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Solar PV Tree Designed Smart Irrigation to Survive the Agriculture in Effective Methodology
The global economy benefits significantly from agriculture. However, there are significant issues and difficulties in the irrigation sector as a result of a significant regional imbalance in power supply, water availability, rainfall, and adoption of technology. The most economical approach to supporting agriculture in the modern day is through irrigation powered by renewable energy. Productivity is impacted by environmental issues, defective irrigation systems, and unknowable soil moisture content in agricultural fields. Traditional watering systems might lose up to 50% of the water used due to ineffective irrigation, evaporation, and overwatering. As a result, the proposed study will modify solar tree-based smart irrigation systems that use the most recent sensors for real-Time or old data to influence watering flows and change watering schedules to enhance the system efficiency. One application of a wireless sensor network is proposed for low-cost wireless controlled irrigation and real-Time monitoring of soil water levels using Arduino controllers. Data is gathered for drip irrigation control using wireless acquisition stations powered by renewable energy, which lowers the risk of electrocution and boosts output. 2022 IEEE. -
Artificial Ecosystem-Based Optimization for Optimal Location and Sizing of Solar Photovoltaic Distribution Generation in Agriculture Feeders
In this paper, an efficient nature-inspired meta-heuristic algorithm called artificial ecosystem-based optimization (AEO) is proposed for solving optimal locations and sizes of solar photovoltaic (SPV) systems problem in radial distribution system (RDS) towards minimization of grid dependency and greenhouse gas (GHG) emission. Considering loss minimization as main objective function, the location and size of solar photovoltaic systems (SPV) are optimized using AEO algorithm. The results on Indian practical 22-bus agriculture feeder and 28-bus rural feeders are highlighted the need of optimally distributed SPV systems for maintaining minimal grid dependency and reduced GHG emission from conventional energy (CE) sources. Moreover, the results of AEO have been compared with different heuristic approaches and highlighted its superiority in terms of convergence characteristics and redundancy features in solving the complex, nonlinear, multi-variable optimization problems in real time. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Enhancement of Agriculture Feeder Performance by Optimal Sizing and Placing of Solar PV Tree through AEO-Based Optimization Technique
Electrical demand, which makes up a large share of the overall power market, agriculture at the top of the list of priorities. To provide end users with a dependable and high-quality supply via various feeders and renewable energy sources, distribution generations are now being developed. In recent years, solar PV systems have been used to meet the demands of numerous applications, including boosting the efficiency of distribution networks. This paper presents the system with effect ive optimization method like Artificial Eco-System based Optimization Technique for identification of the best location to install distribution generation and the optimum size to minimize feeder losses. To meet service expectations, the integration of a solar PV system is swapped out for a solar tree in this suggested work. A 28-bus Indian agriculture feeder is considered for better understanding the proposed algorithm. MATLAB software is used for implementing the proposed optimization technique and CREO-2.0 is used for designing the 3-dimensional solar PV tree. 2023 by the Kamal Kumar U and Varaprasad Janamala. -
Stakeholders' expressions of tech layoffs: A text mining analysis on the "Balance of Arguments"
The volume and nature of work undertaken by the tech employees is humongous, tech employees are known to manage those high tides because of attractive salary packages, perks, and other incentive options, which turn out to be a catastrophic collapse when such layoffs are levied, giving little or no room for a quick transfer to another job from the viewpoint of the affected employees. User-generated unstructured data content in the forms of either tweets, reviews, or comments from around seven major social media platforms were collected to understand the various expressions and discussions linked to layoffs. The collected data is further segregated into employee expressions, social media reviewers, news critiques views, etc., and their perspectives were further analysed. The overall analysis of sentiments on various stakeholders are formulated using Python (Juypter Notebook) package. The authors attempt to model out the viewpoints of various expressors and suggests various measures to be taken by the tech majors to better handle the phenomenon of layoffs. 2023, IGI Global. All rights reserved. -
IoT-Integrated CNN Deep Learning for Automated Breast Cancer Detection and Diagnosis
Breast cancer continues to be a primary cause of death in women, requiring prompt and accurate diagnosis to enhance treatment results. Traditional diagnostic techniques depend on manual assessment, which leads to possible misclassification, significant inter-observer variability, and delays in decision-making. Current deep learning models, including CNNs, frequently experience feature loss, gradient declining and restricted adaptability to real-time data. To overcome these restrictions, we present a hybrid framework combining CNN and ResNet that merges deep learning-based feature extraction with real-time data collecting from IoT devices. The proposed approach utilises CNNs for preliminary feature extraction, ResNet for hierarchical learning with residual connections, and IoT for real-time patient monitoring and automatic notifications. The dataset undergoes preprocessing through normalisation, augmentation, and histogram equalisation to improve image quality and learning efficacy. The model is trained with cross-entropy loss and the Adam optimiser, guaranteeing stability and excellent performance. The evaluation results indicate a substantial enhancement compared to baseline models, with an accuracy of 97, an F1-score of 95.3, and a recall rate of 96.4%, exceeding traditional deep learning (90 accuracy) and CNN-based models (80% accuracy). The suggested model similarly minimises mistakes, with RMSE and MSE values declining to 1.2 and 1.6, respectively, signifying reduced misclassification rates. The inclusion of IoT facilitates instantaneous data transmission with little latency, hence improving clinical decision-making and minimising diagnostic delays. This advanced system facilitates automated and precise breast cancer detection, providing an innovative method for early diagnosis, optimised treatment planning, and improved patient outcomes, while ensuring data privacy and security through encryption and commitment to healthcare regulations. 2026 Yamini Kalva, R. Ganesh Babu, Sindhu V, S. Gokul Pran, Garaga Srilakshmi, Kavitha C T, Sathish Kumar Shanmugam and V. Bhoopathy. This open-access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. -
Violence Prevention Climate and Turnover Intention: Mediating Role of Spirit at Work and Emotional Exhaustion
Workplace violence is a costly organizational problem. Violence prevention, incorporating employee perspectives on safe working policies, is crucial. A safe environment can enhance spirit at work, reducing burnout and turnover intention. This study investigates the relationship between violence prevention climate, emotional exhaustion, spirit at work, and turnover intention. Standardized tools were administered to 146 IT professionals aged 30-40 years. Results showed violence prevention climate positively correlated with spirit at work (r = 0.39; p < 0.001) and negatively with emotional exhaustion (r = -0.40; p < 0.001) and turnover intention (r = -0.35; p < 0.001). Emotional exhaustion mediated the relationship between violence prevention climate and turnover intention (b = -0.23; p < 0.001), while spirit at work did not show mediation. 2024 selection and editorial matter, Dr. Sundeep Katevarapu, Dr. Anand Pratap Singh, Dr. Priyanka Tiwari, Ms. Akriti Varshney, Ms. Priya Lanka, Ms. Aankur Pradhan, Dr. Neeraj Panwar, Dr. Kumud Sapru Wangnue; individual chapters, the contributors. -
Symbolism, Ritual, and Continuity: A Socio-Cultural Interpretation of Kumaoni Folk Art Aipan
The chapter aims to explore Aipan, a traditional floor art form of the Kumaoni people in Uttarakhand. It examines Aipan as a cultural practice that encompasses symbolism, rituals, gender roles, and oral traditions. The chapter uses insights from folklore studies, anthropology, and cultural theory to study the visual language and social-religious importance of Aipan motifs. It will also examine how these motifs, which are rooted in Brahmanical tradition but maintained through folk practices, represent personal and community identities. Aipan is not just an art form; it is a culturally meaningful practice that conveys cosmological, spiritual, and social stories. In a time when tradition is both being revived and diluted in the modern market, the chapter will also discuss how Aipan is changing to fit todays consumer culture thus addressing important questions about preservation, change, and authenticity. 2026, IGI Global Scientific Publishing. -
Violence Prevention Climate and Turnover Intention: Mediating Role of Spirit at Work and Emotional Exhaustion
Workplace violence is a costly organizational problem. Violence prevention, incorporating employee perspectives on safe working policies, is crucial. A safe environment can enhance spirit at work, reducing burnout and turnover intention. This study investigates the relationship between violence prevention climate, emotional exhaustion, spirit at work, and turnover intention. Standardized tools were administered to 146 IT professionals aged 30-40 years. Results showed violence prevention climate positively correlated with spirit at work (r = 0.39; p < 0.001) and negatively with emotional exhaustion (r = -0.40; p < 0.001) and turnover intention (r = -0.35; p < 0.001). Emotional exhaustion mediated the relationship between violence prevention climate and turnover intention (b = -0.23; p < 0.001), while spirit at work did not show mediation. 2024 selection and editorial matter, Dr. Sundeep Katevarapu, Dr. Anand Pratap Singh, Dr. Priyanka Tiwari, Ms. Akriti Varshney, Ms. Priya Lanka, Ms. Aankur Pradhan, Dr. Neeraj Panwar, Dr. Kumud Sapru Wangnue; individual chapters, the contributors. -
Relevance of backcasting as a strategic tool towards organisations' sustainable future ' A key to thrive in the VUCA world
Purpose: The business world has become more turbulent than ever. Organisations must be proactive to meet the challenges of the increasingly disruptive, dynamic, and unpredictable world. One technique that has supported leaders and organisations under challenging circumstances is 'backcasting', which works by envisioning a desirable future state and then working backwards to attain it. The current study aims to extend an understanding of the backcasting practices and strategic approaches that can be used by leadership teams in different industries in order to survive in turbulent times and can be adapted within and beyond any pandemic. Methodology: The research employs a desktop research method to review and compare the most commonly used strategies that leaders from different sectors can use for their respective organisations to thrive in the VUCA world. Findings: There needs to be more research on the applicability and relevance of backcasting that the leaders of different sectors can employ. The study would provide insights that would bridge the existing research gap and come up with suitable strategies that can be employed for dealing with VUCA challenges in the Indian context. Significance: The outcome of the study will be helpful to the leaders in designing and aligning 'out of the box' backcasting strategies to survive in the highly disruptive world. 2024 The authors. Published under exclusive licence by Emerald Publishing Limited. All rights reserved. -
Matrix-Based Apriori Methods for Frequent Pattern Mining: An In-Depth Survey
Data Mining identifies intriguing, useful, and previously unknown patterns and correlations between data stored in databases or warehouses. Frequent Pattern Mining (FPM) is one of the vital methods in the prospering arena of data mining (DM), and it describes the relationship between the items in the datasets. In the last two decades, many studies were carried out in FPM using the Apriori algorithm. The Apriori algorithm requires many database scans and produces numerous candidate itemsets, increasing I/O cost and decreasing computational efficiency. To address these issues, researchers contributed many improved versions of Apriori and proved that those algorithms scan the database only once and identify the frequent itemsets quickly, especially when the itemsets are higher, and provide higher efficiency and feasibility. This research article summarizes matrix-based Apriori algorithms in the literature used for identifying frequent itemsets. 2025 IEEE. -
Optimizing the Performance of Nae Bayesian Classification Using RELSUn, a Bi-stage Feature Selection Algorithm
Feature Selection (FS) is an ideal pre-processing stage to make supervised learning more effective and efficient. RELIEF_NCM, a variant of Relief, a non-parametric feature weighting algorithm in the literature developed to overcome the limitations of RELIEF_DISC. It is designed to consider nominal and continuous features and support multi-class problems. The RELIEF_NCM algorithm removes the irrelevant features from the dataset, but there may still be a possibility of redundant features that may hurt the performance of the classifiers. RedunSUn, a method that removes redundant features using Symmetric Uncertainty (SU), has been introduced in the research paper. The research article introduced a bi-stage FS algorithm to remove redundant and irrelevant features in the dataset by combining RELIEF_NCM and RedunSUn called RELSUn. This hybrid approach RELSun has been examined using eight real-time datasets from the UCI machine learning repository. The investigational outcomes reveal that RELSun outperforms RELIEF_NCM and state-of-the-art methods regarding classification accuracy, precision, and speed of Nae Bayesian Classifier (NBC) with minimum selected attributes. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
The evaluation of the electrochemical properties of Co3O4 nanopowders synthesized by autocombustion and solgel methods
The present investigation involves two synthesis methods, autocombustion (Co3O4-AC) and solgel (Co3O4-SG), for producing nearly spherical-shaped and polygonal shaped nanomaterials of spinel cobalt oxide (Co3O4) respectively as electrode materials. TEM image analysis unveiled distinct particle morphologies for the two samples. The Co3O4-AC particles exhibited a nearly spherical shape, whereas the Co3O4-SG particles displayed a polygonal shape. The phase purity of the Co3O4 samples were confirmed via XRD patterns analysis and the crystallite size was calculated to be 44nm for Co3O4-AC and 36nm for Co3O4-SG. The surface area, estimated via BET experiments, of Co3O4-AC was found to be 15m2/g, while Co3O4-SG exhibited a slightly lower surface area of 11m2/g. Co3O4-AC exhibited a higher specific capacitance (Cs) of 162F/g at 0.25A/g, indicating its superior energy storage capability. On the other hand, Co3O4-SG shows a Cs of 98F/g, indicating slightly lower performance compared to Co3O4-AC. Both nanomaterials exhibited better stability, with more than 85% capacity retention after 5000 chargedischarge cycles. 2023, The Author(s), under exclusive licence to the Institute of Chemistry, Slovak Academy of Sciences. -
Hydrothermally synthesized mesoporous Co3O4 nanorods as effective supercapacitor material
Mesoporous Co3O4 nanomaterial in rod-shape morphology has been synthesized via a hydrothermal method, and heat treated at 350 C for 2 h to develop a phase. Phase purity, morphology, specific surface area and chemical composition of as-obtained Co3O4 material were studied using XRD, Raman, TEM, N2-adsoprtion/desorption and XPS techniques. XRD and Raman analyses indicate single phase material formation with nano-structure, and cubic normal spinel-type structure with a cell parameter of 8.123 The spinel particles are of rod-shape morphology and the specific surface area, estimated through BET studies, is obtained as 47 m2/g. Cyclic voltammogram (CV) recorded at different scan rates evidently demonstrate pseudocapacitance nature of the synthesized material. Maximum specific capacitance (CS) is computed and the value is 261 F/g at 0.25 A/g. These materials have shown longer cycle stability at lower KOH concentration and lower current density. Synthesized Co3O4 nanomaterial could be used as electrode material for energy storage applications. 2023 Elsevier B.V. -
Morphology-dependent supercapacitive properties of Co3O4 nanomaterials synthesized via coprecipitation and hydrothermal methods
The supercapacitive properties of Co3O4 nanocrystalline powders with two different morphologies synthesized by coprecipitation (referred to as Co3O4C) and hydrothermal (referred to as Co3O4-H) methods were compared and studied. The samples were analyzed for their phase purity, crystal structure, surface morphology, and surface area. Both samples were found to be single-phase nanostructures with a normal spinel-type cubic crystal structure (space group Fd3m), as indicated by Raman and XRD (X-ray diffraction) data analyses. TEM (Transmission electron microscopy) images clearly show that the Co3O4C sample exhibits spherical particles with a mean size of 10 nm. On the other hand, the Co3O4H sample shows a flower-like assembly of particles. The Co3O4C sample has a higher specific surface area than the Co3O4-H sample due to its smaller particle size. XPS (X-ray photoelectron spectroscopy) data were collected to analyze the chemical states and cation distribution of the samples, revealing a 2:1 ratio of Co3+ and Co2+ in both samples. Both samples displayed pseudocapacitive behaviour in CV (cyclic voltammetry) and GCD (galvanostatic chargedischarge) analyses. Despite having a smaller surface area, the Co3O4H electrode exhibited a higher CS (specific capacitance) compared to the Co3O4C electrode at all current densities when tested using 1 M KOH electrolyte. At a specific current density (0.5 A/g), the Cs values for Co3O4C and Co3O4H are found to be 366 F/g and 233 F/g, respectively. As the current density increases, the specific capacitance of both electrodes decreases, but this reduction is more prominent for Co3O4-C than Co3O4-H. The study indicates that besides surface area, the morphology of the sample also plays a crucial role in determining the capacitance of a material. 2023 Elsevier B.V. -
Electrochemical performance of ZnxCo3-xO4/N-doped rGO nanocomposites for energy storage application
In this study, nanocomposites consisting of zinc-doped cobalt oxides with a spinel structure and nitrogen-doped reduced graphene oxide (ZnxCo3-xO4 (x = 0 and 1))/N-doped rGO) were synthesized using a solvothermal method. The synthesized materials were investigated using XRD, TEM, EDS, BET, Raman, and XPS for their phase formation, morphology, elemental composition, surface area, and chemical states. XRD analysis revealed that the metal oxides (Co3O4 and ZnCo2O4) present in the composites exhibited a single-phase cubic spinel structure, with a nanocrystalline nature and crystallite size ranging from 8 nm to 20 nm. Raman and TEM analyses revealed the co-existence of metal oxide nanoparticles and N-doped rGO phases in the composites. Electrodes were fabricated using the synthesized nanocomposite materials and subjected to electrochemical testing, including CV, GCD and EIS. The specific capacitiance (Cs) of samples determined to be 181 F/g and 234 F/g for CO/NrGO (Co3O4/N-doped rGO) and ZCO/NrGO (ZnCo2O4/N-doped rGO) nanocomposites, respectively, at lower current density (0.5 A/g). At all current densities, the CS of ZCO/NrGO nanocomposite electrode is observed to be higher than the CO/NrGO nanocomposite, probably due to structural defects and uniform anchoring of ZnCo2O4 particles over the layers of NrGO. The ZCO/NrGO composite electrode exhibits ?86 % capacitance retention after 3000 cycles. 2024 Elsevier B.V. -
Exploring the influence of KOH electrolyte concentration on the electrochemical properties of Co3O4-GO nanocomposite
In this study, we investigate the electrochemical performance of Co3O4-GO nanocomposite, synthesized via a hydrothermal method, as a function of electrolyte concentration. XRD, Raman, SEM, TEM, XPS, and BET techniques have been employed to examine the structural, microstructural, chemical states, and specific surface area characteristics of the composite material. According to SEM micrographs, the aggregated particles have a sheet-like morphology, and these sheets have been assembled into clusters. Using N2-adsorption/desorption isotherms, the pore volume, diameter, and specific surface area of composite were determined to be 0.24 cm3/g, 15 nm, and 63 m2/g, respectively. Based on cyclic voltammograms (CVs) recorded at different scan rates and electrolyte concentrations, the working electrode demonstrated pseudocapacitance behavior. The specific capacitance (Cs) of the fabricated electrode was estimated from GCD curves recorded at different current densities and electrolyte concentrations. For 1 M KOH solution, Cs of the composite electrode is found to be 258 F/g at 1 A/g, and this value drops to 222 F/g at 5 A/g. Furthermore the composite electrode's Cs decreases with increasing electrolyte concentration at a specific current density. The study indicates that 1 M of KOH electrolyte is the optimal electrolyte concentration for optimal energy storage. 2024 Elsevier Ltd -
A comprehensive review on natural macromolecular biopolymers for biomedical applications: Recent advancements, current challenges, and future outlooks
Versatile material properties coupled with high degree of biocompatibility and biodegradability has made biopolymers as potential candidates for diverse applications in the biomedical field. Natural biopolymers derived from various plant, animal and microbial sources with different biochemical compositions are extensively used in biomaterial industry with or without further medication to their native form. Biopolymeric biomaterials have been employed in a wide range of biomedical applications like tissue engineering, drug delivery, bone regeneration, wound dressings and cardiovascular surgery. Carbohydrate based biopolymers and protein based biopolymers are extensively used for several applications in the biomedical field including cartilage regeneration, periodontal tissue regeneration, bone regeneration, corneal regeneration, drug delivery and wound healing. This review work presents a comprehensive outlook on the applications of various biopolymers in biomedical field. The work elaborates the biochemistry of these polymers with special focus on their crucial properties in the biomedical industry. Further a detailed description on the most recent application of various biopolymers in the biomedical filed is presented in this review. This work further summarizes the current challenges and future prospects in the use of biopolymers in biomedical field. 2024 The Author(s) -
Extending schizophrenia diagnostic model to predict schizotypy in first-degree relatives
Recently, we developed a machine-learning algorithm EMPaSchiz that learns, from a training set of schizophrenia patients and healthy individuals, a model that predicts if a novel individual has schizophrenia, based on features extracted from his/her resting-state functional magnetic resonance imaging. In this study, we apply this learned model to first-degree relatives of schizophrenia patients, who were found to not have active psychosis or schizophrenia. We observe that the participants that this model classified as schizophrenia patients had significantly higher schizotypal personality scores than those who were not. Further, the EMPaSchiz probability score for schizophrenia status was significantly correlated with schizotypal personality score. This demonstrates the potential of machine-learned diagnostic models to predict state-independent vulnerability, even when symptoms do not meet the full criteria for clinical diagnosis. 2020, The Author(s). -
Phishfort - Anti-phishing framework
Phishing attack is one of the most common form of attack used to get unauthorized access to users' credentials or any other sensitive information. It is classified under social engineering attack, which means it is not a technical vulnerability. The attacker exploits the human nature to make mistake by fooling the user to think that a given web page is genuine and submitting confidential data into an embedded form, which is harvested by the attacker. A phishing page is often an exact replica of the legitimate page, the only noticeable difference is the URL. Normal users do not pay close attention to the URL every time, hence they are exploited by the attacker. This paper suggests a login framework which can be used independently or along with a browser extension which will act as a line of defense against such phishing attacks. The semi-automated login mechanism suggested in this paper eliminates the need for the user to be alert at all time, and it also provides a personalized login screen so that the user can to distinguish between a genuine and fake login page quite easily. 2018 Authors. -
Motivational Behaviour of Tourism Employees in Relation to Organisational Culture and Career Orientations
The productivity and effectiveness of any organisation depends mainly on the performance level of the employees in the organisation. Human behaviour scientists over the years have conducted various studies and have concluded that, the performance of employees in any organisation depends largely on their motivational behaviour. Reviews of related literature confirm the role of various factors in the motivational behaviour of employees including organisational culture and career orientation of employees. The title of the present study is Motivational Behaviour of Tourism Employees in Relation to Organisational Culture and Career Orientations. The major objectives included ascertaining the relationship between motivational behaviour and organisational culture and career orientations of tourism employees and finding out whether differences in demographic variables would account for significant differences in motivational behaviour. The population of the study consisted of 323 employees of public sector, private sector and multinational companies working in travel agencies, tour operations, airlines and hotels and resorts in Bangalore. The sampling technique employed was judgment sampling. For the present study three tools namely: Motivational Analysis of Organisations- Behaviour (MAO-B) by Pareek (2003), Organisational Culture Survey by Pareek (2003) and Career orientations Inventory by Schein (1990) were used to collect data. The findings of the study show that while two aspects of organisational culture namely internal and future oriented influence the motivational behaviour of employees working in the private sector, no aspect of organisational culture has any influence on the motivational behaviour of employees working in the public sector. Further, only ambiguity tolerant aspect of organisational culture influence the motivational behaviour of employees working in multinational companies.