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Prediction of health insurance premium using bidirectional long short-term memory network with local interpretable model-agnostic explanations
This research proposes an application of deep learning techniques towards the prediction of insurance premiums using ConvLSTM, BI-LSTM, and CNN-LSTM models. Nowadays, Insurance is becoming more sophisticated, there is a need for better models that predict premiums so that risk factors that can be properly valued. The aim of this study is to improve the accuracy and reliability of insurance premium prediction using deep learning methods. The main challenge is the shallow traditional models, whose capturing of temporal dependencies is ineffective and results are not explainable resulting in very few stakeholders having any trust to the predictions. To solve this, this study compared three models: ConvLSTM model, BI-LSTM and CNN LSTM. Of these, the BI-LSTM model was the most effective because it was able to learn bidirectional sequential patterns. These patterns were enhanced using L2 regularization, dropout and dense layers to improve generalization. The dataset used comes from a Kaggle repository, which contained actual insurance data incorporating age, BMI, region and smoking as attributes. Results showed that BI-LSTM had performed the best as compare to other models in terms of accuracy and loss minimization. Important findings highlighted features such as age, smoking, and BMI as pivotal to estimating premiums. Also, to make the model explainable, we incorporated Explainable AI using LIME which delivers interpretable explanations by showing and visualizing the most important features for single predictions. 2026 selection and editorial matter, K. V. Sambasivarao, and Anasuya Sesha Roopa Devi Bhima; individual chapters, the contributors. All rights reserved. -
Processing low-cost feedstock's into high quality biodiesal with a novel chemical multi functional process intensifier method /
Patent Number: 202141060717, Applicant: Ravikumar R.
A method and apparatus for producing a cost-effectively purified biodiesel product from feedstocks are provided. It is possible to utilize both a crude feedstock pretreatment process and a free fatty acid refining process in certain implementations before transesterification and the creation of crude biodiesel and glycerin. When it comes to biodiesel refining, there are several options. As a result of these operations, a pure biodiesel product that meets various commercial requirements may be produced. -
Effect of multiwalled carbon nanotube alignment on the tensile fatigue behavior of nanocomposites
The one-dimensional structure of carbon nanotubes makes them highly anisotropic, making them to possess unusual mechanical properties, and hence employed as promising nanofiller for the composite structures. However, various carbon nanotube properties are not completely utilized when they are used as reinforcement in composites due to inadequate and immature processing techniques. In the present work, an attempt has been made to utilize the strong anisotropic nature of multi-walled carbon nanotubes (MWCNTs) for improving the fatigue life of nanocomposites only by considering a very low weight percentage (<0.5 wt%). The anisotropy of MWCNTs was imparted into the nanocomposites by aligning them in the epoxy matrix with DC electric field during composite curing. Nanocomposites were made for three MWCNT loadings (0.1, 0.2, and 0.3 wt%). The tensile fatigue behavior was investigated under stress control by applying cyclic sinusoidal load with the frequency range of 13 Hz and stress ratio, R = 0.1. The specimens were tested for the fatigue load until the failure or 1E+05 cycles. The fractured surfaces were examined through scanning electron microscope to analyze the fatigue fracture behavior. A small weight percentage of MWCNT loading (0.2 wt%) into the polymer composite has enhanced on an average 13% to 15% fatigue life, which is encouraging to develop the low cost, improved fatigue life composite structures. Also, the energy dissipation mechanism in MWCNT dispersed nanocomposites has shown a reduced crack propagation rate. The Author(s) 2017. -
Digital education for a resilient new normal using artificial intelligenceapplications, challenges, and way forward
As society and technology advance to meet Industry 4.0 requirements, the educational system has also undergone many transformative changes in the past decade. Education is regarded as one of the most important tools for developing individuals, families, businesses, and the economy. New digital technologies are making a great revolution by transforming all aspects of education in teaching, learning, assessment, and feedback. The COVID-19 pandemic has led to the proliferation of digital education and its replacement of traditional education in the educational system. The developments in artificial intelligence (AI) are indispensable in all sectors, including education. AI-integrated learning helps management, teachers, students, parents, and other stakeholders gain insight into their performance to impact the process positively. This chapter aims to throw light on the emerging need and technologies used for digital education and to examine the role of AI in education with examples from the perspectives of teaching, learning, and assessment in the new normal. The application of AI in education and its effectiveness is explored through six publicly available datasets along with strengths, weaknesses, opportunities, challenges, and the future of digital education. This chapter discusses several examples and benefits of AI applications that enhance the educational experience and also emphasizes the need to align it with technology and curriculum to achieve the intended learning outcomes. 2023 Elsevier Ltd. All rights reserved. -
A research on the use of VFX in Indian film director Shankar's films /
Shankar is a renowned Tamil film director popular for his technically brilliant films. He is well known for his big budgeted films, which are outstanding. The research consists of an in depth analysis of the use of visual effects in all eleven films made by the director using various parameters. The parameters that are considered are title sequence, song sequence; fight sequence, romantic/, dramatic sequence, comedy sequence and similarity of visual effects (VFX) in scenes. -
2D Pnictogens for Biosensing and Imaging Applications
2D pnictogens are considered the most promising nano agents among the family of 2D materials. The pnictogens contain phosphorus, arsenene, antimonene, and bismuthine. It has inherent tunable midrange band gaps and unparalleled mobility. It is popular due to its efficient photothermal behavior, loading capability the drug, high charge carrier mobility, in-plane anisotropic properties and less toxicity. The pnictogens nanosheet is obtained by using the Shear exfoliation method. The exfoliation is feasible when there is the existence of weak van der Waals forces between individual layers and during covalent interactions between layers, these elements will adopt the rhombohedral structure. Raman spectroscopy acts as the molecular fingerprints in identifying the number of layers and temperature dependence peak shift of pnictogen substances. The biosensor device can be made by using a layer-by-layer method for each pnictogen, cross-linking agent, and enzyme. Pnictogens can be employed in the application of photothermal therapy (PTT) to overcome the hinder which is because of the lack of efficient cell perception and cell toxicity. It can be used in biomedical applications to make the candidates absorb the ultraviolet and infrared lights as preferable. It can also be used in Biosensors, diagnosis, therapy and in anti-bacterial, anti-inflammation, anti-tumor, neurodegenerative treatment, and tissue repair. Pnictogen has many advantages in the application of biosensing and imaging techniques, but the main disadvantage of pnictogens are low thermal and chemical stability and degradation in ambient conditions. Therefore, this chapter focuses on different characteristics and various applications of pnictogen. 2025 Scrivener Publishing LLC. -
Perception of Anticipatory Psychological Contract: A Study among Post Graduate Students
Psychological Contract is the the unwritten contract between employer and employee, representing mutual expectations, beliefs, and obligations. The concept is popular among the HR managers as it is based on managing employment relationships. In order to best align the requirements of the organization and employee, the Human Resource manager has to be cognizant of this unwritten ???contract???. Like all relationships, it is important to shape and understand it starting from recruitment, through talent management and finally the employee exit. High volume campus recruitment still forms a key strategy among companies in growing markets, segments. Hence the understanding of this employment relationship begins with prospective employees who are the final year students. This study talks about the employment beliefs, & future employment relationships of final year students as an ???anticipatory psychological contract??? and includes promises that the future employee wants to make to their employer, along with the obligations they expect in return. Additionally, the long-term Career expectations of students, in conjunction with their Work values was also explored, to understand their interrelationship with the anticipatory psychological contract. This interrelationship has gained importance in organizational psychology, organizational behavior, and HRM where there is an opportunity for practical application. In this study, the respondents' expectations were measured through the collection of inputs on pre-employment beliefs from final year postgraduate students soon to be part of their respective industry???s workforce. The independent variables identified from the review of literature were career strategy and work values. As part of the study, these variables were tested for correlation, and multiple regression was done to understand the impact of career strategy and work values on anticipatory psychological contract. The results proved that a significant relationship exists between the dependent variable (anticipatory psychological contract ) and independent variables( career strategy and work values). Managing psychological contract is an advantage for employees and employers whereby, both can understand the expectations of the other to prevent breach or violation of the contract. Maintaining a positive contract ensures job satisfaction, commitment and talent retention. This study is significant as the expectations during the pre-employment stage affect the psychological contract even after organizational entry. Understanding contributions to the organization and what employees receive in return are also about managing the psychological contract. This study is also aimed at employers in redesigning their recruitment & talent management strategies. The outcomes of strong HRM strategy based on psychological contract are high employee motivation, productivity, and controlled attrition rates. The study also explores the formation and transition of psychological contract from university to workplace. Lastly, the study aims in exposing the expectations of the younger generation, future workforce. The absence of a longitudinal study is a limitation which would provide the longer term view of the concept. -
Response of ChatGPT for Humanoid Robots Role in Improving Healthcare and Patient Outcomes
Humanoid robotics is characterized by constant developments, which are supported by several research facilities across the world. Humanoid robots are used in many different industries. In this setting, this letter, written by people, makes use of ChatGPT answers to examine how humanoid robots might be used in the medical industry, particularly in light of the COVID-19 pandemic and in future. Although humanoid robots can help with certain jobs, it is important to recognize the indispensable importance of human healthcare professionals who have knowledge, empathy, and the capacity for critical judgment. Although humanoid robots can complement healthcare initiatives, they shouldnt be viewed as a full-fledged replacement for human care. 2023, The Author(s) under exclusive licence to Biomedical Engineering Society. -
Transformative impact of electrical engineering on society, education, academia, and industry: a brief review
The transforming power of electrical engineering (EE) on societal evolution, educational paradigms, university systems, and industrial revolutions is comprehensively reviewed in this study. This study emphasizes the critical contribution of EE in promoting technological development, improving learning approaches, and allowing sustainable industrial practices by methodically analyzing the co-evolution of these domains from Society 1.0 to 5.0, Education 1.0 to 4.0, University 1.0 to 4.0, and Industry 1.0 to 4.0. Unlike conventional stories that credit EE alone for development, this assessment critically examines the multidisciplinary character of progress and acknowledges the contributions of computer science, computer engineering, and artificial intelligence (AI) in forming the digital world. Focusing on fundamental technologies, including power systems, semiconductor devices, renewable energy integration, and automation, which have been the backbone of recent AI-driven advancements, this study offers a crucial contribution. This study clarifies EEs special contribution of EE in the global technological revolution by separating its basic contributions from those resulting from its junction with computing disciplines. Furthermore underlined in this paper are EEs contributions to smart infrastructure development, sustainable energy solutions, and society resilience. Presenting an evidence-based evaluation, this paper provides an insightful analysis for academics, teachers, and legislators, thus supporting EEs basic enabler of multidisciplinary technical and societal advancement. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025. -
Optimal Reactive Power Compensation in Indian Urban Electrical Distribution Networks Using Hybrid Starfish Optimization Algorithm
This paper presents an efficient hybrid optimization approach for optimal reactive power compensation (ORPC) problem in electrical distribution networks (EDNs) using a Hybrid Starfish Optimization Algorithm (HSFOA). A Voltage Stability Index (VSI) is integrated to identify critical buses and narrow the search space, improving solution quality and convergence efficiency. The proposed method determines the optimal locations and sizes of capacitor banks (CBs) and Distribution static synchronous compensators (DSTATCOMs) to minimize real power losses and enhance voltage stability. The effectiveness of the HSFOA is evaluated first on the IEEE 33-bus benchmark system. The results demonstrate that the proposed approach provides superior improvements compared to conventional techniques. Later, the approach is implemented on 106-bus and 85-bus real-time Indian urban distribution networks. For the 106-bus system, losses decrease from 644.768 kW (base case) to 495.273 kW with CBs and to 487.933 kW with DSTATCOMs, corresponding to 23.25% and 24.32% reductions. In the 85-bus system, real power losses are reduced by 34.56% with CBs and 34.44% with DSTATCOMs, while the VSI improves by 15.05% and 20.70%, respectively. Similar improvements were recorded for the IEEE 33-bus system. Overall, the findings confirm that HSFOA offers a robust and effective solution for optimal reactive power planning and enhanced operational performance in modern EDNs. This article is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. https://creativecommons.org/licenses/by-sa/4.0/ -
Experimental data-driven machine learning approach for predicting workability in sustainable concrete using green material replacements
Concrete workability is a critical factor governing the placement, compaction, and durability of fresh concrete, yet it remains less explored in data-driven studies compared to hardened properties. This study presents an experimentally validated machine learning framework for predicting fresh concrete workability, namely Compaction Factor Equivalent (CFE) and Vee Bee Time Equivalent (VBTE), using a newly generated laboratory dataset comprising 300 concrete mixes. The dataset was developed through controlled experiments by systematically varying the waterbinder ratio (W/B), aggregatebinder ratio (A/B), type of green material, and replacement percentage, with fly ash and ground granulated blast-furnace slag (GGBS) used as partial cement replacements to promote sustainability, aligning strategies with SDG 9 (Industry, Innovation, and Infrastructure) and SDG 12 (Responsible Consumption and Production). To capture the nonlinear relationships between mix design parameters and workability indicators, three ensemble learning modelsRandom Forest (RF), Gradient Boosting (GB), and Extreme Gradient Boosting (XGBoost)were developed and evaluated. Model performance was assessed using standard statistical metrics, including R, RMSE, MAE, and MAPE. The results indicate that boosting-based models outperform baseline approaches, with XGBoost achieving the highest prediction accuracy for both CFE and VBTE. By shifting the focus from hardened properties to fresh-state performance, this study addresses a critical research gap and demonstrates that ensemble machine learning models, when combined with experimentally generated datasets, can significantly reduce experimental workload while supporting intelligent and sustainable concrete mix design for practical engineering applications. 2026 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC license. http://creativecommons.org/licenses/by-nc/4.0/ -
Coyote optimization algorithm for optimal allocation of interline Photovoltaic battery storage system in islanded electrical distribution network considering EV load penetration
In current times, there is a need to do power system planning to endure situations of any kind. An islanding operation is one such unavoidable situation that may be required in many cases for both technical and economic reasons. First and foremost, this paper focuses on the determination of the best allotment of Interline-Photovoltaic (I-PV) system as per Electric Vehicle (EV) load penetration in the network. With different operational constraints, a multi-objective optimization using real power loss and voltage deviation index is formulated and solved using the Coyote Optimization Algorithm (COA).The paper highlights the computational efficiency of COA with Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO), in addition to various literary works, and the results suggest the superiority of COA by its global optima. The required battery energy storage system (BESS) capacity for supplying an islanded network's entire load demand for a day is determined in the second stage. The simulations were carried out on the IEEE 33-bus electrical distribution network (EDN) contemplating different levels of EV load penetration. The proposed methodology results have proved that the required energy is provided by optimal I-PV-BESS backup for a daylong islanding operation and its adaptability for practical situations. 2021 Elsevier Ltd -
Optimal allocation of solar photovoltaic distributed generation in electrical distribution networks using Archimedes optimization algorithm
This paper proposes to resolve optimal solar photovoltaic (SPV) system locations and sizes in electrical distribution networks using a novel Archimedes optimization algorithm (AOA) inspired by physical principles in order to minimize network dependence and greenhouse gas (GHG) emissions to the greatest extent possible. Loss sensitivity factors are used to predefine the search space for sites, and AOA is used to identify the optimal locations and sizes of SPV systems for reducing grid dependence and GHG emissions from conventional power plants. Experiments with composite agriculture loads on a practical Indian 22-bus agricultural feeder, a 28-bus rural feeder and an IEEE 85-bus feeder demonstrated the critical nature of optimally distributed SPV systems for minimizing grid reliance and reducing GHG emissions from conventional energy sources. Additionally, the voltage profile of the network has been enhanced, resulting in significant reductions in distribution losses. The results of AOA were compared to those of several other nature-inspired heuristic algorithms previously published in the literature, and it was observed that AOA outperformed them in terms of convergence and redundancy when solving complex, non-linear and multivariable optimization problems. The Author(s) 2022. -
Realization of Green 5G Cellular Network Role in Medical Applications: Use of ChatGPT-AI
Wireless communication in medical applications improves patient monitoring, care coordination, early disease detection, and patient empowerment. It improves healthcare and patient outcomes. The design and configuration of a solar-powered emergency battery backup system for 5G telecommunication base stations, including medical applications, may vary depending on local climate, power requirements, and resources. In this connection, uninterrupted power supply to the base stations become crucial. The author utilizes the ChatGPT-AI features and prepared this comprehensive letter for realizing the role of sustainable practices towards climatic changes. 2023, The Author(s) under exclusive licence to Biomedical Engineering Society. -
Optimal Switching Operations of Soft Open Points in Active Distribution Network for Handling Variable Penetration of Photovoltaic and Electric Vehicles Using Artificial Rabbits Optimization
Global warming, rising fuel prices, and limited conventional fuel supplies are driving the use of renewable energy, battery energy storage, and electric vehicles, transforming traditional electrical distribution networks into active distribution networks. Stochastic technologies can present operational and control challenges, especially for radially configured active distribution networks. In this scenario, strengthening the existing active distribution networks is necessary. This study optimally integrates soft open points for dynamic network reconfiguration to handle uncertainty in active distribution networks. The location, size, and reconfiguration of the soft open points were obtained for the hourly load profile, which included electric vehicle fleet load penetration and PV distributed generation. The proposed multi-objective function uses active power loss, voltage profile, and reliability indices. The proposed multivariable optimization problem was solved using artificial rabbits optimization. The simulations were performed on a modified IEEE 33-bus radial distribution system. The computational efficiency of artificial rabbits optimization is competitive with other prominent algorithms. The proposed approach of optimal soft open points and dynamic network reconfiguration is utilized to cope with uncertainty and run the present active distribution networks with better technical and reliability characteristics. 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. -
Static voltage stability of reconfigurable radial distribution system considering voltage dependent load models
This paper presents the static voltage stability analysis of RDS. Initially the performance of RDS is evaluated using backward/forward load flow considering voltage-dependent load modeling. Later, the load flow solution is used for determining the static voltage stability of the system. The analysis is performed for different type of loads such as constant power, constant current, constant impedance, residential, industrial, commercial, agricultural and electric vehicle loads. The simulations are performed for standard and optimal reconfigured topology of standard IEEE 33-bus test system. The comparative study reveals the importance of load type and topology while assessing the static stability analysis of radial distribution systems. 2020, International Information and Engineering Technology Association. -
Voltage stability analyis of radial distribution systems by considering load models
Generally, the distribution systems have served for different types of loads like commercial, industrial, residential, agriculture and municipality etc. and diverse changes in consumption pattern occur at any part of the network at any time of the day. During light loading condition, the voltage profile can increase and vice versa for peak loading condition. Under these circumstances, it is worthwhile to understand the voltage stability for planning of any Volt/VAr controls. This paper has presented the voltage stability analysis of 12-bus and 85-bus standard radial distribution systems using line stability index. Different load models have been taken and under each model, the system performance as well as its stability discussed. The focal points are suitable for planning studies like Volt/VAr controls, optimal location of Distribution Generation (DG) or load shedding etc. 2018 IEEE. -
Future search algorithm for optimal integration of distributed generation and electric vehicle fleets in radial distribution networks considering techno-environmental aspects
In this paper, a new nature-inspire meta-heuristic algorithm called future search algorithm (FSA) is proposed for the first time to solve the simultaneous optimal allocation of distribution generation (DG) and electric vehicle (EV) fleets considering techno-environmental aspects in the operation and control of radial distribution networks (RDN). By imitating the human behavior in getting fruitful life, the FSA starts arbitrary search, discovers neighborhood best people in different nations and looks at worldwide best individuals to arrive at an ideal solution. A techno-environmental multi-objective function is formulated using real power loss, voltage stability index. The active and reactive power compensation limits and different operational constraints of RDN are considered while minimizing the proposed objective function. Post optimization, the impact of DGs on conventional energy sources is analyzed by evaluating their greenhouse gas emission. The effectiveness of the proposed methodology is presented using different case studies on Indian practical 106-bus agriculture feeder for DGs and 36-bus rural residential feeder for simultaneous allocation of DGs and EV fleets. Also, the superiority of FSA in terms of global optima, convergence characteristics is compared with various other recent heuristic algorithms. 2021, The Author(s). -
Congestion management approaches in deregulated power system an illustrative approach
In deregulated power system with competitive electricity market environment, the provision of strategic bidding option to the market participants and its consequences are open up new challenging tasks to the system operator. The market economic efficiency is mainly dependent on transmission system support. The inability of transmission system support to drive market cleared schedule is known as congestion and which is not desirable. The remedial actions to relieve congestion in the transmission system known as congestion relief approaches and are differ in various markets around the world. The objective of this paper is to illustrate some of the technical and non-technical approaches using simple case studies. 2016 SERSC. -
A new meta-heuristic pathfinder algorithm for solving optimal allocation of solar photovoltaic system in multi-lateral distribution system for improving resilience
A new meta-heuristic Pathfinder Algorithm (PFA) is adopted in this paper for optimal allocation and simultaneous integration of a solar photovoltaic system among multi-laterals, called interline-photovoltaic (I-PV) system. At first, the performance of PFA is evaluated by solving the optimal allocation of distribution generation problem in IEEE 33- and 69-bus systems for loss minimization. The obtained results show that the performance of proposed PFA is superior to PSO, TLBO, CSA, and GOA and other approaches cited in literature. The comparison of different performance measures of 50 independent trail runs predominantly shows the effectiveness of PFA and its efficiency for global optima. Subsequently, PFA is implemented for determining the optimal I-PV configuration considering the resilience without compromising the various operational and radiality constraints. Different case studies are simulated and the impact of the I-PV system is analyzed in terms of voltage profile and voltage stability. The proposed optimal I-PV configuration resulted in loss reduction of 77.87% and 98.33% in IEEE 33- and 69-bus systems, respectively. Further, the reduced average voltage deviation index and increased voltage stability index result in an improved voltage profile and enhanced voltage stability margin in radial distribution systems and its suitability for practical applications. 2021, The Author(s).