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Empowering Tribal Communities: Unveiling the Role of Skill Proficiency, Demographics, and Economic Pathways in Wayanad
The complex relationship between skill proficiency, demographics, and economic empowerment among the tribal communities in Kerala's Wayanad district is examined in the study Empowering Tribal Communities, Unveiling the Role of Skill Proficiency, Demographics, and Economic Pathways in Wayanad. The study examines how gender, age, income, and education affect skill development and how that affects resilience and economic prospects using a quantitative research approach. Important insights are revealed by stratifying data from a representative sample of 200 tribal members based on important demographic characteristics. Chi-Square tests, regression models, and structural equation modelling (SEM) are examples of descriptive and inferential techniques that show how skill development promotes economic inclusion. The findings indicate that skill competency is strongly correlated with higher education levels (p?<?0.0001) and has a significant impact on monthly income levels (R2?=?0.263, p?<?0.05). Nevertheless, neither gender nor skill proficiency were shown to be significantly correlated with skill type or age group (p?>?0.05). The SEM framework's path analysis shows that skill development directly improves market connections (??=?0.55), digital literacy (??=?0.70), social inclusion (??=?0.50), and economic possibilities (??=?0.65), all of which contribute to economic empowerment (??=?0.40). However, these effects are sometimes hampered by obstacles like restricted access to resources. The results highlight the need for focused skill-development programs that tackle educational inequalities and infrastructure obstacles. Policymakers, educators, and community organisations looking to improve tribal communities through customised interventions that promote socioeconomic stability and resilience in the face of persistent difficulties can benefit from the practical insights this research offers. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Assessing the Environmental and Health Impacts of Cashew Processing: Pathway Analysis of Pollution Sources, Emissions, and Intervention Strategies
This study investigates the environmental and health impacts of cashew processing in Kollam, Kerala, renowned globally as the Cashew Capital of the World. While the cashew industry is a significant economic driver, providing employment to thousands primarily women and boosting Kerala's export revenue, its environmental footprint has raised growing concerns. Cashew processing generates considerable waste, including emissions from the roasting process, shell waste, and pollutants, which affect air, water, and soil quality. These environmental issues pose a threat to nearby ecosystems and raise health risks for workers and local communities, including respiratory, skin, and cardiovascular conditions due to exposure to toxic fumes and chemical residues. This study aims to balance economic advantages with environmental sustainability by conducting a pathway analysis of pollution sources, emissions, and potential interventions. Employing a mixed-method approach that includes stratified sampling and statistical techniques such as Chi-Square Tests and Structural Equation Modeling (SEM), the research assesses pollution levels, health impacts, and current sustainability practices within the industry. Through a comprehensive evaluation, the study offers actionable recommendations for mitigating the environmental impact of cashew processing. This analysis provides a foundation for policy reforms and green technology adoption, ensuring the long-term viability of Kollam's cashew industry while safeguarding public health and ecological balance. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Explainable artificial intelligence framework for wind turbine fault detection using random forest Extreme gradient boosting hybrid model
Though wind energy has great promise for clean energy generation in India, operational inefficiencies and underutilization still present major obstacles. Although installed wind capacity exceeds 51. 3 GW, actual power generation is still significantly lower than predicted mostly because of weak fault detection and maintenance techniques. Existing machine learning (ML) methods offer high accuracy but typically lack transparency in their forecasts, therefore making it difficult for engineers to correctly interpret and act on model outputs. This research aims to develop an understandable and high-performance anomaly detection model using real-time SCADA data from an Indian wind plant. This research aims to develop an understandable and high-performance anomaly detection model using real-time SCADA data from an Indian wind plant. A hybrid ensemble approach integrating Random Forest and XGBoost is proposed, combined with Local Interpretable Model-Agnostic Explanations (LIME) to provide local interpretability of predictions. The model was trained and evaluated on actual SCADA data using SelectKBest for feature selection, SMOTE for handling class imbalance, and RandomizedSearchCV for hyperparameter optimization. The tuned hybrid model achieved outstanding performance, with an accuracy of 0.9995, F1-score of 0.9995, and minimal error rates (MAE and MSE = 0.00052). LIME-based interpretability highlighted key features driving predictions, with Nacelle Temperature and Gearbox Temperature consistently emerging as critical indicators of turbine braking events, underscoring the importance of thermal variables in fault diagnosis. The findings suggest that interpretable machine learning not only enhances root cause analysis but also supports proactive maintenance, particularly by emphasizing improvements to cooling systems to reduce thermal failures. By providing transparent and reliable insights, the proposed solution enables wind farm operators to make informed, timely decisions, thereby improving turbine reliability and energy yield. The framework is practical, explainable, and well-suited for deployment in smart wind farms, aligning with the United Nations Sustainable Development Goals, including SDG 7 (Affordable and Clean Energy), SDG 9 (Industry, Innovation, and Infrastructure), and SDG 12 (Responsible Consumption and Production) 2025 The Author(s) -
Dynamics of public debt sustainability in major Indian states
This study empirically tests whether the public debt is sustainable or not at 22 major Indian states during 200607 to 201516. It employs the Bohn model for panel data, five alternative specifications and p-spline technique to analyze the issue at aggregate and disaggregate levels. While the results indicate that the debt is sustainable at the aggregate level, it is sustainable only in about 11 states. The results suggest that the fiscal reaction function is linear and the central grant-in aid is an important and a significant undermining factor of sustainability. If the grant-in-aid is excluded from the primary balance, there remain significant positive responses at the aggregate level. However, at the disaggregate level it is significant in only 11 states. Further, the most sustainable states fail to meet the no-Ponzi condition and so the policy intervention is required to improve the debt situation of the states where debt is unsustainable. 2019, 2019 Informa UK Limited, trading as Taylor & Francis Group. -
Comparing the Accuracy of CNN Model with Inception V3 for Music Instrument Recognition
Identification of music instruments from an audio signal is a complex but useful task in music information retrieval. Deep Learning and traditional machine learning models are extremely very useful in many music related tasks such as music genre classification, recognizing music similarity, identifying the singer etc. Music Instrument recognition and classification would be helpful in categorizing different categories of music. Many researchers have proposed models for classifying western music instruments. But very little research has been done in identifying instruments accompanied with South Indian music. This research aims at identifying string instrument such as violin and woodwind instrument such as flute accompanied in a Carnatic music concert and also in other categories of music. In order to identify the instruments accompanied, Convolutional Neural Network model and Inception V3 models were used. The Mel Frequency Cepstral Coefficients images were extracted from the audio input and fed in to the neural network model. The model has been trained for the above mentioned instruments, tested and validated on different types of audio input. This research also evaluates the performance of Inception V3 transfer learning model with CNN model in recognizing the instruments used in different categories of music. 2024, Ismail Saritas. All rights reserved. -
Influence of grandparents on the emotional intelligence of early adolescents in Kerala
Children find unique acceptance in their relationships with grandparents, which benefits them emotionally and mentally. The presence of grandparents in the family can be a source of great support for other family members, especially children and adolescents. They are often role models, playmates and mentors for younger generations. The aim of the study is to compare emotional intelligence of adolescents with regard to the influence of grandparents through a quantitative research design. The sample taken for this research comprised of 427 adolescents of VIII to XII standards, among which 278 were from nuclear families and 149 from three generation families. They belonged to ten different government aided urban state syllabus English medium schools in Kerala. Mangal Emotional Intelligence Inventory was used to yield the total score and four dimensional scores in areas of Intrapersonal awareness, Interpersonal awareness, Intrapersonal management and Interpersonal management in adolescents. An independent sample t-test between two types of families indicated that grandparents have an influence on the emotional intelligence of adolescents. Journal of the Indian Academy of Applied Psychology. -
Perceived emotional intelligence family environment and locus of control as predictors of well-being among adolescents
Adolescence is unparalleled in a person s life as the most important period in his/her newlinepersonality development. It is perceived that well-being is the keystone to an individual s wholesome being. In the past, well-being was discussed in the context of the absence of distress symptoms that include depression, anxiety, divergent behaviors and other disorders. In more recent times it has received a positive note where well-being is widely identified with the positive qualities, which each individual possesses to lead a healthy and happy life. Fifty percent of the mental health disorders begin during the onset of adolescence. There is a need to focus on adolescent well-being in India which has the world s largest adolescent population. Based on the related theories and studies, certain variables like perceived emotional intelligence, family environment and locus of control were selected for the study. The main aim of the study boiled down to two points, to investigate the relationship between perceived emotional intelligence, family environment, locus of control and well-being among adolescents and to research and explore the extent to which newlineperceived emotional intelligence, family environment, locus of control and the newlinedemographic variables (gender, type of family and religion) would contribute towards newlinethe development of well-being among adolescents. Accordingly psychological tools newlinewere adopted. The sample consisted of 903 students studying in plus one and plus two newline(age 16-18 years) Government aided and unaided state-syllabus schools in Parur, subdistrict of Ernakulum, Kerala. Among them 450 adolescents were from nuclear families and 453 others were from joint families. Gender-wise the sample constituted of 413 adolescents males and 490 females. A quantitative survey method design and newlinepurposive sampling method was adopted in the study. Spearman s coefficient of correlation was used to measure the relationship between the variables. -
Enhanced Horse Optimization Algorithm Based Intelligent Query Optimization in Crowdsourcing Systems
Crowdsourcing is a strategy of collecting information and knowledge from an abundant range of individuals over the Internet in order to solve cognitive or intelligence intensive challenges. Query optimization is the process of yielding an optimized query based upon the cost and latency for a given location based query. In this view, this article introduces an Enhanced Horse Optimization Algorithm based Intelligent Query Optimization in Crowdsourcing Systems (EHOA-IQOCSS) model. The presented EHOA-IQOCSS model mainly based on the enhanced version of HOA using chaotic concepts. The proposed model plans to accomplish a better trade-off between latency and cost in the query optimization process along with answer quality. The EHOA-IQOCSS is used to compute the Location-Based Services (LBS) namely K-Nearest Neighbor (KNN) and range queries, where the Space and Point of Interest (POI) can be obtained by the conviction level computation. The comparative study stated the betterment of the EHOA-IQOCSS model over recent methods. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
An efficient privacy-preserving model based on OMFTSA for query optimization in crowdsourcing
Crowdsourcing is now one of the most important and transformative paradigms, with great success in a variety of application tasks. Crowdsourcing obtains knowledge and information to solve cognitive or intelligence-intensive tasks from an evolving group of participants via the Internet. Unfortunately, providing a hard privacy guarantee and query optimization is incompatible when a higher task acceptance rate needs to be accomplished and this case is common in most existing crowdsourcing solutions. The state of art systems suffered from different complexities such as lack of crowdsourcing optimization techniques, increased cost, latency, security, and scalability issues. In this paper, we have proposed a crowdsourcing model to optimize the cost and latency, issues that occur while query optimization using the Moth Flame and Tunicate Swarm Algorithm (MF-TSA). The TSA algorithm is added to the MF algorithm to enhance its exploitation capability and yield fast convergence. The data privacy concerns of the worker and the requestor are addressed using homomorphic encryption that simultaneously enhances the efficiency of the crowdsourcing framework. The main aim of this work is to optimize the cost and latency for query plan selection along with security. Initially, the homomorphic encryption model is used to encrypt the data. In query design, two kinds of crowd-controlled administrators, that is, Crowd Powered Selection (CSelect) and Crowd Powered Join (CJoin) are connected for assessing query. The proposed framework utilizes MF-TSA to optimize the selection and join queries with low cost and latency. Finally, the experimental results demonstrate better query optimization performance than other existing algorithms such as sequential, parallel, and CrowdOp. 2021 John Wiley & Sons Ltd. -
An Efficient Fuzzy Logic Cluster Formation Protocol for Data Aggregation and Data Reporting in Cluster-Based Mobile Crowdsourcing
Crowdsourcing is a procedure of outsourcing the data to an abundant range of individual workers rather than considering an exclusive entity or a company. It has made various types of chances for some difficult issues by utilizing human knowledge. To acquire a worldwide optimal task assignment scheme, the platform usually needs to collect location information of all workers. During this procedure, there is a major security concern; i.e., the platform may not be trustworthy, and so, it brings about a threat to workers location privacy. Recently, many distinguished research papers are published to address the security and privacy issues in mobile crowdsourcing. According to our knowledge, the security issues that occur in terms of data reporting were not addressed. Secure and efficient data aggregation and data reporting are the critical issue in Mobile Crowdsourcing (MCS). Cluster-based mobile crowdsourcing (CMCS) is the efficient way for data aggregation and data reporting. In this paper, we propose a novel procedure, the efficient fuzzy logic cluster formation protocol (EFLCFP) for cluster formation, and use cluster cranium (CC) for data aggregation and data reporting. We recommend a couple of secure and efficient data transmission (SET) protocols for CMCS, (i) SET-IBE uses additively homomorphic identity-based encryption system and (ii) SET-IBOOS uses the identity-based online/offline digital signature system, respectively. Then, we have widen the features of cluster cranium by increasing the propensity to achieve aggregation and reporting on the data yielded by the requesters without scarifying their privacy. Also, considering query optimization using cost and latency. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Brain image classification using time frequency extraction with histogram intensity similarity
Brain medical image classification is an essential procedure in Computer-Aided Diagnosis (CAD) systems. Conventional methods depend specifically on the local or global features. Several fusion methods have also been developed, most of which are problem-distinct and have shown to be highly favorable in medical images. However, intensity-specific images are not extracted. The recent deep learning methods ensure an efficient means to design an end-to-end model that produces final classification accuracy with brain medical images, compromising normalization. To solve these classification problems, in this paper, Histogram and Time-frequency Differential Deep (HTF-DD) method for medical image classification using Brain Magnetic Resonance Image (MRI) is presented. The construction of the proposed method involves the following steps. First, a deep Convolutional Neural Network (CNN) is trained as a pooled feature mapping in a supervised manner and the result that it obtains are standardized intensified pre-processed features for extraction. Second, a set of time-frequency features are extracted based on time signal and frequency signal of medical images to obtain time-frequency maps. Finally, an efficient model that is based on Differential Deep Learning is designed for obtaining different classes. The proposed model is evaluated using National Biomedical Imaging Archive (NBIA) images and validation of computational time, computational overhead and classification accuracy for varied Brain MRI has been done. 2022 CRL Publishing. All rights reserved. -
Grading of Red Chilli, Cardamom and Coriander Using Image Processing
Indian cuisine is known for its wide range of spices. Spices are known as the heart and soul of Indian food. Traditionally, categories are identified based on certain chemical technology or with the help of senses gifted to mankind. In this paper, an image processing technique used to extract multiple features is presented to determine the various categories of spices consumed. This proposed work uses different varieties of common Indian spices such as Capsicum annuum (dry red chilli), Elettaria cardamomum (cardamom) and Coriandrum Sativum (coriander). While creating the image dataset, different categories of all spices were taken from southern region of India. Features are extracted from the manually created image dataset, which forms the base for classification. The result obtained using Multilayer Perceptron (MLP), Naive Bayes and Random Forest classifier is found to be optimal. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Ultraviolet Flux and Spectral Variability Study of Blazars Observed with UVIT/AstroSat
Blazars, the peculiar class of active galactic nuclei, are known to show flux variations across the accessible electromagnetic spectrum. Though they have been studied extensively for their flux variability characteristics across wavelengths, information on their ultraviolet (UV) flux variations on timescales of hours is very limited. Here, we present the first UV flux variability study on intraday timescales of a sample of ten blazars comprising two flat-spectrum radio quasars (FSRQs) and eight BL Lacertae objects (BL Lacs). These objects, spanning a redshift (z) range of 0.034 ? z ? 1.003, were observed in the far-UV (1300?1800 and near-UV (2000?3000 wavebands using the ultraviolet imaging telescope on board AstroSat. UV flux variations on timescales of hours were detected in nine sources out of the observed ten blazars. The spectral variability analysis showed a bluer-when-brighter trend with no difference in the UV spectral variability behavior between the studied sample of FSRQs and BL Lacs. The observed UV flux and spectral variability in our sample of both FSRQs and BL Lacs revealed that the observed UV emission in them is dominated by jet synchrotron process. 2024. The Author(s). Published by the American Astronomical Society. -
Efficient feature fusion model withmodified bidirectional LSTM for automatic Parkinson's disease classification
The majority of people affected by Parkinsons disease (PD) are middle-aged and older. The condition causes a variety of severe symptoms, including tremors, limited flexibility, and slow movements. As Parkinsons disease develops with changing symptoms and growing severity, the importance of computer-aided diagnosis based on algorithms cannot be highlighted. Gait recognition technology appears to be a potential path for Parkinson's disease identification since it captures unique properties of a persons walking pattern without requiring active participation, providing stability and non-intrusiveness. To begin,the median filter is used to remove noise from the input images received during data collection. This paper describes a new method for finding local and global features in gait images to assess the severity of Parkinsons disease.Local features are extracted using a stacked autoencoder, and global features are obtained using an Improved Convolutional Neural Network (ICNN). The Enhanced Sunflower Optimisation (ESO) technique is used to improve the CNN model's performance by optimizing hyperparameters such as batch size, learning rate, and number of convolutional layers. To classify PD severity, a modified bidirectional LSTM (MBi-LSTM) classifier receives input in the form of a combination of local and global features. The proposed model's performance is completely evaluated with the GAIT-IT and GAIT-IST datasets, which include key measures such as accuracy, precision, recall, and the F-measure. This study improves the diagnosis of Parkinsons disease by introducing a non-intrusive real-time monitoring system capable of early detection and prevention. Bharati Vidyapeeth's Institute of Computer Applications and Management 2024. -
Ultraviolet variability in radio-loud active galactic nuclei observed by UVIT onboard AstroSat
Radio-loud active galactic nuclei (AGN) are among the most luminous objects in the Universe, emitting radiation from low-energy radio waves to high energy ? -rays. They are well known to exhibit flux variations at nearly all accessible wavelengths. However, their variability properties in the ultraviolet (UV) band remain relatively less explored compared to other wavebands. Here, we present the results of a systematic investigation of the UV flux and spectral variability characteristics of 24 radio-loud AGN spanning the redshift range 0.018 ? z ? 2.218. The sample comprises 17 BL Lac objects, 6 flat spectrum radio quasars (FSRQs) and one radio-loud narrow line Seyfert 1 galaxy. We used observations obtained with the Ultra-Violet Imaging Telescope (UVIT) onboard AstroSat during its first ten years of operation, covering both the far-UV (FUV; 1300 - 1800 and near-UV (NUV; 2000 - 3000 bands. Of the 24 sources analysed, 18 showed significant UV variability on hour timescales. We found a bluer when brighter (BWB) spectral trend in two sources: the FSRQ CTA 102 and the BL Lac PKS 0447 - 439. The observed UV variability in our sample of radio-loud AGN, together with the BWB trend detected in these two sources, supports a scenario in which the hour timescale UV variations are driven by intrinsic processes within their relativistic jets. 2026 Elsevier B.V. -
Detection of time delay between UV and X-ray variability in Mrk 1044 using AstroSat observations
Active galactic nuclei are known to exhibit flux variations across the entire electromagnetic spectrum. Among these, correlations between UV/optical and X-ray flux variations serve as a key diagnostics for understanding the physical connection between the accretion disk and the corona. In this work, we present the results of analysis of ultraviolet (UV) and X-ray flux variations in the narrow line Seyfert 1 galaxy Mrk 1044. Simultaneous observations in the far-UV band (FUV: 1300 - 1800 and the X-ray band (0.5 - 7 keV) obtained during 31 August - 8 September 2018 with the Ultraviolet Imaging Telescope and the Soft X-ray Telescope onboard AstroSat were used for this study. Significant flux variability was detected in both FUV and X-ray bands. The fractional root mean square variability amplitude ( F var) was found to be 0.036 0.001 in the FUV band and 0.384 0.004 in the X-ray band. To explore potential time lag between the two bands, cross-correlation analysis was performed using both the interpolated cross-correlation function (ICCF) and just another vehicle for estimating lags in nuclei (JAVELIN) methods. Results from both approaches are consistent within 2 ? uncertainty, indicating that X-ray variations lead the FUV variations, with measured lags of 2.25 0.05 days (ICCF) and 2.35?0.01+0.02 days (JAVELIN). This is the first detection of a time delay between UV and X-ray variations in Mrk 1044. The observed UV lag supports the disk reprocessing scenario, wherein X-ray emission from the corona irradiates the accretion disk, driving the observed UV variability. 2026 Elsevier B.V. -
Innovative implementation, ethical challenges, and future prospects of artificial intelligence in pharmaceuticals
Incorporation of pharmaceutical industry and artificial intelligence (AI) is revolu-tionizing patient care, drug development, and discovery. Drug interactions have been predicted using machine learning techniques, optimal molecular structures have been designed, and clinical trial lengths have been reduced. Ethical problems including violations of data privacy, biassed algorithms, and unequal access to AI-informed treatments have been highlighted in the face of these technologies Reacting to these problems, regulatory models have been debated and passed. It is well known that artificial intelligence can drive therapeutic developments and customize medicine. One expects continuous innovations in artificial intelligence technologies to change the pharmaceutical industry. Ethical standards must be reinforced and openness kept if artificial intelligence is to be fully embraced. Innovation must guide newly developing projects. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Electrical and Mechanical Properties of Vapour Grown Gallium Monotelluride Crystals
International Journal of Minerals, Metallurgy and Materials, Vol-20 (10), pp. 967-971. ISSN-1674-4799 -
Vapour Growth and Characterization of Beta Indium Sesquitelluride Crystals
Journal of Crystal Growth, Vol-394, pp. 1-6. ISSN-0022-0248