demonstrates an increase of 0.16 years per year (1% per year). The application of the state-of-the-art ML technique, SVR, not only improves the accuracy of estimates but also enhances predictive performance measures such as the coefficient of determination (R[superscript 2]) and Root Mean Square Error (RMSE) when compared to the OLS method. The implications drawn from these findings emphasize the need for expanding university education, as well as investments in primary education, along with significant attention toward promoting girls' education. These findings hold considerable importance for policymakers who are tasked with making informed decisions regarding education expenditure and the implementation of education financing programs.

The Government of India offers various schemes for various classes of citizens. Most of the application forms of schemes to be filled are in English and it is observed that monolingual individuals find it difficult to access and fill the forms. This paper addresses the challenges faced by monolingual individuals in India, particularly the elderly, people with impairments, and those from marginalized communities. The proposed work is to create an interactive system called "Dhvani" voicebot, specifically designed for the Kannada language. It helps users in identifying suitable government schemes and fills forms in English. The proposed system is developed using the RASA chatbot framework and NLP techniques to comprehend user utterances. RNN and SVM algorithms are employed to ensure smooth conversation flow and interaction with the users. To enhance scheme suggestion accuracy, a knowledge graph is created, containing relevant data on government schemes. The intent classification model achieves an accuracy of 97%, indicating its ability to accurately understand user intentions. The integration of a knowledge graph improves the accuracy of scheme identification and suggestion to users. The system automates the process of filling out government scheme forms based on user inputs. Dhvani voicebot system presents a practical solution to address the challenges faced by monolingual individuals in accessing government schemes in India. The high accuracy of intent classification and the use of a knowledge graph contribute to the system's effectiveness. The study suggests that this system can be extended to other languages. An automated tool called "Dhvani" will solve the problem of aged, illiterate and physically challenged persons filling forms in post offices and banks. Most of the schemes, pension funds, cash withdrawal, cash deposit is through these organizations. So. the tool makes the process easier for the above mention persons without the help of others. An intent recognition and interactive tool developed in Kannada Language which is widely spoken in Karnataka, India. The digital resources available in Kannada Language is very sparce. Use of technology like interactive tool, Knowledge graph, RNN and SVM are used in the development of the tool. Government scheme recommendation interactively makes the users to choose the scheme faster in an interactive way. The form is filled automatically and can be edited to rectify mistakes. [ABSTRACT FROM AUTHOR]

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Background: India is a large and populous country where reliable data on headache disorders are relatively scarce. This study in northern India (Delhi and National Capital Territory Region [NCR], including surrounding districts in the States of Haryana, Uttar Pradesh and Rajasthan) continues the series of population-based studies within the Global Campaign against Headache and follows an earlier study, using the same protocol and questionnaire, in the southern State of Karnataka. Methods: This cross-sectional study used the Global Campaign's established methodology. Biologically unrelated Indian nationals aged 18–65 years were included through multistage random sampling in both urban and rural areas of NCR. Interviews at unannounced household visits followed the structured Headache-Attributed Restriction, Disability, Social Handicap and Impaired Participation (HARDSHIP) questionnaire in its original English version or in the validated Hindi version. Demographic enquiry was followed by a neutral headache screening question and diagnostic questions based on the International Classification of Headache Disorders edition 3 (ICHD-3), which focused on each respondent's most bothersome headache. Questions about headache yesterday (HY) enabled estimation of 1-day prevalence. A diagnostic algorithm first identified participants reporting headache on ≥ 15 days/month (H15+), diagnosing probable medication-overuse headache (pMOH) in those also reporting acute medication use on ≥ 15 days/month, and "other H15+" in those not. To all others, the algorithm applied ICHD-3 criteria in the order definite migraine, definite tension-type headache (TTH), probable migraine, probable TTH. Definite and probable diagnoses were combined. Results: Adjusted for age, gender and habitation, 1-year prevalences were 26.3% for migraine, 34.1% for TTH, 3.0% for pMOH and 4.5% for other H15+. Female preponderance was seen in all headache types except TTH: migraine 35.7% vs. 15.1% (aOR = 3.3; p < 0.001); pMOH 4.3% vs. 0.7% (aOR = 5.1; p < 0.001); other H15 + 5.9% vs. 2.3% (aOR = 2.5; p = 0.08). One-day prevalence of (any) headache was 12.0%, based on reported HY. One-day prevalence predicted from 1-year prevalence and mean recalled headache frequency over 3 months was slightly lower (10.5%). Conclusions: The prevalences of migraine and TTH in Delhi and NCR substantially exceed global means. They closely match those in the Karnataka study: migraine 25.2%, TTH 35.1%. We argue that these estimates can reasonably be extrapolated to all India. [ABSTRACT FROM AUTHOR]

Background: The role of artificial intelligence and radiomics in prediction model development in cancer has been increasing every passing day. Cervical cancer is the 4th most common cancer in women worldwide, contributing to 6.5% of all cancer types. The treatment outcome of cervical cancer patients varies and individualized prediction of disease outcome is of paramount importance. Purpose: The purpose of this study is to develop and validate the digital signature for 5-year overall survival prediction in cervical cancer using robust CT radiomic and clinical features. Materials and Methods: Pretreatment clinical features and CT radiomic features of 68 patients, who were treated with chemoradiation therapy in our hospital, were used in this study. Radiomic features were extracted using an in-house developed python script and pyradiomic package. Clinical features were selected by the recursive feature elimination technique. Whereas radiomic feature selection was performed using a multi-step process i.e., step-1: only robust radiomic features were selected based on our previous study, step-2: a hierarchical clustering was performed to eliminate feature redundancy, and step-3: recursive feature elimination was performed to select the best features for prediction model development. Four machine algorithms i.e., Logistic regression (LR), Random Forest (RF), Support vector classifier (SVC), and Gradient boosting classifier (GBC), were used to develop 24 models (six models using each algorithm) using clinical, radiomic and combined features. Models were compared based on the prediction score in the internal validation. Results: The average prediction accuracy was found to be 0.65 (95% CI: 0.60– 0.70), 0.72 (95% CI: 0.63–0.81), and 0.77 (95% CI: 0.72–0.82) for clinical, radiomic, and combined models developed using four prediction algorithms respectively. The average prediction accuracy was found to be 0.69 (95% CI: 0.62–0.76), 0.79 (95% CI: 0.72–0.86), 0.71 (95% CI: 0.62–0.80), and 0.72 (95% CI: 0.66–0.78) for LR, RF, SVC and GBC models developed on three datasets respectively. Conclusion: Our study shows the promising predictive performance of a robust radiomic signature to predict 5-year overall survival in cervical cancer patients. [ABSTRACT FROM AUTHOR]

The early detection and accurate diagnosis of liver fibrosis, a progressive and potentially serious liver condition, are crucial for effective medical intervention. Invasive methods like biopsies for diagnosis can be risky and expensive. This research presents a novel computer-aided diagnosis model for liver fibrosis using a hybrid approach of minimum redundancy maximum relevance (MRMR) feature selection, bidirectional long short-term memory (BiLSTM), and convolutional neural networks (CNN). The proposed model involves multiple stages, including image acquisition, preprocessing, feature representation, fibrous tissue identification, and classification. Notably, histogram equalization is employed to enhance image quality by addressing variations in brightness levels. Performance evaluation encompasses a range of metrics such as accuracy, precision, sensitivity, specificity, F1 score, and error rate. Comparative analyses with established methods like DCNN, ANN-FLI, LungNet22, and SDAE-GAN underscore the efficacy of the proposed model. The innovative integration of hybrid MRMR-BiLSTM-CNN architecture and the horse herd optimization algorithm significantly enhances accuracy and F1 score, even with small datasets. The model tackles the complexities of hyperparameter optimization through the IHO algorithm and reduces training time by leveraging MRMR feature selection. In practical application, the proposed hybrid MRMR-BiLSTM-CNN method demonstrates remarkable performance with a 97.8% accuracy rate in identifying liver fibrosis images. It exhibits high precision, sensitivity, specificity, and minimal error rate, showcasing its potential for accurate and non-invasive diagnosis. [ABSTRACT FROM AUTHOR]

Water erosion creates adverse impacts on agricultural production, infrastructure, and water quality across the world, especially in hilly areas. Regional-scale water erosion assessment is essential, but existing models could have been more efficient in predicting the suspended sediment load. Further, data scarcity is a common problem in predicting sediment load. Thus, the current study aimed at modeling the suspended sediment yield of a hilly watershed (i.e., Bino watershed, Uttarakhand-India) using machine learning (ML) algorithms for a data-scarce situation. For this purpose, the ML models, viz., adaptive neuro-fuzzy inference system (ANFIS) and fuzzy logic (FL) were developed using data from ten years (2000–2009) only. Further, runoff and suspended sediment concentration (SSC) were obtained as the primary influencing factors. Varying combinations of lagged SSC and runoff data were considered as model inputs. The ANFIS and FL models were compared with the conventional multiple linear regression (MLR) model. Results indicated that the ANFIS model performed better than the FL and MLR models. Thus, it was concluded that the ANFIS model could be used as a benchmark for sediment yield prediction in hilly terrain in data-scarce situations. The research work would help field investigators in selecting the proper tool for estimating suspended sediment yield/load and policymakers to make appropriate decisions to reduce the devastating impact of soil erosion in hilly terrains. [ABSTRACT FROM AUTHOR]

• Indians develop ASCVD about a decade earlier than Western populations. • Subclinical ASCVD reflects the susceptibility to develop adverse CV events. • LAI recommends coronary artery calcium scoring to stratify ASCVD risk in selected patients. • LDL-C goals recommended by the LAI are more aggressive than other guidelines. • Extreme risk category C patients require the most intensive LDL-C lowering. In 2016, the Lipid Association of India (LAI) developed a cardiovascular risk assessment algorithm and defined low-density lipoprotein cholesterol (LDL-C) goals for prevention of atherosclerotic cardiovascular disease (ASCVD) in Indians. The recent refinements in the role of various risk factors and subclinical atherosclerosis in prediction of ASCVD risk necessitated updating the risk algorithm and treatment goals. The LAI core committee held twenty-one meetings and webinars from June 2022 to July 2023 with experts across India and critically reviewed the latest evidence regarding the strategies for ASCVD risk prediction and the benefits and modalities for intensive lipid lowering. Based on the expert consensus and extensive review of published data, consensus statement IV was commissioned. The young age of onset and a more aggressive nature of ASCVD in Indians necessitates emphasis on lifetime ASCVD risk instead of the conventional 10-year risk. It also demands early institution of aggressive preventive measures to protect the young population prior to development of ASCVD events. Wide availability and low cost of statins in India enable implementation of effective LDL-C-lowering therapy in individuals at high risk of ASCVD. Subjects with any evidence of subclinical atherosclerosis are likely to benefit the most from early aggressive interventions. This document presents the updated risk stratification and treatment algorithm and describes the rationale for each modification. The intent of these updated recommendations is to modernize management of dyslipidemia in Indian patients with the goal of reducing the epidemic of ASCVD among Indians in Asia and worldwide. [ABSTRACT FROM AUTHOR]

This paper explores the different insider employee-led cyber frauds (IECF) based on the recent large-scale fraud events of prominent Indian banking institutions. Examining the different types of fraud and appropriate control measures will protect the banking industry from fraudsters. In this study, we identify and classify Cyber Fraud (CF), map the severity of the fraud on a scale of priority, test the mitigation effectiveness, and propose optimal mitigation measures. The identification and classification of CF losses were based on a literature review and focus group discussions with risk and vigilance officers and cyber cell experts. The CF was analyzed using secondary data. We predicted and prioritized CF based on machine learning-derived Random Forest (RF). An efficient fraud mitigation model was developed based on an offender-victim-centric approach. Mitigation is advised both before and after fraud occurs. Through the findings of this research, banks and fraud investigators can prevent CF by detecting it quickly and controlling it on time. This study proposes a structured, sustainable CF mitigation plan that protects banks, employees, regulators, customers, and the economy, thus saving time, resources, and money. Further, these mitigation measures will improve the reputation of the Indian banking industry and ensure its survival. [ABSTRACT FROM AUTHOR]

Introduction: Data mining in healthcare is a nascent arena of research in healthcare. Heterogeneity of Diabetes Mellitus in terms of clinical presentation calls for newer methods of research to study potential risk factors. Aim: The paper aims to use clustering techniques to identify the relationship between the four variables, namely the pre-prandial and postprandial sugar level, age and sex. Methods: The data was taken from a diagnostic laboratory in Wagholi, Pune. We conducted K-mean algorithm, EM algorithm, model-based clustering and t-mixture model. Results: It is evidenced that the data was best fitted to the t-mixture model. Our 50% samples were people with diabetes, 17% had prediabetes. Trivial correlation existed between age and sugar level. Males and females were equally at risk of having diabetes. Data presented concludes that age and sex have no effect on the risk of having diabetes. Data mining can be used to deduce meaningful clusters to drive plan-based interventions in the population. Conclusion: Methods of data mining can be used to deduce meaningful clusters in a heterogeneous dataset thus providing policymakers and healthcare researchers with novel information that will potentially contribute in formulating evidence-based policies. [ABSTRACT FROM AUTHOR]

Early diagnosis and prognosis of deadly illnesses have been made possible by advancements in machine learning algorithms. In order to analyze patient pathology reports and make informed decisions regarding medicine supply and marketing strategies, pharmaceutical companies employ advanced data mining tools to generate statistical reports and extract valuable information. Our proposed system fulfils the requirement of patients as well as pharmaceutical clients that are concerned with the two diseases: diabetes mellitus and hypothyroidism. Generating statistical reports from the relevant data and providing an aerial view of the occurrence and spread of a disease in India. The diabetes mellitus and hypothyroidism prediction is carried out using three models: Support Vector Machine (SVM), Naive Bayes, and Random Forest (RF). The Random Forest model is the most appropriate for predicting diabetes mellitus with an accuracy of 90% and 98.05% for predicting hypothyroidism. Diabetes increases a patient's risk of heart disease, stroke and vision problems. Hence our findings help patients to take proactive care. [ABSTRACT FROM AUTHOR]

Background and introduction: Statisticians rank oral and lip cancer sixth in global mortality at 10.2%. Mouth opening and swallowing are challenging. Hence, most oral cancer patients only report later stages. They worry about surviving cancer and receiving therapy. Oral cancer severely affects QOL. QOL is affected by risk factors, disease site, and treatment. Using oral cancer patient questionnaires, we use light gradient Boost Tree classifiers to predict life quality. Methods: DIAS records were used for 111 oral cancer patients. The European Organisation for Research and Treatment of Cancer's QLQ-C30 and QLQ-HN43 were used to document the findings. Anyone could enroll, regardless of gender or age. The IHEC/SDC/PhD/OPATH-1954/19/TH-001 Institutional Ethical Clearance Committee approved this work. After informed consent, patients received the EORTC QLQ-C30 and QLQ-HN43 questionnaires. Surveys were in Tamil and English. Overall, QOL ratings covered several domains. We obtained patient demographics, case history, and therapy information from our DIAS (Dental Information Archival Software). Enrolled patients were monitored for at least a year. After one year, the EORTC questionnaire was retaken, and scores were recorded. This prospective analytical exploratory study at Saveetha Dental College, Chennai, India, examined QOL at diagnosis and at least 12 months after primary therapy in patients with histopathologically diagnosed oral malignancies. We measured oral cancer patients' quality of life using data preprocessing, feature selection, and model construction. A confusion matrix was created using light gradient boosting to measure accuracy. Results: Light gradient boosting predicted cancer patients' quality of life with 96% accuracy and 0.20 log loss. Conclusion: Oral surgeons and oncologists can improve planning and therapy with this prediction model. [ABSTRACT FROM AUTHOR]

Ensemble forecasts from multiple models have gained enormous popularity as it provides a more efficient forecast as compared to the individual counterpart. The linear weighted combination method is most widely utilized for its simplicity and efficiency. Despite hard efforts by various researchers, two considerable challenges still exist: (1) Systematic and robust techniques for selecting suitable forecasting models and (2) Techniques to find appropriate combination weights. To address these challenges a novel framework for optimization-based ensemble technique, 'OptiSembleForecasting' has been proposed in this study. The three components of the proposed framework are (a) Principal Component Analysis-based error index, (b) Model Confidence Set algorithm and (c) Optimization techniques. A total of thirteen forecasting models consisting of five deep learning, five machine learning and three stochastic models and twenty optimization techniques have been implemented in the proposed framework. To examine the effectiveness of the proposed technique, wholesale price of three commodities (TOP: Tomato, Onion, and Potato) each with two major markets in India has been considered. The empirical evaluation of the predictive accuracy of different models with that of the proposed techniques has been carried out by means of root mean square error and mean absolute percentage error. The findings of this study demonstrated the superiority of the proposed algorithm. Moreover, a R-package, namely 'OptiSembleForecasting', has been developed to make the implementation of this technique simple and user-friendly. [ABSTRACT FROM AUTHOR]

Investigation of the behaviour and complexity of hard-to-measure parameter time series is not explored to a greater extent before modelling a wastewater treatment facility (WWTF). In this context, A dynamic non-linear chaotic approach, namely the False nearest neighbour (FNN) algorithm, is employed for the first time to investigate the influent and effluent quality parameters of a WWTF. The primary objective of this research is to analyze the parameters of a WWTF located in India for its behaviour and complexity using the FNN algorithm. The autocorrelation function and average mutual information time lags are used as the delay time (τ) in the algorithm for phase space reconstruction and further FNN analysis. The optimum embedding dimensions (mopt) from FNN plots indicate the complexity or number of optimum variables required to model the time series. For influent and effluent parameters, the mopt values fall within a range of 4–15 and 4–17, respectively, and the τ value influences this range. Wastewater time series behaviour differs, such as pure stochastic or chaotic or chaotic series with noise, which is highly dependent on τ. The future scope of the study involves integrating the retrieved behaviour and complexity into state-of-the-art artificial intelligence or data-driven techniques to forecast hard-to-measure parameters. [ABSTRACT FROM AUTHOR]

Introduction: Child marriage, defined as marriage before the age of 18 years, is a precocious transition from adolescence to adulthood, which may take a long-term toll on health. Aim: This study aims to assess whether child marriage was associated with added risk of adverse cardiovascular outcomes in a nationally representative sample of Indian adults. Methods: Applying the non-laboratory-based Framingham algorithm to data on 336,953 women aged 30–49 years and 49,617 men aged 30–54 years, we estimated individual's predicted heart age (PHA). Comparing the PHA with chronological age (CA), we categorized individuals in four groups: (i) low PHA: PHA < CA, (ii) equal PHA: PHA = CA (reference category), (iii) high PHA: PHA > CA by at most 4 years, and (iv) very high PHA: PHA > CA by 5 + years. We estimated multivariable multinomial logistic regressions to obtain relative risks of respective categories for the child marriage indicator. Results: We found that women who were married in childhood had 1.06 (95% CI 1.01–1.10) and 1.22 (95% CI 1.16–1.27) times higher adjusted risks of having high and very high PHA, respectively, compared to women who were married as adults. For men, no differential risks were found between those who were married as children and as adults. These results were generally robust across various socioeconomic sub-groups. Conclusions: These findings add to the relatively new and evolving strand of literature that examines the role of child marriage on later life chronic health outcomes and provide important insights for public health policies aimed at improving women's health and wellbeing. [ABSTRACT FROM AUTHOR]

In schools, children are taught algorithm for extracting the square and cube roots of a number. However, they are not informed that Aryabhata (b. 476 CE) was the first Indian mathematician to present a systematic algorithm for the extraction of the square and cube root of a number. This algorithm has been succinctly presented by Aryabhata in two successive verses in the Ganitapada of his Aryabhatiyam composed in 499 CE. A careful study of the algorithm given by Aryabhata shows that it can be easily extended to find higher order roots. It also clearly reveals that the decimal place value system should have been in vogue in India for quite a long time before him, because this algorithm is heavily based on a thorough understanding of the decimal place value system. In our paper, we will discuss some of these aspects as well as outline how his algorithm can be extended to find the higher order roots. [ABSTRACT FROM AUTHOR]

The increasing amount of air pollution on a global scale is a major source of worry. The elevated levels of pollution are affecting the health of urban dwellers and motorists to a great extent. Since the situation has become so dire, it has become an absolute necessity to find effective ways to reduce air pollution and mitigate the negative effects of air pollution on human health. In this paper, the task of determining the least polluted route is addressed and how this can be used to curb pollution is discussed. The proposed work uses Dijkstra's algorithm, suggests a weight selection metric for air pollution parameters and analyzes the efficacy of the proposed algorithm in determining the least polluted route. The algorithm is tested on real-time data extracted from the Central Pollution Control Board, a statutory organization under the Ministry of Environment, Forest and Climate Change, Government of India. From the results obtained, it is found that the least polluted routes between a given source location and other different locations of a city can be determined using Dijkstra's algorithm accurately and the algorithm has a time complexity of O(V2). Further, using a least polluted route map will help motorists take less polluted routes, diverting air pollution off congested streets and enabling national governments to create policies like constructing flyovers or widening roads in increasingly polluted areas. [ABSTRACT FROM AUTHOR]

Deficits in social verbal communication in individuals with Social Communication Disorder (SCD) is of concern and SCD in the human community is prevalent in large population throughout the globe. Deficits in verbal social communication are prevalent in a large population. This paper aimed to propose internet connected multi-system architecture which is capable to support verbal communication in a social environment for individuals with social communication deficits. Implementation methodology was included with corpus collection for specific communication, deep learning based machine training for intelligent communication, and implementation of the trained algorithm on internet connected electronic multiple social communication devices. The implemented system is smart enough to initiate and maintain two types of communication; the first type includes communication between multiple individuals on the remote location and the second type includes communication with the individual present in the physical listening range. The system was investigated in terms of its algorithmic parameters and found 97% to 100% in terms of training and testing accuracy with negligible mean squared error. Vocal-Friend analysed results based on audio-bot simulative conditions provide more than 91% accuracy, interaction rate and fallback rate. On the basis of the satisfaction analysis, above average results were noticed. In terms of technical implementations and satisfaction analysis, results found acceptable with above average score. Proposed framework is easy to use by caregivers with even having little knowledge. Support individual with deficit to learn social verbal communication skill to survive in society. Aiding parents, caregivers and professionals to understand the communication needs of individuals with communication deficits. Since technology is also grooming in the domain of rehabilitation, so this system could be used in various future applications such as social robots, social virtual assistants etc. [ABSTRACT FROM AUTHOR]

Chronic pain has a huge global prevalence of 20% and in a country like India which has been labeled by the UN as the most populous country in April 2023, the problem is likely to explode and pose a huge challenge to Indian Practitioners. An addition to this can also be due to an expected doubling of individuals over 65 years of age by rises in the aging population. Pharmacotherapy which is the mainstay for conservative medical management (CMM) often seems to be inadequate and/or has a potential for a range of side effects. The patients who are refractory to all forms of CMM move up on the treatment algorithm for pain interventions. Implantable neuromodulation therapies can be of considerable help in patients who have significantly impaired quality of life due to unremitting severe pain, despite CMM and simpler pain interventions. These implanted devices provide ongoing satisfactory pain relief in select patients. This is for the 1st time that practice recommendations are published to guide practitioners offering this treatment in India. This can also be a reference for selecting the most appropriate patients to optimize outcomes. This can also provide motivation to start collecting information in their local database and also to convince our reimbursement agencies about incorporating this useful modality in the gamut of approved therapies. [ABSTRACT FROM AUTHOR]

The present world data is most important in public because without data cannot live in the world. This data is big data and that data daily increases through Social Media like "Twitter, Facebook, Youtube, WhatsApp, Instagram, LinkedIn", etc.., because this media only share public opinion very fast through the net. From this media, more people used especially Twitter. Thus media are used to analyze the public opinion of the tweets and predict the future through Machine Learning Algorithm. This analyzed data should make it polarity score. This score data has very little security because, this score can change the score and affected the future, so apply the existing security algorithms is Salsa and ChaCha. The Salsa algorithm is diagonal values moved to the first row. The ChaCha algorithm is diagonal values moved to the first column. The existing algorithms do not have good security because they focused only on performance, not security. So, the novel security algorithm is RPBB31. This algorithm has seven stages. The 1st stage is to find the secret key N, n, and p values from the matrix. The 2nd stage is to apply the secret key in PN(n) operation. The 3rd stage operation of PN(n) up to n = 1. The 4th stage is all PN(n) operations make their single line. The 5th stage is to pair the values and swap the values in the matrix. The 6th stage is column operations in the matrix. The 7th stage is again Step 4 values used to swap but "0th" cell value start from reverse in the matrix. In world cricket is the most famous game. In all social media Millions of people are following in different manner, especially in indian cricketer are more famous in India and world. In some point of time it may lead to match fixing, betting. To overcome this kind of issue the four datasets chosen. The proposed algorithm has provide good security and performance while compare to existing algorithms. [ABSTRACT FROM AUTHOR]

Introduction: The blood--brain barrier (BBB) is a semipermeable border that is responsible for maintaining central nervous system (CNS) homeostasis in the brain. Screening compounds based on their BBB permeability is an important consideration for CNS-acting drug development. Several studies have attempted to link physicochemical properties to specific BBB permeability; however, there is no widely accepted rule that can determine how and to what extent molecular properties affect the BBB permeability of compounds. To understand the complex phenomenon of BBB permeability, we explored the vast molecular space of the compounds to determine whether some features could differentiate the compounds based on their BBB permeability. Materials and Methods: A dataset of 1951 compounds containing 1246 BBB-permeable and 705 BBB-nonpermeable molecules was used in the study. Each compound encoded 499 molecular features. Feature selection was performed using feature selection algorithms, feature-to-feature, and feature-to-permeability correlation analysis. The findings of the feature selection algorithms were merged to select the best set of 53 features. Results: The descriptive analysis of the molecular features suggests that nCXr (number of X on ring C[sp3]) feature values for BBB nonpermeable compounds are zero for all considered compounds except for compounds with PubChem ID 71260, Flurithromycin. In addition, the majority of compounds were found to have nCrq (number of ring quaternary C[sp3]) feature values of zero for BBB nonpermeable compounds. For BBB-permeable compounds, MACCS fingerprints 8 feature values for all 1951 compounds were found to be zero except for the compound with PubChem ID 146291, Dezinamide. Conclusion: The descriptive and nonparametric tests confirm that molecular feature distributions are significantly different for BBB permeable and BBB nonpermeable compounds. [ABSTRACT FROM AUTHOR]

Background: To examine the effectiveness of a computer-assisted device (CAD) for diabetic retinopathy (DR) screening from retinal photographs at a vitreoretinal outpatient department (VR OPD), telecamps, and diabetes outpatient clinic by an ophthalmologist. In particular, the effectiveness of CAD in gradable and ungradable retinal images was examined. Methods: A total of 848 eyes of 485 patients underwent 45° retinal photographs at the VR OPD of a tertiary care hospital in southern India. A total of 939 eyes of 472 patients with diabetes were examined in the telecamps conducted in remote villages in Tamil Nadu, a state in southern India. A total of 2,526 eyes of 1,263 patients were examined in a diabetes clinic using 45° field retinal photographs. The algorithm was validated under physiological dilatation (without pharmacological dilatation) in all three arms. Results: Seventy-one percent of 848 eyes in VR OPD, 13% of 939 eyes in telecamps, and 7% of 2,526 eyes in diabetes clinic were diagnosed to have DR. The algorithm showed 78.3% sensitivity and 55.1% specificity for all images and 78.9% sensitivity and 56.8% specificity for gradable images in the VR OPD; 80.1% sensitivity and 79.2% specificity for all images and 84.8% sensitivity and 80.0% sensitivity for gradable images in telecamps; 63.0% sensitivity and 79.6% specificity for all images and 63.2% sensitivity and 78.1% specificity for gradable images in the diabetes clinic. The algorithm had an overall accuracy of 76.4%. The ungradable rate was variable. Conclusion: The algorithm performs equally well in identifying DR from gradable and ungradable photographs and may be used for DR screening in a rural setting with limited or no access to eye care. [ABSTRACT FROM AUTHOR]

[Purpose] How to prevent fake news without spoiling the freedom of speech is a growing concern among governments across the world. Some countries see legislation as being the best approach to counter fake news. In the legislation proposals, accountability is mostly placed on technology companies, but also individuals seem to have responsibility in the legislation of some countries. Some other governments see non-legislative means to counter fake news. But it's a fact that countering fake news without compromising free speech is a high priority across governments in the world and a challenging task too. This paper investigates the India scenario and tries to list out other than legislation what other measures are required. [Methodology] This paper takes a survey of mitigation efforts in select countries. This survey is used to testify against similar efforts in India, if any and adopts comparative approach to understand where Indian efforts stand at. [Findings] From using fact-checking tools available online, finding the source, locating how many people viewed a particular story to check grammar and spelling, and developing a critical mindset; plenty of things become a critical means in fighting down fake news. Legislation alone is insufficient. Media literacy, public scrutiny, good citizenship, and education along with sensitive civil society require playing its significant part in India to fight fake news. In India, the policy is vague. It gives the government enormous power to surveillance in the name of fake news. [ABSTRACT FROM AUTHOR]

Aim The aim of this study was to determine the changes in the mean arterial blood pressure (MAP) and mean uterine artery (UtA) pulsatility index (PI) from 11-14 to 19-24 + 6 gestation weeks in Asian Indian pregnant women. Methods Clinical and demographic details, MAP, and mean UtA PI measures were ascertained for pregnant women at 11 to 14 gestation weeks and 19–24 + 6 gestation weeks. Women were categorized as a high-or-low risk for preterm preeclampsia using the Fetal Medicine Foundation algorithm and 1 in 150 cutoff. High-risk pregnant women were recommended low-dose aspirin 150 mg daily at bedtime. Changes in MAP and mean UtA PI were compared for gestational age intervals and high-and-low risk women using nonparametric tests. Results The study analyzed the results of 1,163 pregnant women. Both MAP (mean difference: 5.14, p < 0.001) and mean UtA PI (mean difference: 0.14, p < 0.001) remained significantly higher at the second-trimester assessment in high-risk pregnant women compared to low-risk pregnant women. Seventy-seven (35.16%) of the 219 pregnant women with abnormal mean UtA PI in the first trimester had an abnormal mean UtA PI in the second-trimester assessment. One hundred (10.59%) of the 944 pregnant women with normal mean UtA PI in the first trimester had an abnormal mean UtA PI in the 19-24 + 6 weeks assessment. Seventy-seven pregnant women (6.62% of 1,163 women, 95% confidence interval: 5.33, 8.20) had an abnormal mean UtA PI at both gestation age intervals. High-risk pregnant women taking low-dose aspirin daily showed a larger reduction in mean UtA PI compared to high-risk pregnant women that did not report the use of low-dose aspirin (0.89 vs. 0.62, p <0.001) Conclusion MAP and mean UtA PI decreased significantly from the first to the second trimester of pregnancy. Sequential assessment of the MAP and mean UtA PI in the first and second trimesters of pregnancy will be useful for fetal radiologists in India to identify a subgroup of women with abnormal mean UtA PI at both trimesters that may need more intense surveillance and follow-up till childbirth. [ABSTRACT FROM AUTHOR]

Recently gravity data modeling plays an important role in the study of volcanic activity and geothermal investigation. Generally, gravity data modeling assumes the subsurface either homogenous or spatially variable densities within modeled source rocks and surrounding sediments. As a result, the subsurface geothermal and volcanic goals and targets are included and validated using simple-geometric sources in gravity data modeling. The Bat algorithm, which is considered one of the most recently, developed metaheuristic algorithms in geophysics applications, permits to discovery and delineation of the source's parameters. Here, we presented the contribution of the Bat algorithm technique in elucidating 2D gravity profiles for geothermal exploration and volcanic activity cases. The Bat algorithm is based on the echo-location behavior of bats to perform global optimization. The Bat optimization algorithm is applied to 2D gravity data to estimate the source's parameters such as depth, origin location, amplitude factor, and geometric shape of the causative buried body. The stability and efficiency of the introduced optimizing algorithm were checked to different synthetic cases, i.e., for model 1, which represents a horizontal cylinder model, and model 2 represents a multi-sources effect. Furthermore, the successful applications of the proposed algorithm for discovering the geothermal and volcanic activities in Japan and India were have presented. The obtained results are in good agreement with the available geological, geophysical, and borehole information. [ABSTRACT FROM AUTHOR]

Monitoring the Earth's surface and objects is important for many applications, such as managing natural resources, crop yield predictions, and natural hazard analysis. Remote sensing is one of the most efficient and cost-effective solutions for analyzing land-use and land-cover (LULC) changes over the Earth's surface through advanced computer algorithms, such as classification and change detection. In the past literature, various developments were made to change detection algorithms to detect LULC multitemporal changes using optical or microwave imagery. The optical-based hyperspectral highlights the critical information, but sometimes it is difficult to analyze the dataset due to the presence of atmospheric distortion, radiometric errors, and misregistration. In this work, an artificial neural network-based post-classification comparison (ANPC) as change detection has been utilized to detect the muti-temporal LULC changes over a part of Uttar Pradesh, India, using the Hyperion EO-1 dataset. The experimental outcomes confirmed the effectiveness of ANPC (92.6%) as compared to the existing models, such as a spectral angle mapper (SAM) based post-classification comparison (SAMPC) (89.7%) and k-nearest neighbor (KNN) based post-classification comparison (KNNPC) (91.2%). The study will be beneficial in extracting critical information about the Earth's surface, analysis of crop diseases, crop diversity, agriculture, weather forecasting, and forest monitoring. [ABSTRACT FROM AUTHOR]

Chronic wounds are associated with significant clinical, economic and quality‐of‐life burden. Despite the variety of wound imaging systems available in the market for wound assessment and surveillance, few are clinically validated among patients of Asian ethnicity. We aimed to clinically validate the accuracy of a smartphone wound application (Tissue Analytics [TA], Net Health Systems Inc, Florida, USA), versus conventional wound measurements (visual approximation and paper rulers), in patients of Asian ethnicity with venous leg ulcers (VLU). A prospective cohort study of patients presenting with VLU to a specialist wound nurse clinic over a 5‐week duration was conducted. Each patient received seven wound measurements: one by a trained wound nurse clinician, and three separate wound measurements using TA on each of the iOS and Android operating systems. Inter‐rater and intra‐rater reliability between clinical and TA‐based measurements were analysed using intra‐class correlation statistics, with values of <0.5, 0.5 to 0.75, 0.75 to 0.9, and >0.9 indicating poor, moderate, good and excellent reliability, respectively. 82 patients (51% males), with a mean age at 65.8 years, completed the 5‐week study duration. 25 (30%) had underlying diabetes mellitus. Chinese, Malay and Indian ethnicity comprised 68%, 12% and 11%, respectively. The VLU healed in 26 (32%) of patients within the study period. In total, 358 wound episodes with 2334 wound images were analysed. Inter‐rater reliability for length, width and area between wound nurse measurements and TA application measurements was good (range 0.799‐0.919, P < 0.001). Separate measurements of intra‐rater reliability for length, width and area within the iOS or Android systems were excellent (range 0.967‐0.985 and range 0.977‐0.984 respectively, P < 0.001). Inter‐rater reliability between TA used on the iOS and Android systems was also excellent (0.987‐0.989, P < 0.001). Tissue Analytics, a smartphone wound application, is a useful adjunct for wound assessment and surveillance in VLU patients of Asian ethnicity. [ABSTRACT FROM AUTHOR]

Although the genome sequence of cucumber is publicly available, only a limited number of functional markers are in store for developing gynoecious hybrids using Indian genotypes. This study reported novel SNPs and InDels in the exonic regions of genes involved in gynoecy using two parents and their hybrid with genotyping-by-sequencing (GBS) by generating 3.547 Gb of raw data. Using NSDC reference genome GCA_000004075.2, a total of 40,143, 181,008 and 43,612 SNPs were identified, among which 514 were polymorphic between male and female parents but monomorphic between the male parent and the hybrid (confirming hybridity). We further identified that, out of those 514 SNPs, 74 were within the exonic regions of the sex-specific genes. The most interesting functional marker in this study was SNP 2,13,85,488, identified in the gene CsaV3_6G037780 G3I-38214 on chromosome 6, encoding 1-aminocyclopropane-1-carboxylate oxidase 1 (ACS1), which plays a key role in female flower production, as indicated in CuCyc with the PathoLogic algorithm. The InDel analysis also identified a variation inside the gene CsaV3_6G304050 G3I-37940, encoding histone lysine N-methyl transferase, involved in flowering and female gametophyte development. Thus, this study has identified gynoecy-specific functional markers; upon further validation, these markers will accelerate the evolution of gynoecious hybrids in India and global cucumber breeding programs. [ABSTRACT FROM AUTHOR]

False information is becoming more frequent in distributing disinformation by distorting people's awareness and decision-making by altering their views or knowledge. The propagation of disinformation has been aided by the proliferation of social media and online forums. Allowing it to readily blend in with true information. Parody news and rumors are the most common types of misleading and unverified information, and they should be caught as soon as possible to avoid their disastrous consequences. As a result, in recent years, there has been a surge in interest in effective detection approaches. For this study, a customized dataset was built that included both real and parody tweets from Pakistan and India. This study proposes a two-step strategy for detecting parody tweets. In the first stage of the approach the unstructured data is converted into structured data set. In the second step, multiple supervised artificial intelligence algorithms were employed. An experimental assessment of the different classification methods inside a customized dataset was undertaken in this study, and these classification models were compared using evaluation metrics. Our results showed accuracy of 92%. [ABSTRACT FROM AUTHOR]

Background: Vaccination is a safe and effective way to prevent disease and save lives, but it may also produce some undesirable adverse events (AEs)which may affect healthy individuals. Therefore, the monitoring of AE following immunization (AEFIs) is necessary. The objective of this study was to assess the AEs following COVID-19 vaccinations in a tertiary care hospital. Methodology: The study was conducted as active vaccine safety surveillance for a period of 6 months among the COVID-19 vaccine beneficiaries of the study site. Active surveillance was conducted via initiating two telephone contacts. The first surveillance was conducted in 8 days and the second surveillance after 28 days of post-vaccination. All identified AEs following immunizations (AEFIs) were reported and analysed by the AEFI investigation team at the study site. The causality assessment of each identified AEFI was performed using the World Health Organization's causality assessment algorithm. Results: A total of 2927 enrolled study population completed the study with a response rate of 80.85%. The study identified 902 AEFIs from 614 study populations with an incidence rate of 20.97%. Of which 794 and 79 AEFIs were associated with COVISHIELD™ and COVAXIN®, respectively. The majority of the events were reported among the age group of 18–29 years. Overall, only three events were serious and no deaths were reported among the study population. A total of 75.59% of events had a consistent causal association with vaccination and were categorized as vaccine product-related reactions. The study identified various factors such as gender (p = 0.019), age (p < 0.05), co-morbid status (p = 0.032) and dose number (p = 0.001) as potential predictors for development of AEFI. Conclusion: The study identified only 0.33% of events as serious, and 99.67% of the study population recovered from the AEFIs, which reveals that COVISHIELD™ and COVAXIN® have a generally favourable safety profile. However, close monitoring is required to identify the potential signals, as the safety data from the clinical trials are limited. [ABSTRACT FROM AUTHOR]

The aquaculture expansion has made significant contributions to global food security, socio-economic development and, if implemented sustainably, can help preserve stable coastal environments. This study explicitly details the rapid expansion of largescale aquaculture growth across the Kolleru and Upputeru regions of South India. We developed a novel classification method for automated extraction of aquaculture ponds in the Kolleru zone using the Canny Edge-Otsu algorithm to segment and extract the ponds applied to SAR-VV images in Google Earth Engine. This approach enables the area estimation of dense aquaculture ponds are essential for monitoring and management purposes. The results indicated that this method could effectively map the aquaculture ponds and the overall accuracy achieved in 2020 for the Kolleru and Upputeru areas by 90.6% and 95.7%, respectively. The aquaculture maps of this study can help government organizations, resource managers, stakeholders, and decision-makers understand the dynamics and plan sustainable measures in this area. [ABSTRACT FROM AUTHOR]

The road is the most commonly used means of transportation and serves as a country's arteries, so it is extremely important to keep the roads in good condition. Potholes that happen to appear in the road must be repaired to keep the road in good condition. Spotting potholes on the road is difficult, especially in a country like India where roads stretch millions of kilometres across the country. Therefore, there is a need to automate the identification of potholes with high speed and real-time precision. YOLOX is an object detection algorithm and our main goal of this article is to train and analyse the YOLOX model for pothole detection. The YOLOX model is trained with a pothole dataset and the results obtained are analysed by calculating the accuracy, recall and size of the model which is then compared to other YOLO algorithms. The experimental results in this article show that the YOLOX-Nano model predicts potholes with higher accuracy compared to other models while having low computational costs. We were able to achieve an Average Precision (AP) value of 85.6% from training the model and the total size of the model is 7.22 MB. The pothole detection capabilities of the newly developed YOLOX algorithm have never been tested before and this paper is one of the first to detect potholes using the YOLOX object detection algorithm. The research conducted in this paper will help reduce costs and increase the speed of pothole identification and will be of great help in road maintenance. [ABSTRACT FROM AUTHOR]

Rivers are dynamic geological agents on the earth which transport the weathered materials of the continent to the sea. Estimation of suspended sediment yield (SSY) is essential for management, planning, and designing in any river basin system. Estimation of SSY is critical due to its complex nonlinear processes, which are not captured by conventional regression methods. Rainfall, temperature, water discharge, SSY, rock type, relief, and catchment area data of 11 gauging stations were utilized to develop robust artificial intelligence (AI), similar to an artificial-neural-network (ANN)-based model for SSY prediction. The developed highly generalized global single ANN model using a large amount of data was applied at individual gauging stations for SSY prediction in the Mahanadi River basin, which is one of India's largest peninsular rivers. It appeared that the proposed ANN model had the lowest root-mean-squared error (0.0089) and mean absolute error (0.0029) along with the highest coefficient of correlation (0.867) values among all comparative models (sediment rating curve and multiple linear regression). The ANN provided the best accuracy at Tikarapara among all stations. The ANN model was the most suitable substitute over other comparative models for SSY prediction. It was also noticed that the developed ANN model using the combined data of eleven stations performed better at Tikarapara than the other ANN which was developed using data from Tikarapara only. These approaches are suggested for SSY prediction in river basin systems due to their ease of implementation and better performance. [ABSTRACT FROM AUTHOR]

Background Blood-based biomarkers for diagnosing active tuberculosis (TB), monitoring treatment response, and predicting risk of progression to TB disease have been reported. However, validation of the biomarkers across multiple independent cohorts is scarce. A robust platform to validate TB biomarkers in different populations with clinical end points is essential to the development of a point-of-care clinical test. NanoString nCounter technology is an amplification-free digital detection platform that directly measures mRNA transcripts with high specificity. Here, we determined whether NanoString could serve as a platform for extensive validation of candidate TB biomarkers. Methods The NanoString platform was used for performance evaluation of existing TB gene signatures in a cohort in which signatures were previously evaluated on an RNA-seq dataset. A NanoString codeset that probes 107 genes comprising 12 TB signatures and 6 housekeeping genes (NS-TB107) was developed and applied to total RNA derived from whole blood samples of TB patients and individuals with latent TB infection (LTBI) from South India. The TBSignatureProfiler tool was used to score samples for each signature. An ensemble of machine learning algorithms was used to derive a parsimonious biomarker. Results Gene signatures present in NS-TB107 had statistically significant discriminative power for segregating TB from LTBI. Further analysis of the data yielded a NanoString 6-gene set (NANO6) that when tested on 10 published datasets was highly diagnostic for active TB. Conclusions The NanoString nCounter system provides a robust platform for validating existing TB biomarkers and deriving a parsimonious gene signature with enhanced diagnostic performance. [ABSTRACT FROM AUTHOR]

Communication establishment is crucial for rescue operations in disaster affected areas. A standard tool for communication is the use of cell phones. However, they can be useless in situations where the cellular network's base stations are damaged in a disaster. A contemporary approach to re-establishing a communication network is by hosting base stations in drones. However, low battery life and difficulty in calculating the number of drones needed in different terrains are limitations of the above approach. This paper introduces a novel terrain-aware algorithm that calculates the minimum number of drones needed to cover an area with no voids in the network coverage. Our method ensures that the drones are deployed at optimal heights to maximize the average leftover energy in the network. We apply the algorithm for an actual location in Pettimudi, India and find the optimal number and positions of the drones to cover the area effectively without voids. In addition, we provide a simulation of the the communication establishment using above drones, and our experiments yield an average network efficiency of 98%, showing the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Mesothelioma is a form of cancer that is aggressive and fatal. It is a thin layer of tissue that covers the majority of the patient's internal organs. The treatments are available; however, a cure is not attainable for the majority of patients. So, a lot of research is being done on detection of mesothelioma cancer using various different approaches; but this paper focuses on optimization techniques for optimizing the biomedical images to detect the cancer. With the restricted number of samples in the medical field, a Relief-PSO head and mesothelioma neck cancer pathological image feature selection approach is proposed. The approach reduces multilevel dimensionality. To begin, the relief technique picks different feature weights depending on the relationship between features and categories. Second, the hybrid binary particle swarm optimization (HBPSO) is suggested to automatically determine the optimum feature subset for candidate feature subsets. The technique outperforms seven other feature selection algorithms in terms of morphological feature screening, dimensionality reduction, and classification performance. [ABSTRACT FROM AUTHOR]

This study was designed to predict the post-weaning weights of Akkaraman lambs reared on different farms using multiple linear regression and machine learning algorithms. The effect of factors the age of the dam, gender, type of lambing, enterprise, type of flock, birth weight, and weaning weight was analyzed. The data was collected from a total of 25,316 Akkaraman lambs raised at multiple farms in the Çiftlik District of Niğde province. Comparative analysis was conducted by using multiple linear regression, Random Forest, Support Vector Machines (and Support Vector Regression), Extreme Gradient Boosting (XGBoost) (and Gradient Boosting), Bayesian Regularized Neural Network, Radial Basis Function Neural Network, Classification and Regression Trees, Exhaustive Chi-squared Automatic Interaction Detection (and Chi-squared Automatic Interaction Detection), and Multivariate Adaptive Regression Splines algorithms. In this study, the test dataset was divided into five layers using the K-fold cross-validation method. The performance of models was compared using performance criteria such as Adjusted R-squared (Adj-[Formula: see text]), Root Mean Square Error (RMSE), Mean Absolute Deviation (MAD), and Mean Absolute Percentage Error (MAPE) by utilizing test populations in the predicted models. Additionally, the presence of low standard deviations for these criteria indicates the absence of an overfitting problem. [Formula: see text]The comparison results showed the Random Forest algorithm had the best predictive performance compared to other algorithms with Adj-[Formula: see text], RMSE, MAD, and MAPE values of 0.75, 3.683, 2.876, and 10.112, respectively. In conclusion, the results obtained through Multiple Linear Regression for the live weights of Akkaraman lambs were less accurate than the results obtained through artificial neural network analysis., (© 2024. The Author(s).)

Gravimetry-based direct measurements of mass concentration require offline analysis which is not suited for field campaigns. Hence such campaigns rely on the estimation of mass concentration by indirect methods mostly calibrated in controlled laboratory conditions. Optical particle counter (OPC) employs algorithms converting the measured number concentration to mass concentration using appropriate conversion factors. The accuracy of such conversion has not been validated for widely varying atmospheric conditions. This study compares the mass concentration estimated by OPC with those directly obtained from gravimetry-based instruments for outdoor samples collected in Bathinda City, Punjab, India from January 2022 to November 2023. The difference in the gravimetrically measured and OPC predicted values quantified in terms of ratios (gravimetric to optically estimated mass concentration), came out to be 1.42 ± 0.77, 0.99 ± 0.51, and 1.17 ± 0.58 for PM10, PM2.5 and PM1, respectively. This difference when estimated with the back-up filter of OPC itself (C Factor), was 1.37 ± 0.66. More than half of the samples showed ratios outside the 0.8-1.2 range thus indicating under or over-estimation in the OPC predicted values. The probable role of variation in density, shape, and refractive index of atmospheric aerosol particles towards the observed inaccuracy of estimated mass concentration has been highlighted. In the absence of clear guidelines and protocols, the study suggests ways to improve the accuracy via periodic measurement of the C Factor and/or incorporating calibration factors in such measurements., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: The Co-Authors Ankita Buwaniwal, Veena Sharma, Gagan Gupta and Sandeep Kansal received a grant from Board of Research in Nuclear Sciences (BRNS-DAE), Govt. of India for financial assistance (Project Sanction No. 51/14/02/2020-BRNS/36091)., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Purpose: This study aimed to develop a classifier using supervised machine learning to effectively assess the impact of clinical, demographical, and biochemical factors in accurately predicting the antiseizure medications (ASMs) treatment response in people with epilepsy (PWE)., Methods: Data was collected from 786 PWE at the Outpatient Department of Neurology, Institute of Human Behavior and Allied Sciences (IHBAS), New Delhi, India from 2005 to 2015. Patients were followed up at the 2nd, 4th, 8th, and 12th month over the span of 1 year for the drugs being administered and their dosage, the serum drug levels, the frequency of seizure control, drug efficacy, the adverse drug reactions (ADRs), and their compliance to ASMs. Several features, including demographic details, medical history, and auxiliary examinations electroencephalogram (EEG) or Computed Tomography (CT) were chosen to discern between patients with distinct remission outcomes. Remission outcomes were categorized into 'good responder (GR)' and 'poor responder (PR)' based on the number of seizures experienced by the patients over the study duration. Our dataset was utilized to train seven classical machine learning algorithms i.e Extreme Gradient Boost (XGB), K-Nearest Neighbor (KNN), Support Vector Classifier (SVC), Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB) and Logistic Regression (LR) to construct classification models., Results: Our research findings indicate that 1) among the seven algorithms examined, XGB and SVC demonstrated superior predictive performances of ASM treatment outcomes with an accuracy of 0.66 each and ROC-AUC scores of 0.67 (XGB) and 0.66 (SVC) in distinguishing between PR and GR patients. 2) The most influential factor in discerning PR to GR patients is a family history of seizures (no), education (literate) and multitherapy with Chi-square (χ2) values of 12.1539, 8.7232 and 13.620 respectively and odds ratio (OR) of 2.2671, 0.4467, and 1.9453 each. 3). Furthermore, our surrogate analysis revealed that the null hypothesis for both XGB and SVC was rejected at a 100 % confidence level, underscoring the significance of their predictive performance. These findings underscore the robustness and reliability of XGB and SVC in our predictive modelling framework., Significance: Utilizing XG Boost and SVC-based machine learning classifier, we successfully forecasted the likelihood of a patient's response to ASM treatment, categorizing them as either PR or GR, post-completion of standard epilepsy examinations. The classifier's predictions were found to be statistically significant, suggesting their potential utility in improving treatment strategies, particularly in the personalized selection of ASM regimens for individual epilepsy patients., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Predicting the injury severities sustained by drivers engaged in road traffic accidents is a key topic of research in road traffic safety. The current study analyzed the driver injury severity (DIS) using twelve machine learning (ML) algorithms. These models were implemented using 0.70, 0.80, and 0.90 train ratios and 5-, 10- and 15-fold cross-validation. Ten years of accident data (from 2011 to 2020) was obtained from police department of Shillong, India. A total of 693 accidents were documented, with 68% being nonfatal and 32% being fatal. Precision, recall, accuracy, F 1 score and area under the curve measures were used to compare the performance of all twelve ML models. Overall, the light gradient-boosting machine model was shown to be the best ML model for predicting the injury severities of drivers engaged in road traffic incidents. Finally, variable importance analysis results showed that cause of accident, collision type and types of vehicles were the most influencing factors in nonfatal and fatal driver accidents. The results also revealed that age and gender were slightly associated with DIS. The findings of the current research could be helpful to road safety agencies for the implementation of suitable countermeasures to increase driver safety in road accidents.

In India, the prevailing approach to eye lens dosimetry is the placement of an existing dosemeter on the forehead region after slight modification serves as a dedicated Eye Lens Dosemeter. A methodology for estimating the eye lens dose in terms of the Hp(3) has been previously explored employing an algorithm based on the response characteristics of this dosemeter using ISO slab phantom. It was observed that the performance of the dosemeter in terms of Hp(3) using previous algorithm showed under response at higher angles of incidence and photon beams of energy < 200 keV. Further, study was conducted to modify the algorithm following the latest ISO recommendations. This involved generation of data from the response of existing dosemeter on a cylindrical phantom. The results of this study revealed better performance of the newly established algorithm in estimating eye lens dose in terms of Hp(3) when compared to the earlier algorithm., (© The Author(s) 2024. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

India has been dealing with fluoride contamination of groundwater for the past few decades. Long-term exposure of fluoride can cause skeletal and dental fluorosis. Therefore, an in-depth exploration of fluoride concentrations in different parts of India is desirable. This work employs machine learning algorithms to analyze the fluoride concentrations in five major affected Indian states (Andhra Pradesh, Rajasthan, Tamil Nadu, Telangana and West Bengal). A correlation matrix was used to identify appropriate predictor variables for fluoride prediction. The various algorithms used for predictions included K-nearest neighbor (KNN), logistic regression (LR), random forest (RF), support vector classifier (SVC), Gaussian NB, MLP classifier, decision tree classifier, gradient boosting classifier, voting classifier soft and voting classifier hard. The performance of these models is assessed over accuracy, precision, recall and error rate and receiver operating curve. As the dataset was skewed, the performance of models was evaluated before and after resampling. Analysis of results indicates that the RF model is the best model for predicting fluoride contamination in groundwater in Indian states., Competing Interests: The authors declare there is no conflict., (© 2024 The Authors This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY-NC-ND 4.0), which permits copying and redistribution for non-commercial purposes with no derivatives, provided the original work is properly cited (http://creativecommons.org/licenses/by-nc-nd/4.0/).)