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Artificial Intelligence is quickly emerging as a technological solution for the agriculture industry to surmount its classical challenges. Artificial Intelligence is facilitating farmers to refine their products and alleviate unfavourable impacts due to the environment. The central concern of this paper is predictive analytics to develop a machine learning model to identify and predict crop yield based on multiple environmental factors. In this paper, a hybrid learner 'RaNN' is proposed that combines the feature sampling and majority voting technique of Random Forest in-combination with the multilayer Feedforward Neural Network to predict the crop yield. Research has also ascertained the essential features responsible for accurate yield prediction. The proposed model works for rice yield prediction, one of the chief grains of India. The region chosen for the work is Punjab, which is among the largest producer states of India for rice. The dataset consists of 15 attributes comprising the weather and agriculture data collected from the Indian Meteorological Department Pune, and Punjab Environment Information System (ENVIS) Center, Government of India. The study has also made a comparative assessment of 'RaNN' with machine learning methods like Multiple Linear Regression, Random Forest, Decision Tree, Boosting Regression, Support Vector Machine Regression, Ensemble Learner, and Artificial Neural Network. Our model RaNN has listed a better prediction accuracy with minimal error among the other techniques providing a 98% correlation between the actual and the predicted yield. Abbreviations: AI – Artificial Intelligence; ANN – Artificial Neural Network; BR – Boosting Regression; Chem Fert – chemical fertilisers; DT – Decision Tree; EL – Ensemble Learner; ENVIS – Punjab Environment Information System; GBM – Stochastic Gradient Boosting Method; GPS – Global Positioning System; HMAX – highest maximum temperature in degrees C; IMD – Indian Meteorological Department; L1 – Lasso regression; L2 – Ridge regression; LMIN – lowest minimum temperature; ML – Machine Learning; MAE – Mean Absolute Error; MEVP – mean evaporation in mm; MLR – Multiple Linear Regression; MMAX – mean maximum temperature in degrees C; MMIN – mean minimum temperature in degrees C; MSSH – Mean sunshine duration in hours; MWS – mean wind speed in km/h; P1 – number of days with precipitation (0.1–0.2 mm); P2 – number of days with precipitation (greater than or equal to 0.3 mm); RaNN – Hybrid RF-ANN model; RMSE – Root Mean Squared Error; $${R^2}$$ R 2 – Coefficient of determination; RD – number of rainy days; RF – Random Forest; SVM Reg – Support Vector Machine Regression; TMRF – total rainfall per month in mm [ABSTRACT FROM AUTHOR]

Background: The purpose of this study is to present a comprehensive overview of the ethical issues and the processes involved in research and publishing in India. The study examines the present ethical norms, guidelines, frameworks and developments in India, providing insights into the nation's current status of research and recommendations for publication. This document will be a useful starting point and reference document for those embarking on research and publication in Orthopaedics in India. Materials: A survey of the literature was done, which included scholarly papers, reports, rules, and policies pertaining to Indian publishing norms and research ethics. the document starts with a general introduction to ethics, followed by the evolution of ethics in research and the current International as well as Indian codes of ethics. Subsequently, the discussion is divided into two broad headings of ethics in research and ethics in publishing. Under each heading, there are many specific areas in orthopaedics that would require the application of a unique set of ethics. These areas are discussed separately as subheadings. Results and Discussion: The review draws attention to the complexity of ethical issues in Indian and international research and publishing in orthopaedics. Where available, specific guidelines about the topic in India or international guidelines are discussed. The importance of informed consent, data integrity, plagiarism, authorship disputes, and conflicts of interest are only a few of the key results. It is obvious that ethical norms and regulations, such as those offered by the University Grants Commission (UGC), the Indian Council of Medical Research (ICMR), and the Council of Publication Ethics (COPE) are crucial in determining how research is conducted and how papers are published. The types of studies discussed include research in humans and animals, research with stem cells, metal implants and devices, orthobiologics, Artificial Intelligence, Robotics, computer modelling, virtual reality, 3D printing and bioprinting, tissue banking and data management. The roles of different personnel in research and publications are discussed. Conclusions: Ethics in research and publishing play a crucial role in establishing the authority and standard of scholarly work in India. This study underlines the key concepts of ethics that guide various types of studies and the publication process. It also highlights the requirement for frameworks and guidelines for certain unique areas of research in orthopaedics. [ABSTRACT FROM AUTHOR]

Introduction: The emergence of artificial intelligence (AI) chat programs has opened two distinct paths, one enhancing interaction and another potentially replacing personal understanding. Ethical and legal concerns arise due to the rapid development of these programs. This paper investigates academic discussions on AI in medicine, analyzing the context, frequency, and reasons behind these conversations. Methods: The study collected data from the Web of Science database on articles containing the keyword "ChatGPT" published from January to September 2023, resulting in 786 medically related journal articles. The inclusion criteria were peer-reviewed articles in English related to medicine. Results: The United States led in publications (38.1%), followed by India (15.5%) and China (7.0%). Keywords such as "patient" (16.7%), "research" (12%), and "performance" (10.6%) were prevalent. The Cureus Journal of Medical Science (11.8%) had the most publications, followed by the Annals of Biomedical Engineering (8.3%). August 2023 had the highest number of publications (29.3%), with significant growth between February to March and April to May. Medical General Internal (21.0%) was the most common category, followed by Surgery (15.4%) and Radiology (7.9%). Discussion: The prominence of India in ChatGPT research, despite lower research funding, indicates the platform's popularity and highlights the importance of monitoring its use for potential medical misinformation. China's interest in ChatGPT research suggests a focus on Natural Language Processing (NLP) AI applications, despite public bans on the platform. Cureus' success in publishing ChatGPT articles can be attributed to its open-access, rapid publication model. The study identifies research trends in plastic surgery, radiology, and obstetric gynecology, emphasizing the need for ethical considerations and reliability assessments in the application of ChatGPT in medical practice. Conclusion: ChatGPT's presence in medical literature is growing rapidly across various specialties, but concerns related to safety, privacy, and accuracy persist. More research is needed to assess its suitability for patient care and implications for non-medical use. Skepticism and thorough review of research are essential, as current studies may face retraction as more information emerges. [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]

The growing incorporation of Artificial Intelligence (AI) in healthcare presents significant opportunities for transforming medical diagnosis, treatment, and accessibility, especially in countries with diverse populations such as India. Nevertheless, there are significant ethical concerns that accompany these commitments, specifically related to data privacy, algorithmic bias, and the potential erosion of human agency. This paper addresses these challenges by presenting a comprehensive policy framework for the responsible implementation of AI in the healthcare sector in India. The framework prioritizes values such as transparency, inclusivity, and public trust. The framework promotes the use of strong data governance mechanisms, such as informed consent, data anonymization, and responsible data sharing practices, based on the most favorable outcomes for ethical AI. In order to address algorithmic bias, it is crucial to conduct regular audits, employ bias mitigation techniques, and implement explainable AI models. Moreover, it advocates for a methodology that prioritizes the needs and well-being of individuals, fostering cooperation between AI and healthcare experts, while also valuing the independence of patients through transparent communication and honoring their decisions. The framework suggests the creation of a specialized regulatory entity with a diverse composition to formulate and enforce ethical standards, oversee adherence, and promptly address complaints. Lastly, recognizing the vital importance of public awareness, it underscores the need for extensive training and development of healthcare professionals, policymakers, and the general public. This will promote well-informed discussions and help alleviate any potential societal concerns. This paper seeks to establish a clear ethical framework and propose a detailed policy structure to enable the use of AI in Indian healthcare. The objective is to create a future where AI can effectively improve healthcare access, reduce disparities, enhance medical results, and ultimately prioritize the well being of patients and society. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to establish and empirically investigate a research model examining the effect of four dimensions of the technology readiness index – optimism, innovativeness, discomfort and insecurity – on customer engagement that further influences purchase intention in the context of online shopping through artificial intelligence voice assistants (AI VAs). Design/methodology/approach: Data were collected in India from 429 customers in a self-administered online survey. Data analysis uses the structural equation modelling technique. Findings: Technology readiness dimensions, e.g. optimism, innovativeness, discomfort and insecurity, are critical factors driving customer engagement. Customer engagement further results in purchase intention in online shopping through AI VAs. Research limitations/implications: This study adds to the literature by understanding how customers' technology readiness levels drive engagement and purchase intention. However, this study includes customer engagement as a unidimensional construct. Further research can consist of customer engagement as a multidimensional construct. Practical implications: The findings offer guidelines for e-retailers to enhance customer engagement that matches their personality traits, thereby strengthening their purchase intention through AI VAs. Originality/value: The research contributes to the literature by empirically investigating a research model, revealing optimism, innovativeness, discomfort and insecurity as crucial parameters for customer engagement and purchase intention. [ABSTRACT FROM AUTHOR]

Purpose: India is a rapidly developing economy with a rapidly expending internet infrastructure and among the largest Generation Z population. This generation is tech savvy and the access to technology and network creates a conducive environment for such usage. Internet banking for the same reasons is growing leaps and bounds. The introduction of artificial intelligence (AI) has created disruptions in the traditional banking also. This paper aims to analyze the comfort level and usage of AI-enabled banking services by Generation Z. Design/methodology/approach: The data is collected from 272 Generation Z members. The differential aspects, that is, the relationship of independent variables with dependent variables (AI-enabled internet banking), were analyzed using the structural equation modeling approach. Findings: Defining factors for AI-enabled internet banking were identified. The results of factors were consistent with previous studies. It was found that the usage of AI-enabled internet banking services is insignificant, indicating that Generation Z does not perceive any advantage in using AI-enabled internet banking services. Research limitations/implications: This paper does not incorporate age groups other than Generation Z. Further research could throw light on the difference based on age groups. Further research is required to deeply understand why Generation Z does not perceive AI-enabled internet services as very important. Practical implications: It has been observed that internet banking is important for Generation Z, but they also place greater importance on interpersonal communication. Banks need to consider this in designing their internet banking services. Originality/value: This paper addresses the gap between comfort with and usage of AI-enabled internet banking services, by Generation Z. This paper indicates that the comfort with AI-enabled internet banking services does not translate to usage. [ABSTRACT FROM AUTHOR]

Indian military strategy has tended to neglect the link between the conventional and nuclear domains in a nuclear weapons environment. We argue that this anomaly is evident in two broad areas: the conception of "war," and the complexity produced by new technologies that span the two domains. First, we show with empirical evidence that "limited war" in a nuclear environment is misnomer: the reality is more appropriately called "marginal conflict" owing to its extremely restricted nature. It follows that strategic planning and posture must be tailored accordingly. We then highlight the risk of escalation produced by conventional technologies that carry potential cross-domain nuclear effects, noteably with respect to cyber, artificial intelligence, missile defence and space. We note that the complex strategic effects produced also complicate military-strategic interactions traversing geographic domains, noteably South, Southeast Asia, and Northeast Asia. The paper concludes with some reflections on the reasons for these lacunae in Indian strategic thinking and what might be done about them. [ABSTRACT FROM AUTHOR]

Escalated groundwater contamination around the world developed the idea of aquifer vulnerability, which has widely been employed globally to safeguard groundwater from contamination. Several extensive studies have been done to evaluate the degree of groundwater vulnerability using various techniques. However, a comprehensive assessment of existing literature in order to identify major research gaps associated with aquifer vulnerability is still required. The current study provides a critical overview of several groundwater risk assessment methodologies, including the GIS-based DRASTIC and modified DRASTIC methods, application of Artificial Intelligence (AI) and Machine Learning techniques, and integrated approach for both intrinsic and specific vulnerability assessment. The study discusses validation of DRASTIC model and its comparison with a few other methodologies as well. The paper also highlights the international approach (time dimension in groundwater vulnerability, integrated process based and isotopic studies, etc.) that needs to be practiced rigorously on a larger scale in India. A descriptive summary of the literature indicates that a multidisciplinary and integrated framework such as understanding contaminant type, uncertainties in vadose zone need to be implemented in a much more comprehensive way, especially in India. A conceptual framework that is both reasonably adaptable and scientifically sound must be created for specific vulnerability assessment. [ABSTRACT FROM AUTHOR]

In this paper, I have described the healthcare problem (maldistribution of doctors) in India. Later, I have introduced the concept of artificial intelligence (AI) and I have described AI technology with various examples, how it is rapidly changing the healthcare scenario across the world. I have also described the various advantages of artificial intelligence technology. At the end of the paper, I have raised some serious concerns regarding complete replacement of human based healthcare technology with artificial intelligence technology. I concluded that there is not the slightest question that AI will influence the future. People must be innovative, insightful, and context-aware for AI to work. This is because humans will continue to contribute value that cannot be reproduced by robots. [ABSTRACT FROM AUTHOR]

In the 21st century, global waste challenges worsen in developing nations relying on manual sorting. This improper waste disposal poses significant threats to human health and the environment, necessitating the adoption of Artificial Intelligence-Based Solid Waste Segregation Technology (AIBSWST). In this context, the advanced Frank t-norm captures nuanced relationships in fuzzy logic, crucial in scenarios where fuzzy set order matters. Building on these principles, the complex q-rung picture fuzzy set (Cq-RPFS) becomes instrumental in representing decision-makers preferences in a two-dimensional manner, enhancing the handling of vague information in real-world scenarios. Expanding on these foundational principles, the paper introduces innovative Frank operations grounded in Frank t-norms within the context of Cq-RPFS. Leveraging these operations, the paper proposes four robust aggregation operators (AOs) under Cq-RPFS: complex q-rung picture fuzzy Frank weighted average (Cq-PFFWA), complex q-rung picture fuzzy Frank weighted geometric (Cq-PFFWG), complex q-rung picture fuzzy Frank ordered weighted average (Cq-PFFWOA), and complex q-rung picture fuzzy Frank ordered weighted geometric (Cq-PFFWOG). These AOs exhibit essential properties such as idempotency, monotonicity, and boundedness. A Multi-Criteria Decision-Making (MCDM) method based on the proposed AOs is suggested to validate these strategies. A real-life case study on India's adoption of AIBSWST serves as a practical application, with thorough analyses, including sensitivity, comparative, and superiority assessments, evaluating the performance of the approaches. A thoughtful discussion of the pros and cons of the proposed AOs accompanies the analysis, emphasizing the significance of the approach in ensuring the cleanliness and health of developing nations. [ABSTRACT FROM AUTHOR]

In the 21st century, global waste challenges worsen in developing nations relying on manual sorting. This improper waste disposal poses significant threats to human health and the environment, necessitating the adoption of Artificial Intelligence-Based Solid Waste Segregation Technology (AIBSWST). In this context, the advanced Frank t-norm captures nuanced relationships in fuzzy logic, crucial in scenarios where fuzzy set order matters. Building on these principles, the complex q-rung picture fuzzy set (Cq-RPFS) becomes instrumental in representing decision-makers preferences in a two-dimensional manner, enhancing the handling of vague information in real-world scenarios. Expanding on these foundational principles, the paper introduces innovative Frank operations grounded in Frank t-norms within the context of Cq-RPFS. Leveraging these operations, the paper proposes four robust aggregation operators (AOs) under Cq-RPFS: complex q-rung picture fuzzy Frank weighted average (Cq-PFFWA), complex q-rung picture fuzzy Frank weighted geometric (Cq-PFFWG), complex q-rung picture fuzzy Frank ordered weighted average (Cq-PFFWOA), and complex q-rung picture fuzzy Frank ordered weighted geometric (Cq-PFFWOG). These AOs exhibit essential properties such as idempotency, monotonicity, and boundedness. A Multi-Criteria Decision-Making (MCDM) method based on the proposed AOs is suggested to validate these strategies. A real-life case study on India's adoption of AIBSWST serves as a practical application, with thorough analyses, including sensitivity, comparative, and superiority assessments, evaluating the performance of the approaches. A thoughtful discussion of the pros and cons of the proposed AOs accompanies the analysis, emphasizing the significance of the approach in ensuring the cleanliness and health of developing nations. [ABSTRACT FROM AUTHOR]

Background: Silicosis has been one of the most serious occupational public health problems worldwide for many decades. The global burden of silicosis is largely unknown, although it is thought to be more prevalent in low and medium-income countries. Individual studies among workers exposed to silica dust in various industries, however, reveal a high prevalence of silicosis in India. This paper is an updated review of the novel challenges and opportunities for silicosis prevention and control in India. Main body: The unregulated informal sector employs workers on contractual appointment thereby insulating the employers from legislative provisions. Due to a lack of awareness of the serious health risks and low-income levels, symptomatic workers tend to disregard the symptoms and continue working in dusty environments. To prevent any future dust exposure, the workers must be moved to an alternative job in the same factory where they will not be exposed to silica dust. Government regulatory bodies, on the other hand, must guarantee that factory owners relocate workers to another vocation as soon as they exhibit signs of silicosis. Technological advances such as artificial intelligence and machine learning might assist industries in implementing effective and cost-saving dust control measures. A surveillance system needs to be established for the early detection and tracking of all patients with silicosis. A pneumoconiosis elimination program encompassing health promotion, personal protection, diagnostic criteria, preventive measures, symptomatic management, prevention of silica dust exposure, treatment, and rehabilitation is felt important for wider adoption. Conclusion: Silica dust exposure and its consequences are fully preventable, with the benefits of prevention considerably outweighing the benefits of treating patients with silicosis. A comprehensive national health program on silicosis within the public health system would strengthen surveillance, notification, and management of workers exposed to silica dust in India. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) is a thriving technology, which is transforming all aspects of our society especially in the education sector. AI in the educational domain helps in improving the learning process by making use of AI tools. In India, different tools have been developed for different purposes. Some AI tools have also been developed for disabled students so that people with disabilities may also gain benefit of locationindependent learning. Thus, these tools help to improve the learning of students effectively. In this paper, the authors discussed and analyzed some AI tools developed for the improvement of education by breaking the language barrier among students. The results of the study showed that AI tools help in making high-quality educational content accessible to a wider range of the population. This has led to a more talented workforce, to an improved employability and has contributed to the comprehensive development of the country. The COVID-19 pandemic led to a surge in online education, and AI language translation tools played a crucial role in facilitating multilingual online learning. For instance, during the pandemic, the National Program on Technology Enhanced Learning (NPTEL) in India adopted AI translation tools to translate video lectures into regional languages. [ABSTRACT FROM AUTHOR]

Water quality strongly influences sustainable growth of a healthy society and green environment. According to the International Initiative on Water Quality (IIWQ) of the UNESCO Intergovernmental Hydrological Programme (IHP), it is essential to address water-quality issues holistically in developed and developing countries. Due to rapid urbanization and industrialization in many developing countries, groundwater - one of the major sources of drinking is getting highly affected. The traditional laboratory-based chemical testing process with conventional statistical methods is often used to analyze water quality. However, it is time-consuming. Recently, Artificial Intelligence (AI) based approaches have proven to be a better alternative for analysis and prediction of the quality of water, provided with its chemical components' data. In this paper, we present research focusing on groundwater quality analysis using Artificial Intelligence (AI) in a case study of Odisha, an eastern- state of India and the data acquired from the Northern delta, the North Central Coast of Vietnam. The dataset in Vietnam is collected by the Ministry of Natural Resources and Environment, providing technical regulations on water resources monitoring. The Central Groundwater Board and the Government of India collect the dataset from India. The target problem is formulated as a multi-class classification problem to predict groundwater quality for drinking suitability by WHO standards. AI methodologies such as logistic regression, K-NN, Support Vector Machine (SVM) variants, decision tree, AdaBoost and XGBoost are used. Prediction results have demonstrated that Adaboost, the XGBoost and the Polynomial SVM model accurately classified the Water Quality Classes with an accuracy of 92% and 98%, respectively. It would help decision-makers effectively choose the best source of water for drinking. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) and Machine Learning (ML) are increasingly utilized in share market services due to significant efficiencies and benefits for companies and investors across the globe. This has resulted in an alteration in the firm's business models and has a potential impact on the effectiveness of the share market and could harm investors. Indian share market is also witnessing the usage of this technology by market intermediaries. The present regulatory framework of Securities Exchange Board of India (SEBI) on share market intermediaries is not dealing with the Fintech/technology 2.0-based products and services offered in retail trading and investment advisor platforms in India. The research is primarily based on the normative method presenting a qualitative analysis of the usage of AI & ML in various business models by share market intermediaries. How various share market regulators are addressing and regulating this technology usage and their judicial exposition. The paper concludes that the Indian share market is no exception & SEBI require to look at this new transformation and address the challenges posed by it. SEBI needs to take a proactive step to promote, guide & regulate usages of AI & ML which is gradually seeking the attention of Indian share market intermediaries into their business models and get the maximum benefit out of these technologies. [ABSTRACT FROM AUTHOR]

Drought Prediction is to be focused of great importance for India. Drought can be Predicted using the amount of rainfall data. Two types of predictions can be done, they are – Long term Predictions: Predict rainfall over few weeks/months in advance. Short term Predictions: Predict rainfall a few days in advance in specific locations. Indian meteorological department provides forecasting data required for the project. In this Paper we are planning to work on long term predictions of rainfall. The main motive of the paper is to predict the amount of rainfall in Particular division and state whether the drought will occur or not in advance. We predict the amount of rainfall using Multiple Linear Regression(MLR) which is a branch of AI by using the past dataset. [ABSTRACT FROM AUTHOR]

Prior research in judgment and decision making (JDM) has investigated the effect of problem framing on human preferences. Furthermore, research in JDM documented the absence of such reversal of preferences when making decisions from experience. However, little is known about the effect of context on preferences under the combined influence of problem framing and problem format. Also, little is known about how cognitive models would account for human choices in different problem frames and types (general/specific) in the experience format. One of the primary objectives of this research is to investigate the presence of preference reversals under the influence of problem framing (gain/loss), problem format (experience/description), and problem type (general/specific). Another objective of this research is to develop cognitive models to account for human choices across different problem frames and types in the experience format. A total of 320 participants from India were randomly assigned to one of eight between‐subjects conditions that differed in problem frame, format, and type. Results revealed preference reversals in the description condition; however, they were absent in the experience condition. Moreover, preference reversals were less pronounced in the general problem framing compared to the specific problem framing. Furthermore, specific problems influenced risk‐seeking behavior among participants. We developed cognitive and heuristics models using instance‐based learning theory and natural mean heuristic. Results reveal models' dependency on recent and frequent observations during information sampling. These experience‐based cognitive models could help build artificial intelligence models with fewer preference reversals. This paper focuses on investigating the presence of preference reversals under the influence of problem framing (gain/loss), problem format (experience/description), and problem type (general/specific). Furthermore, Uttrani et al. develop cognitive models using instance‐based learning theory and natural mean heuristic to account for human choices across different problem frames and types in the experience format. [ABSTRACT FROM AUTHOR]

Motivated by the growing significance of solar energy, the paper presents the findings of the maiden study on the long-term seasonal performance assessment of three PV module technologies—monocrystalline (m-Si), polycrystalline (p-Si), and amorphous silicon (a-Si) housed in a 1 MW solar plant installed at a local steppe climate in Gandhinagar, Gujarat, India using the key performance indicators (KPIs), daily power generation, final yield (Y f), reference yield (Y r), total energy loss (T EL) and performance ratio (PR). Notably, the am-Si PV modules were found to be the best performing technology, with an average PR of 71.26% and the lowest average total energy loss of 631 h during the monitored period. Moreover, PV systems have become more popular as a source of green, clean energy; yet, they have low base-load energy sustainability. Explainable AI (XAI) aids in understanding predictions made by the machine learning models. The importance of XAI lies in the comprehension and trust which is embedded into the results of machine learning algorithms. Therefore, the present work builds a comprehensive XAI model for predicting KPIs and assesses the performance of several learning algorithms using R-value, MAE, the number of iterations, and execution time. The Levenberg-Marquardt algorithm predicts the KPIs for p-Si, am-Si, and m-Si with prediction accuracies of 98.63%, 98.58%, and 90.09%, respectively. In summary, this study not only advances our understanding of PV module performance in a steppe climate context but also contributes to the broader field by integrating an XAI model for transparent and reliable predictions of the KPIs. [Display omitted] • Comparison of m-Si, p-Si, and a-Si PV modules in a 1 MW solar plant, Gandhinagar, India, showed long-term performance. • a-Si PV modules outperformed with a 71.26% PR and the lowest 631 hours energy loss, surpassing m-Si and p-Si technologies. • Study underscores XAI's role in ML predictions, developing a robust model to predict KPIs and evaluate learning algorithms. • Levenberg-Marquardt showed high accuracy: 98.63% for p-Si, 98.58% for a-Si, and 90.09% for m-Si PV modules. [ABSTRACT FROM AUTHOR]

Applying artificial intelligence in education is relevant to addressing the current educational crises. Many available solutions apply Convolutional Neural Networks (CNNs) to help improve educational outcomes. Therefore, a series of works have been developed integrating techniques in different educational contexts, for instance, in online teaching practices. Given the various studies and the relevance of CNNs for educational applications, this paper presents a systematic literature review to discuss the state-of-the-art. We reviewed 133 papers from the IEEE Xplore, ACM Digital Library, and Scopus databases. Based on our revision, we discuss characteristics of studies such as publication venues, educational context, datasets, types of CNNs models, and performance of models. We evidence that the literature regarding CNNs still misses more studies discussing educational problems faced by Global South students, considering both teaching and learning perspectives. Such a population cannot be neglected during experiments due to specific educational weaknesses (for example, basic skills) demanding personalized solutions. • A systematic literature review of 133 published papers. • China covered most publications, followed by India. • There is a research gap regarding using CNNs in some regions of the Global South. • The main educational context with CNN applications is students' performance. [ABSTRACT FROM AUTHOR]

The rapid growth of electric vehicles in India necessitates more power to energize such vehicles. Furthermore, the transport industry emits greenhouse gases, particularly SO2, CO2. The national grid has to supply an enormous amount of power on a daily basis due to the surplus power required to charge these electric vehicles. This paper presents the various hybrid energy system configurations to meet the power requirements of the electric vehicle charging station (EVCS) situated in the northwest region of Delhi, India. The three configurations are: (a) solar photovoltaic/diesel generator/battery-based EVCS, (b) solar photovoltaic/battery-based EVCS, and (c) grid-and-solar photovoltaic-based EVCS. The meta-heuristic techniques are implemented to analyze the technological, financial, and environmental feasibility of the three possible configurations. The optimization algorithm intends to reduce the total net present cost and levelized cost of energy while keeping the value of lack of power supply probability within limits. To confirm the solution quality obtained using modified salp swarm algorithm (MSSA), the popularly used HOMER software, salp swarm algorithm (SSA), and the gray wolf optimization are applied to the same problem, and their outcomes are equated to those attained by the MSSA. MSSA exhibits superior accuracy and robustness based on simulation outcomes. The MSSA performs much better in terms of computation time followed by the SSA and gray wolf optimization. MSSA results in reduced levelized cost of energy values in all three configurations, i.e., USD 0.482/kWh, USD 0.684/kWh, and USD 0.119/kWh in configurations 1, 2, and 3, respectively. Our findings will be useful for researchers in determining the best method for the sizing of energy system components. [ABSTRACT FROM AUTHOR]

India's energy demand is predicted to rise by 135% within a span of 20 years. Coping up with surging energy demands requires several reforms in both renewable and non-renewable sectors. Factors such as rising population, reduction in the cost of renewable energy technology and their effect on the nation's GDP, can make policy making a herculean task and the justification for such policies, quite opaque to the public. Artificial Intelligence (AI) technology can help decision makers to quickly draw conclusions from voluminous datasets under different heads. However, AI results need to be post-processed so that they are easily understood by the layperson. This paper focuses on using Sankey diagrams as a post-processing tool for AI systems. Policy formulation often requires an overall assessment of energy sources, production pathways, end used destinations and wastage encountered—these can be easily visualized with a Sankey diagram. For this work, a Sankey protocol was written out in a form where a prompt asks the user to supply information through a spreadsheet interfaced with a customized mobile app—a first anywhere in the subcontinent. India's mobile sector is vibrant, and the paper presents a enabling the user to have a handle on the dynamics of energy distribution locally. The app gives an instant feel of the apportioned energy across grids, the projected changes for the next decade, the efficiency and feasibility of each sector and lastly a telling visual representation showing the main strengths and weaknesses in the energy sector. [ABSTRACT FROM AUTHOR]

In this paper, we have described the health care problem (maldistribution of doctors) in India. Later, we have introduced the concept of artificial intelligence and we have described this technology with various examples, how it is rapidly changing the health care scenario across the world. We have also described the various advantages of artificial intelligence technology. At the end of the paper, we have raised some serious concerns regarding complete replacement of human based health care technology with artificial intelligence technology. Lastly, we concluded that we have to use artificial intelligent technology to prevent human sufferings/health care problems with proper caution. [ABSTRACT FROM AUTHOR]

The need of reducing emission of carbon dioxide became possible with the increased penetration of solar photovoltaic (PV) power generation. The variable and intermittent nature of solar PV power generation affects the stability of the distribution grid. It comes to be necessary to forecast the generated PV power to avoid such type of uncertain conditions. This paper presents the empirical comparisons of six developed supervised learning algorithms to predict the solar power generation for the Dehradun region in India. The algorithms namely multiple linear regression (MLP), ridge regression, decision tree (DT), random forest (RF), support vector machine (SVM) and K-nearest neighbor (KNN) are modified in accordance with the higher prediction accuracy. The detailed empirical comparisons of results are discussed on the basis of mean absolute percentage error (MAPE) and root mean squared error (RMSE). It is found that the best performance of RF method with MAPE as 2.2790% and RMSE as 0.8792%. Tree based algorithms have shown the improved performance among all the methods while SVM and ridge techniques perform quietly low. [ABSTRACT FROM AUTHOR]

Intermittency in wind offers the major challenge in accomplishing the wind energy as a dependable sustainable energy resource in power grid. Fluctuations in wind speed occur seasonally over a year and if this seasonality is considered, the prediction of the speed of wind can be made more accurate. In this paper, an attempt is made to apply a signal decomposition technique called Variational Mode Decomposition (VMD), which decomposes series of wind speed data into several intrinsic mode functions (IMFs) to make the data more regular thereby enhancing the accuracy of the wind speed forecast model. Then, artificial intelligence technique, Adaptive neuro fuzzy inference system (ANFIS) is applied for the wind speed prediction by combining the obtained modes from VMD. Here, wind data of two sites in India, Jogimatti and Lamba are taken for the study. Each site data is grouped into high and low wind speed months and later, this series is decomposed into regular modes using VMD. Later, ANFIS is applied for training and predicting the wind speed for different time horizons. [ABSTRACT FROM AUTHOR]

Background: In medical diagnosis and clinical practice, diagnosing a disease early is crucial for accurate treatment, lessening the stress on the healthcare system. In medical imaging research, image processing techniques tend to be vital in analyzing and resolving diseases with a high degree of accuracy. This paper establishes a new image classification and segmentation method through simulation techniques, conducted over images of COVID-19 patients in India, introducing the use of Quantum Machine Learning (QML) in medical practice.Methods: This study establishes a prototype model for classifying COVID-19, comparing it with non-COVID pneumonia signals in Computed tomography (CT) images. The simulation work evaluates the usage of quantum machine learning algorithms, while assessing the efficacy for deep learning models for image classification problems, and thereby establishes performance quality that is required for improved prediction rate when dealing with complex clinical image data exhibiting high biases.Results: The study considers a novel algorithmic implementation leveraging quantum neural network (QNN). The proposed model outperformed the conventional deep learning models for specific classification task. The performance was evident because of the efficiency of quantum simulation and faster convergence property solving for an optimization problem for network training particularly for large-scale biased image classification task. The model run-time observed on quantum optimized hardware was 52 min, while on K80 GPU hardware it was 1 h 30 min for similar sample size. The simulation shows that QNN outperforms DNN, CNN, 2D CNN by more than 2.92% in gain in accuracy measure with an average recall of around 97.7%.Conclusion: The results suggest that quantum neural networks outperform in COVID-19 traits' classification task, comparing to deep learning w.r.t model efficacy and training time. However, a further study needs to be conducted to evaluate implementation scenarios by integrating the model within medical devices. [ABSTRACT FROM AUTHOR]

Objectives: We aim to describe the history of iodine testing of the cervix and identify areas where further work is required.Study Design: We conducted a search of PubMed and Google Scholar. Full article texts were reviewed. Reference lists were screened for additional articles and books. 37 basic articles in journals including ones written in German and three basic articles in books were identified.Results: Glycogen staining of the ectocervical squamous epithelium with iodine goes back to Paul Ehrlich (1854-1915). Walter Schiller (1887-1960) examined nearly 200 different dyes and found that vital staining of the cervical squamous epithelium was best achieved with Lugol's iodine solution, which was indicated by Jean Guillaume Lugol (1786-1851) for disinfection of the vagina. In 1928 W. Lahm observed that the glycogen content of a squamous epithelium cell decreases as anaplasia increases. From the outset, H. Hinselmann included the iodine test in the minimum requirements for colposcopy. In 1946 H. J. Wespi first mentioned the finding of an "uncharacteristic iodine negative area." The first international colposcopic terminology from Graz in 1975 lists the "iodine light area" among the different colposcopy findings. The IFCPC nomenclatures from Rome 1990, Barcelona 2002, and Rio de Janeiro 2011 have evaluated the iodine test and classified their findings differently. A breakthrough to effective cervical cancer screening in resource-limited settings in Africa, India, and Latin America was achieved with R. Sankaranarayanan's publication on naked-eye visual inspection of the cervix after application of Lugol's iodine.Conclusions: This paper is a step toward a better understanding of what we think and do today with iodine testing and what problems and upcoming tasks will arise in future. [ABSTRACT FROM AUTHOR]

Machine translation has become the most challenging as well as demanding field of research and as a result, to develop a fully automatic 100% correct machine translation system has not been achieved till date. More than 7000 of languages are being used worldwide for communication purposes and in India alone 22 languages have been recognized as official languages for communication purposes. Various efforts have been made to solve this problem by different researchers using different approaches. With the enhancement in technology and internet, automatic translation has become more important to share the knowledge. The authors in this paper described a mechanism of translation using machines for one of the ancient Indian Language Sanskrit to the latest computer understandable language Universal Networking Language (UNL). The proposed system uses two databases: one for the analysis purpose consists of more than 300 rules and the other for the generation of UNL which consists of about 1500 rules. For the performance evaluation, the authors selected 500 Sanskrit sentences in such a way that covers maximum UNL relations from various sources. The sentences are manually converted from English to Sanskrit language taken from Spanish Language center, Hindi to English language taken from IIT Bombay Hindi–UNL expressions and NCERT class 8th Sanskrit book titled "Ruchita Part 3". The proposed system achieves a BLEU score of 0.85. The proposed system gives 93.18% efficiency in resolving UNL relation successfully. [ABSTRACT FROM AUTHOR]

Background: COVID-19 has a profound impact on people with existing mental disorders, augmenting the prevailing inequalities in mental health. Methods: In order to understand the status of telepsychiatry in India and the role of artificial intelligence (AI) in mental health and its potential applications, a scoping review was done between March 2020 and May 2020. The literature review revealed 253 papers, which were used to derive the primary framework for analysis. The information was then reviewed for ideas and concepts, which were integrated with evidence from gray literature and categorized under broader themes based on the insights derived. Finally, a thematic framework was developed for discussion to tailor scientific information for decision-makers' needs. Results: Review findings are summarized under the following headings: changing patterns of health-seeking behavior, origin and evolution of telepsychiatry, possible applications of telepsychiatry and AI, technological features, and AI models in mental health. Conclusions: Though there are several potential opportunities, the time is not yet ripe for telepsychiatry and AI to be adopted fully in the field of mental health care. But it is time that we develop indigenous proprietary technology and test and validate it. With many solutions offered by telepsychiatry and AI, psychiatrists must choose an appropriate tool based on their requirements, availability of resources, and feasibility of deployment. Harmony between conventional care and technology-based care must be reached gradually. [ABSTRACT FROM AUTHOR]

Artificial intelligence is being reconfigured as a set of technologies that can address poverty with precision. The impacts of AI will both exacerbate and ameliorate the conditions of uneven development. Recent debates focus on the disruptive effects of AI, particularly to replication of development trajectories that have had success in reducing poverty. In this paper we further these debates by analysing the backroads of AI. The backroads serve as a metaphor for understanding the ways AI will travel from the sites of incubation to the frontlines of uneven development. We explore dialogues between AI and development in two arenas: the World Bank's Development Impact Evaluation initiative (DIME) and the Government of India's national AI strategy, #AIforAll. We argue that both these arenas serve as hubs from which AI will travel out along the backroads to remote, poor, and fragmented polities. While the World Bank utilises AI as technology to empower experts and mobilise a techno‐political authority, what we refer to as precision AI, India seeks to function as a second‐tier AI hub, making AI cheaper and more accessible domestically and for other developing countries, what we refer to as populist AI. We conclude by discussing the interrelations of precision and populist AI along the backroads, and the potential of backroads research for mapping AI, uneven geographies of development and technology and the impacts of AI's disruptions at different scales. Artificial intelligence is being reconfigured as a set of technologies that can address poverty with precision. We further these debates by analysing the backroads of AI in two arenas: the World Bank's Development Impact Evaluation initiative (DIME) and the Government of India's national AI strategy #AIforAll. While the World Bank utilises AI as technology to empower experts and mobilise a techno‐political authority, what we refer to as precision AI, India seeks to function as a second‐tier AI hub, making AI cheaper and more accessible domestically and for other developing countries, what we refer to as populist AI. [ABSTRACT FROM AUTHOR]

Referral is a dynamic process, in which a health worker at one level of the health system, having insufficient resources (drugs, equipment, skills) to manage a clinical condition, seeks the help of a better or differently resourced facility at the same or higher level to assist in. Health care systems of every country are designed in such a way to encourage patients to first attempt to get care at the primary level and then to approach a higher level of care according to the need. This protocol minimizes the costs for the caretaker/patients. However, in most of the countries, patients often bypass primary care facilities and directly go to the higher center thereby, increasing the burden on higher level facilities, the picture is not very different in India also. Health care system in India is plugged by: overpopulation, lack of expert clinicians, skewed distribution of physicians, lack of motivation among existing health care personnel and an ineffective referral mechanism. Due to failure of conventional paper-based referral systems in our country, we can introduce an e-referral system in the era of internet. It is evident from our experiences, that this artificial intelligence enabled e-referral system has many advantages over the traditional paper-based referral system. It will aid health workers for timely management of cases. Most importantly, it will streamline the existing unorganized referral process. Although, for effective e-Referral system, there should be a collaborative platform where easy search and discovery for health care providers is possible and help in decision making. e- Referral should be incorporated in our health system to strengthen it by bridging the access gap may be through Public Private Partnership model. [ABSTRACT FROM AUTHOR]

Abstract Wind speed and power forecast is an essential component to ensure grid stability and reliability. The traditional forecasting methods fail to address the non-linearity in the wind speed time-series, thus paving way for machine intelligent algorithms. This paper discusses a hybrid machine intelligent wind forecasting model utilizing different variants of Support Vector Regression (SVR) built on wavelet transform. Various performance indices are evaluated to identify the possible best one among four different machine learning regressors for wind forecasting application. Apart from standard ε-SVR and LS-SVR, two new regression models, namely, ε-Twin Support vector regression (ε-TSVR) and Twin Support vector regression (TSVR) are used to forecast short-term wind speed, and are compared with Persistence model for four wind farm sites. The effect of larger dataset on forecasting performance is evaluated for two wind farm sites from USA and India. Further, wind power ramp events are investigated at different hub heights and the forecasting performance of different variants of SVR is compared for five wind farm sites. Highlights • Machine intelligent SVR variants are discussed for short-term wind forecasting. • Overfitting and underfitting scenarios of variants of SVR are assessed. • Forecasting performance of SVR variants is assessed during wind power ramp event. • Wind power ramp events are analyzed at different hub heights for 5 wind farm sites. [ABSTRACT FROM AUTHOR]

Due to lack of a central bureaucrat in mobile ad hoc networks, the security of the network becomes serious issue. During malicious attacks, according to the motivation of intruder the severity of the threat varies. It may lead to loss of data, energy or throughput. This paper proposes a lightweight Intruder Node Detection and Isolation Action mechanism (INDIA) using feature extraction, feature optimization and classification techniques. The indirect and direct trust features are extracted from each node and the total trust feature is computed by combining them. The trust features are extracted from each node of MANET and these features are optimized using Particle Swarm Optimization (PSO) algorithm as feature optimization technique. These optimized feature sets are then classified using Neural Networks (NN) classifier which identifies the intruder node. The performance of the proposed methodology is studied in terms of various parameters such as success rate in packet delivery, delay in communication and the amount of energy consumption for identifying and isolating the intruder. [ABSTRACT FROM AUTHOR]

Objective: Proper diagnosis of Low Back Pain (LBP) is quite challenging in especially the developing countries like India. Though some developed countries prepared guidelines for evaluation of LBP with tests to detect psychological overlay, implementation of the recommendations becomes quite difficult in regular clinical practice, and different specialties of medicine offer different modes of management. Aiming at offering an expert-level diagnosis for the patients having LBP, this paper uses Artificial Intelligence (AI) to derive a clinically justified and highly sensitive LBP resolution technique.Materials and Methods: The paper considers exhaustive knowledge for different LBP disorders (classified based on different pain generators), which have been represented using lattice structures to ensure completeness, non-redundancy, and optimality in the design of knowledge base. Further the representational enhancement of the knowledge has been done through construction of a hierarchical network, called RuleNet, using the concept of partially-ordered set (poset) with respect to the subset equality (⊆) relation. With implicit incorporation of probability within the knowledge, the RuleNet is used to derive reliable resolution logic along with effective resolution of uncertainties during clinical decision making.Results: The proposed methodology has been validated with clinical records of seventy seven LBP patients accessed from the database of ESI Hospital Sealdah, India over a period of one year from 2018 to 2019. Achieving 83% sensitivity of the proposed technique, the pain experts at the hospital find the design clinically satisfactory. The inferred outcomes have also been found to be homogeneous with the actual or original diagnosis.Discussions: The proposed approach achieves the clinical and computational efficiency by limiting the shortcomings of the existing methodologies for AI-based LBP diagnosis. While computational efficiency (with respect to both time and space complexity) is ensured by inferring clinical decisions through optimal processing of the knowledge items using poset, the clinical acceptability has been ascertained reaching to the most-likely diagnostic outcomes through probabilistic resolution of clinical uncertainties.Conclusion: The derived resolution technique, when embedded in LBP medical expert systems, would provide a fast, reliable, and affordable healthcare solution for this ailment to a wider range of general population suffering from LBP. The proposed scheme would significantly reduce the controversies and confusion in LBP treatment, and cut down the cost of unnecessary or inappropriate treatment and referral. [ABSTRACT FROM AUTHOR]

The COVID-19 outbreak has led to a substantial loss of human life throughout the world and has a tremendous impact on healthcare services. Industry 4.0 technologies have established effective supply chain management towards the fulfillment of customized demands in the healthcare field. In addition, the internet of things, artificial intelligence, big data analytics, and 3D printing have been extensively used to combat the COVID-19 pandemic and assist in providing value-added services in the healthcare sector. Henceforth, this paper presents a scientometric analysis on the literature of aforementioned Industry 4.0 technologies in the context of COVID-19. It provides extensive insights into co-citation and co-occurrence analysis of high cited publications, participating countries, influential authors, prolific journals, and keywords using the CiteSpace tool. The analyses reveal that China has produced the highest research outputs, although India is the most collaborative country in this field. The current research hotspots include supply chain, 4D printing, and social distancing technologies. Furthermore, it explores emerging trends, intellectual structure of publications, research frontiers, and potential research directions for further work in the Industry 4.0 assisted healthcare domain. [Display omitted] • Industry 4.0 technologies have emerged in the healthcare domain as an area of growing interest in academia and industry discipline. • Artificial intelligence, big data analytics, internet of things and 3D printing have been extensively used to control the COVID-19 spread and assist in providing value-added services in the healthcare sector. • It explores emerging trends, intellectual structure of publications, and potential research directions for further work in the Industry 4.0 assisted healthcare domain. [ABSTRACT FROM AUTHOR]

This paper uses the data from seven wind farms at Muppandal, Tamil Nadu, India, collected for three years from April 2002 to March 2005 for the estimation of energy yield from wind farms. The model is developed with the help of neural network methodology, and it involves three input variables-wind speed, relative humidity, and generation hours-and one output variable, which give the energy output from wind farms. The modeling is done using MATLAB software. The most appropriate neural network configuration after trial and error is found to be 3-5-1 (3 input layer neurons, 5 hidden layer neurons, 1 output layer neuron). The mean square error for the estimated values with respect to the measured data is 7.6 × 10-3. The results demonstrate that this work is an efficient energy yield estimation tool for wind farms. [ABSTRACT FROM AUTHOR]

Of late, there has been an increase in hearing impairment cases and to provide the most advantageous solutions to them is an uphill task for audiologists. Significant difficulty faced by the audiologists is in effective programming of hearing aids to provide enhanced satisfaction to the users. The main aim of our study was to develop a software intelligent system (SIS): (i) to perform the required audiological investigations for finding the degree and type of hearing loss, and (ii) to suggest appropriate values of hearing aid parameters for enhancing the speech intelligibility and the satisfaction level among the hearing aid users. In this paper, we present a Neuro-Fuzzy based SIS to automatically predict and suggest the hearing-aid parameters such as gain values, compression ratio and threshold knee point, which are needed to be fixed for different octave frequencies of sound inputs during the hearing-aid trial. The test signals for audiological investigations are generated through the standard hardware present in a personal computer system and with the aid of a software algorithm. The proposed system was validated with 243 subjects’ data collected at the Government General Hospital, Chennai, India. The calculated sensitivity, specificity and accuracy of the proposed audiometer incorporated in the SIS were 98.6%, 96.4 and 98.2%, respectively, by comparing its interpretations with those of the ‘gold standard’ audiometers. Furthermore, 91% (221 of 243) of the hearing impaired subjects attained satisfaction in the first hearing aid trials itself with the gain values as recommended by the improved SIS. The proposed system reduced around 75% of the ‘trial and error’ time spent by audiologists for enhancing satisfactory usage of the hearing aid. Hence, the proposed SIS could be used to find the degree and type of hearing loss and to recommend hearing aid parameters to provide optimal solutions to the hearing aid users. [ABSTRACT FROM AUTHOR]