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A Intelligent Approach for Fault Detection in Solar Photovoltaic Systems based on BERT-BiGRU Network
Large-scale photovoltaic (PV) plant problem identification and diagnosis is expected to grow more difficult in the future as more and more plants of increasing capacity enter into existence. To keep large-scale PV installations safe, reliable, and productive, automatic identification and localization of any mal-operation among thousands of PV modules is necessary. In order to identify problems in PV plants, the suggested method compares the 'residuals' (fault indicator signals) generated by each string to a predetermined threshold. The suggested method relies on three distinct processes: data preparation, feature extraction, and model training. Preprocessing employs the method of Transform Invariant Low-rank Textures (TILT). The most useful and efficient measurements from feature extraction are kept while less important ones are discarded in favor of the Reduced Kernel PCA technique. Let's move on to model training with BERT-BiGRU. The proposed method is clearly superior compared to the two leading options, BERT and GRU. The proposed method had a 97.36% success rate. 2023 IEEE. -
A Journey toward the Syntheses of ?-Amino-?-hydroxybutyric Acid (GABOB) and Carnitine
In this review, we discuss the synthetic approaches to ?-amino-?-hydroxybutyric acid (GABOB) and carnitine from 1980 to date. The unique structure and biological importance of these molecules have created much interest in various synthetic approaches over the last four decades by organic chemists from across the world. Most of the syntheses are asymmetric methods that involved chiral pool, enantioselective synthesis, enzyme resolution, or a chiral auxiliary as a source of chirality, and the biological significance of the molecules was also discussed. The compiled synthetic methods may fill the existence gap, simplify the complexity involved in the synthesis, and allow the best synthetic route to be found by comparison of all the methods. However, this review also will be useful to prepare similar kind of scaffolds present in various complex natural products. 2021 American Chemical Society -
A JSON Web Signature Based Adaptive Authentication Modality for Healthcare Applications
In the era of fast internet-centric systems, the importance of security cannot be stressed more. However, stringent and multiple layers of security measures tend to be a hindrance to usability. This even prompts users to bypass multi-factor authentication schemes recommended by enterprises. The need to balance security and usability gave rise to Adaptive authentication. This system of utilizing the user's behavioral context and earlier access patterns is gaining popularity. Continuously analyzing the user's request patterns and attributes against an established contextual profile helps maintain security while challenging the user only when required. This paper proposes an Open standards based authentication modality that can seamlessly integrate with an Adaptive Authentication system. The proposed authentication modality uses JavaScript Object Notation(JSON), JSON Web Signature(JWS) and supports a means of verifying the authenticity of the requesting client. The proposed authentication modality has been formally verified using Scyther and all the claims have been validated. 2022 IEEE. -
A learner-cantered educational landscape: Era of education 5.0 and disruptive technologies
The chapter focuses on concepts of Education 5.0 and its competence in shaping future learning environments. It emphasises on learners social and personal growth by improving quality of life standards with the help of current technologies and digitalisation. (Shabir Ahmad, 2023) To deliver humanised approach by the application of new technologies is the primary use of Education 5.0. However, usage of new age technology in education doesn't mean giving laptop and tablet to each and every child and the usage of digital mediums for teaching and learning. After covid-19, digitisation becomes the integral part of our life, education is no exception for that. (Shabir Ahmad, 2023) Beyond digitalisation pandemic also remained us the importance of human hardships to social transformation with emotional intelligence driving technology as a tool. In short education 5.0. (SYDLE. com, 2023) referring to the significance of human, social and emotional abilities to enhance wellbeing of an individual by using technology advancement as a tool. 2025 by IGI Global Scientific Publishing. All rights reserved. -
A Legal Analysis of Cyber-Enabled Wildlife Offences in India: A Qualitative Case Study of Sea Fans (Gorgonia spp.) on YouTube
With the advent of the Internet, offences against threatened species have transitioned online. Such species are directly or indirectly traded on social media despite being protected under Indian wildlife law. A qualitative case study was undertaken to assess the preparedness of national law and policy in prohibiting such offences. Sixty-three YouTube links on sea fans in the Hindi language were accessed over 8 weeks, and the information generated by both content creators and audiences was gathered and categorized for analysis. The legal provisions were then interpreted and applied to assess the extent to which the parties involved could be held liable. Our investigation shows that of these video links, the content creators directly offered specimens for sale in 15.87% of instances, demonstrated physical possession of wild specimens in 23.81% of these posts, and were involved in both activities in 20.63% of the links, which in our analysis is explicitly prohibited under national law. The remaining 39.68% of video links merely disseminated information on the relevance or usage of species in occult or religious practices, for which no express legal provision currently exists. Certain indirect legal provisions were found to be relevant; however, there were challenges associated with their implementation. Even the liability of a social media company was found to be limited if it can be demonstrated that the company exercised due diligence. Therefore, there is a need to explicitly regulate online content that has the potential to drive an unlawful demand for protected species alongside the imposition of enhanced liability on social media companies. Such measures, coupled with community awareness, can reduce cyber-enabled wildlife offences committed through social media channels. 2024 Taylor & Francis Group, LLC. -
A Legal Analysis on Navigating Facial Recognition Technology in Indias State Surveillance Framework
The evolution of the state surveillance apparatus in India, particularly through the integration of novel technologies such as Facial Recognition Technology (FRT) and Artificial Intelligence (AI), has significantly transformed national security and law enforcement strategies. While these advancements enhance the states capacity for counter- terrorism and crime prevention, they also raise critical privacy and human rights concerns. The paper analyses the existing legal framework governing surveillance in India, focusing on the implications of AI- driven FRT. Key concerns are categorised into three areas: (a) security vulnerabilities, (b) inaccuracies, biases, and lack of transparency in FRT systems, and (c) the potential misuse of surveillance powers by the state. The paper is not technical in nature; instead, it critically analyses existing laws and proposes policy recommendations and regulatory mechanisms to balance national security imperatives with the protection of individual privacy rights in a democratic society. 2026 by IGI Global Scientific Publishing. -
A Lesion Feature Engineering Technique Based on Gaussian Mixture Model to Detect Cervical Cancer
Latest innovations in technology and computer science have opened up ample scope for tremendous advances in the healthcare field. Automated diagnosis of various medical problems has benefitted from advances in machine learning and deep learning models. Cancer diagnosis, prognosis prediction and classification have been the focus of an immense amount of research and development in intelligent systems. One of the major concerns of health and the reason for mortality in women is cervical cancer. It is the fourth most common cancer in women, as well as one of the top reasons of mortality in developing countries. Cervical cancer can be treated completely if it is diagnosed in its early stages. The acetowhite lesions are the critical informative features of the cervix. The current study proposes a novel feature engineering strategy called lesion feature extraction (LFE) followed by a lesion recognition algorithm (LRA) developed using a deep learning strategy embedded with a Gaussian mixture model with expectation maximum (EM) algorithm. The model performed with an accuracy of 0.943, sensitivity of 0.921 and specificity of 0.891. The proposed method will enable early, accurate diagnosis of cervical cancer. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
A Lightweight Hybrid Deep Learning model for Real-Time Intrusion Detection in IoT Networks.
In IoT networks, cyber threats are hard to identify because of dynamic and heterogenic nature of IoT traffic as it cannot be identified with more traditional intrusion detection systems. This paper discusses the deep learning methods of intrusion detection in terms of CNN+LSTM, CNN+BiLSTM, CNN+GRU, and BiGRU+RF and propose a novel Dense+SimpleRNN architecture. Preprocessing includes label encoding, feature selection, normalization, SMOTE balancing, and reshaping sequences, using the RT-IoT 2022 dataset. The paper demonstrates, CNN + BiLSTM and CNN + GRU achieving similar accuracy but with higher computational cost. On the other hand, the proposed Dense+SimpleRNN has 98.59% accuracy, precision and recall and Fl-score, which are higher than the baselines models. The results point that Dense+SimpleRNN is an efficient and lightweight IDS that is very appropriate in real-time IoT network security. 2025 IEEE. -
A Lightweight LCDECG Model for Cardiovascular Diagnostics Using ECG Features
Cardiovascular disease (CVD) is among the leading causes of death around the world, requiring accurate and reliable diagnostics, and early detection. This project aims at the development of an efficient and accurate, lightweight model to classify heart rhythms based on an ECG.. In this paper, we propose the LCDECG (Lightweight Cardiac Diagnostic ECG) model, which integrates deep morphological feature extraction from ECG with clinically relevant handcrafted features. With MobileNetV2 used as a feature extractor and statistical descriptors of ECG signals, both the pathways are combined at the feature level for multi-class classification of cardiac conditions. Experiments conducted on the Dataset demonstrate better classification performance with 97.8% accuracy, 96.4% precision, 97.1% recall, and 96.7% F1-score over traditional neural networks alone or only statistical methods. The model is able to achieve the desired results as it only utilizes 2.43M parameters in its architecture, and therefore is amenable to real-time deployment in resource-scarce environments. Its use is advantageous for facilitating timely and early detection, which is necessary to improve patient survival and reduce healthcare costs through preventative treatment. Current ECG readings are based on manual assessment by trained cardiologists, which can be time-consuming and potentially subjective, depending on several professionals in the medical field evaluating the tracing. Due to the increased incidence of cardiovascular disease globally, and the limited number of professionals, particularly in developing countries, there is even greater need for automated convenient and trustworthy ECG tracing for diagnostic support. 2025 IEEE. -
A Lightweight Multi-Chaos-Based Image Encryption Scheme for IoT Networks
The swift development of the Internet of Things (IoT) has accelerated digitalization across several industries, offering networked applications in fields such as security, home automation, logistics, and quality control. The growth of connected devices, on the other hand, raises worries about data breaches and security hazards. Because of IoT devices' computational and energy limits, traditional cryptographic methods face issues. In this context, we emphasize the importance of our contribution to image encryption in IoT environments through the proposal of Multiple Map Chaos Based Image Encryption (MMCBIE), a novel method that leverages the power of multiple chaotic maps. MMCBIE uses multiple chaotic maps to construct a strong encryption framework that considers the inherent features of digital images. Our proposed method, MMCBIE, distinguishes itself by integrating multiple chaotic maps like Henon Chaotic Transform and 2D-Logistic Chaotic Transform in a novel combination, a unique approach that sets it apart from existing schemes. Compared to other chaotic-based encryption systems, this feature renders them practically indistinguishable from pure visual noise. Security evaluations and cryptanalysis confirm MMCBIE's high-level security properties, indicating its superiority over existing image encryption techniques. MMCBIE demonstrated superior performance with NPCR (Number of Pixel Changing Rate) score of 99.603, UACI (Unified Average Changing Intensity) score of 32.8828, MSE (Mean Square Error) score of 6625.4198, RMSE (Root Mean Square Error) score of 80.0063, PSNR (Peak Signal to Noise Ratio) score of 10.2114, and other security analyses. 2013 IEEE. -
A literature review on friction stir welding of dissimilar materials
Friction stir welding (FSW) employs a tool that does not require any filler materials; frictional heat is produced and performs a solid-state joining method. Severe plastic deformation causes to join similar and dissimilar materials without melting the workpiece at the welding line. Friction stir welding is the most recent friction welded joining processes with the most surprising features when welding various metal alloys, including magnesium, aluminium, copper, and steel. FSW is victorious of all the other conventional welding methods implied in many industrial applications like automobile, aerospace, fabrication, shipping, marines and robotics. It gives high-quality welds, energy input, and distortion are lower, better retention of mechanical properties; it is eco-friendly and can be performed less operating cost. This research work aims at the FSW process in Al-Cu alloys, highlighting:(a) Optimizing the welding process parameters, welding feed rate, tool rotation speed, (b) Evaluation of Electrical Conductance properties of joints, (c) Mechanical properties and metallography characteristics of joints. 2021 Elsevier Ltd. All rights reserved. -
A LLM-Powered Approach for Generating Meeting Minutes from Audio Conversations
Accurate documentation of meetings is essential for effective communication, decision-making, and accountability across various settings, including corporate environments, academic discussions, research collaborations, project reviews, and multi-stakeholder forums. Manual note-taking is often error-prone and time-consuming, highlighting the need for automated solutions. This paper proposes an LLM-based approach for generating structured Minutes of Meeting (MoM) from audio recordings. The method begins with audio transcription using the Vosk model, followed by grammatical and punctuation recovery to enhance text readability. The preprocessing step cleans and segments the transcribed text. Named Entity Recognition (NER) is applied to identify relevant entities such as names, dates, and organizations. The method then segments the conversation into topical sections aligned with the meeting agenda. A Large Language Model (LLM), named OpenAI, has been used to produce abstractive summaries. An action item extraction module identifies tasks, responsible individuals, and deadlines. A speaker analysis component highlights principal participants in decision-making processes. The system organizes the final output into a structured format, such as JSON or PDF, for easy access and distribution. This end-to-end approach enhances the clarity, accessibility, and utility of meeting documentation, reducing manual effort and improving organizational productivity. 2025 IEEE. -
A longitudinal examination of the relation between academic stress and anxiety symptoms among adolescents in India: The role of physiological hyperarousal and social acceptance
Academic stress is a critical aspect of adolescent experience around the world, but particularly in countries with dense populations that lead to highly competitive college admissions. With a population of over one billion people, the competition for higher education in India is significantly high. Although research has shown that academic pressures are associated with anxiety in adolescents, this work is primarily cross-sectional. The current study examined academic stress and anxiety symptoms over time and assessed physiological hyperarousal as a mediator and social acceptance as a moderator of this relation in a sample of adolescents from India (N= 282, 1318 years, 84% female). Adolescents completed measures of academic stress, physiological hyperarousal, social acceptance and anxiety symptoms at two-time points, 5 months apart. Findings demonstrate direct effects of academic stress on changes in symptoms of generalised anxiety and panic, as well as indirect effects through physiological hyperarousal. Social acceptance did not moderate the relation, although it uniquely predicted changes in panic disorder symptoms over time. The findings of this study contribute to the scientific understanding of a potential mechanism through which academic stress leads to anxiety among adolescents in India. 2021 International Union of Psychological Science. -
A Low Voltage and Low Power Analog Multiplier
In this research work, a low voltage analog multiplier has been realized through the utilization of a flipped voltage follower (FVF). The multiplier is characterized by its capacity to function at low power while exhibiting high gain. The exclusive use of transistors in its implementation renders it highly appropriate for fully integrated circuit applications. The multiplier has been developed using a supply voltage of 500 mV and an operating frequency of 25 KHz. The design consumes power of 8.23 uW. Moreover, a comparative study between the proposed multiplier and the conventional gilbert multiplier is presented in the paper. All simulations and layout designs have been conducted through the virtuoso analog design environment (ADE) of Cadence at 45 nm CMOS technology. 2023 IEEE. -
A Low-Complexity Multiplier-Less Filter Bank Based on Modified IFIR for the SDR Channelizer
Digital filter banks are extensively used in an SDR channelizer for channelization. The objective of this research work is to design a low computational complexity FIR filter bank for generating sharp transition width channels for SDR. The design of unified and variable bandwidth channels for SDR using the proposed structure is based on the modified IFIR filter structure and cosine modulation technique (CMT). The performance of the proposed structure is demonstrated with the help of an example. The results show that the multiplier complexity of the proposed structure is less than those of other state-of-the art methods. The optimization techniques are incorporated in this work to further reduce the complexity of the proposed structure. With the help of canonical signed digit (CSD), multi-objective artificial bee colony (MOABC) and shift inclusive differential coefficients (SIDC) common sub-expression elimination (CSE) optimization, the filter used in this structure is made multiplier-less. 2024 IETE. -
A LSTM based model for stock price analysis and prediction
The share market in India is exceedingly unpredictable and volatile, with an infinite range of factors regulating the share market's orientations and tendencies; hence, forecasting the upswing and downturn is a difficult procedure. Because of several essential aspects, the principles of share market have always been unclear for shareholders. This study aims to significantly reduce the likelihood of analysis and forecasting with Long Short-term Memory (LSTM) model approach that is both resilient yet easy is still suggested. LSTM is a complete Learning Model that is a Predictive Method. Conversely, advancements in technology have opened the way for more efficient and precise share market forecasting in current times. Using the provided historical data sets, the results showed that the LSTM model has considerable potential for forecasting. 2023 Author(s). -
A Machine Learning Approach for Revving Up Revenue of Indian Tech Companies
This study addresses a critical gap in research by examining the effectiveness of various machine learning models in predicting revenue for Indian tech companies. The V.A.R, ARIMA, simple moving average, weighted moving average, and FB Prophet models were employed and their performances was compared. The findings demonstrate that FB Prophet consistently outperforms other models, exhibiting superior accuracy in revenue forecasting. This underscores FB Prophet's potential to offer precise revenue predictions, enabling companies to gain insights into their financial health, anticipate market trends, and optimize decision-making. Future research could further enhance accuracy by incorporating economic indicators, providing a more holistic view of revenue dynamics and empowering companies to make more informed strategic decisions. 2024 IEEE. -
A Machine Learning Approach to Consumer Behavior Analysis in Social Media-Influenced E-Book Markets
Social media has emerged as a dominant marketing channel, significantly influencing consumer purchase decisions. Despite extensive global research, little is known about region-specific dynamics in emerging markets such as India. This study addresses this gap by applying Random Forest and Gradient Boosting models to survey data from 386 respondents in the Delhi-NCR region to analyze e-book purchasing behavior. Data were preprocessed through encoding, normalization, and stratified traintest splitting (80:20), with reproducibility ensured via a fixed random seed. Model evaluation employed R, RMSE, and MAE metrics, alongside a paired-sample t-test. Results showed that Gradient Boosting (R = 0.82) outperformed Random Forest (R = 0.78; p = 0.038). Feature importance analysis revealed that behavioral variablespurchase intention, brand awareness, and social media engagementwere the strongest predictors, whereas demographic features contributed minimally. These findings emphasize the primacy of behavioral traits in social mediadriven e-book markets and provide evidence for designing region-specific digital marketing strategies in emerging economies. 2025, Interdisciplinary Publishing Academia. All rights reserved. -
A Machine Learning Approach to Crude Oil Price Prediction Using Support Vector Machine (SVM)
Crude oil is one of the most important energy sources, and fluctuations in its international prices affect all aspects of the economy. The price of crude oil is influenced by several variables, and the length of time that each component has an effect differs giving an increase in non-linear oil price features. Although it is a complex task, identifying the most essential factor influencing for precise predicting, crude oil prices are essential. Therefore, this study aims to employ a machine learning model to address the intricate relationships among different factors. Primarily, it gathers data regarding West Texas Intermediate (WTI) and Brent crude oil prices as well as macroeconomic variables. Secondly, the data is normalized to prepare it for further analysis. Finally, a crude oil prediction model is constructed using Support Vector Machine (SVM) to predict future international crude oil prices. The daily, weekly, and monthly prices are used to confirm the models efficacy developed using WTI and Brent oil. The models performance is also evaluated by incorporating various combinations of macroeconomic variables to find the most influential factor. Results from experiments show indicates the benchmark model was much exceeded by the developed model and performed very well in terms of prediction accuracy. The findings reveal that selecting the appropriate variables can greatly enhance prediction accuracy. This model has the potential to provide valuable insights for traders, investors, and energy-related enterprises, offering beneficial guidance for decision-making purposes. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
A Machine Learning Approach to Modelling Sales Performance Based on Social Media Analysis
By address the strategic challenges faced while maximising the advertising effectiveness, this paper examines the predictive relationship between different advertising sales platform and the brand sales performance. Four different supervised machine learning models - Random Forest, XGBoost, LightGBM, and a voting classifier ensemble model are applied to categorise the brand sales performance. The models are trained on a multi-platform advertising dataset. This research focuses on both the accuracy and interpretability of the data to make it easier identifying which platform affects the brand sales the most, which is quite different from the existing approaches that concentrates on finding predictive accuracy. This study aims to help marketers and businesses to make better and well informed data-driven decisions for marketing strategies by finding the most effective advertising platform. Each of the models were evaluated using Classification metrics which includes Accuracy, Precision, Recall and F1 score, along with confusion matrix. Future scope of the paper includes merging the models in real time systems and also by expanding it to carefully examine different time periods and customer groups. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.