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Intelligence-Software Cost Estimation Model for Optimizing Project Management
With the evolution of pervasive and ubiquitous application, the rise of web-based application as well as its components is quite rising as such applications are used both for development and analysis of the web component by developers. The estimation of software cost is controlled by multiple factors right from human-driven to process driven. Most importantly, some of the factors are never even can be guessed. At present, there are no records of literature to offer a robust cost estimation model to address this problem. Therefore, the proposed system introduces an intellectual model of software cost model that is mainly targets to perform optimization of entire cost estimation modeling by incorporating predictive approach. Powered by deep learning approach, the outcome of the proposed model is found to be cost effective in comparison to existing cost estimation modeling. 2019, Springer Nature Switzerland AG. -
Pain track analysis during gestation using machine learning techniques
During the gestation period women experience Braxton Hicks which is called the false labor, contractions during the second trimester. These contractions are not in regular intervals and also they are often unnoticed. The real labour or the true labour contractions develop late in the third trimester of the gestation usually beyond 36th week (excluding pre-term birth). Some women often fail to identify these pains in the third trimester of the gestation where an efficient facial recognition algorithm along with the support vector machine (SVM) helps them to identify these pains and take optimum care of themselves. The authors in this paper convey a mechanism to identify the pains effectively by creating a database of images pertaining to the pregnant women, her emotional states throughout the pregnancy. Using MATLAB the algorithm of decision tree is implemented and the values obtained from them help us analyze the pain type efficiently. 2021 Institute of Advanced Engineering and Science. All rights reserved. -
In Vitro Production of Saponins
Plants have been utilized as food, feed, and fodder since the dawn of civilization. Plants are also thought to be a rich source of bioactive compounds with a variety of pharmacological actions. Saponins are one such group of molecules which are present in various plant species. As triterpenoid glycosides, they have a 30C oxidosqualene precursor aglycone moiety (sapogenin), which is then linked with glycosyl residues to form saponin. These saponins have a unique platform in the field of pharmaceutical and nutraceutical industries. Saponins are used for the treatment of various diseases which include cancer, diabetic, cardiac, hepatic, and nervous disorders. The production of saponins through conventional approaches is time-consuming and hard to extract pure compounds, and thus to achieve this, in vitro methods have been developed and enhanced the production and extraction of the metabolites. The present chapter focuses on the in vitro production of saponins through various tissue culture techniques such as shoot, callus, cell suspension, adventitious root, hairy root culture, and applications of bioreactors at commercial level. The chapter also focuses on biosynthetic pathway, extraction methods, and biological activities of saponins. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022. -
Soft grafting of DNA over hexagonal copper sulfide for low-power memristor switching
Green electronics, where functional organic/bio-materials that are biocompatible and easily disposable are implemented in electronic devices, have gained profound interest. DNA is the best biomolecule in existence that shows data storage capacity, in virtue of the sequential arrangement of AT and GC base pairs, analogous to the coding of binary numbers in computers. In the present work, a robust, uniform and repeatable room-temperature resistive switching in a Cu/Cu2S/DNA/Au heterojunction is demonstrated. The DNA nanostructures were anchored on the densely packed hexagonal Cu2S structures by simple electrochemical deposition. This heterostructure presents outstanding memristor behavior; the device exhibits resistive switching at a very low threshold voltage of 0.2 V and has a relatively high ON/OFF ratio of more than 102 with a good cycling stability of ?1000 cycles and a negligible amount of variation. The justification for such a switching mechanism is also given on the basis of the energy-band diagram of the Cu2S-DNA interface. Based on the studies herein, the resistive switching is attributed to the reversible doping of DNA by Cu+ ions, leading to intrinsic trap states. Further, the switching is modeled with the help of different transport mechanisms, like Schottky-barrier emission, Poole-Frenkel emission and Fowler-Nordheim tunneling. 2023 The Author(s). -
New constraints on f(T) gravity from DESI DR2 and dark energy survey supernovae
We present new observational constraints on three viable f (T) gravity parametrizationsthe Power Law (f 1CDM), Linder (f 2CDM), and Exponential (f 3CDM) modelsusing the latest Baryon Acoustic Oscillations (BAO) measurements from the Dark Energy Spectroscopic Instrument (DESI) Data Release 2 (DR2). We combine the spectroscopic DESI-DR2 data with Cosmic Chronometer (CC) measurements, the Type Ia Supernovae sample from the Dark Energy Survey (DES) Year 5, and early-universe CMB distance priors to break parameter degeneracies. Our Bayesian MCMC analysis reveals that while late-time data alone suggests a statistical preference for a non-zero deviation from GR at the 1 ? 2 ? level, the inclusion of CMB priors pulls the models significantly closer to the standard ?CDM limit. Specifically, we constrain the joint dataset distortion parameters to p1=?0.002?0.041+0.047, 1/p2=0.156?0.057+0.10, and 1/p3=0.144?0.023+0.071. Across all three models, the addition of early-universe data anchors the inferred Hubble constant to sub-percent precision, clustering around H0?67.3?67.7kms?1Mpc?1, showing excellent agreement with Planck 2018 results but remaining in tension with local SH0ES calibrations. Statistical model comparison demonstrates that while late-time data favors the f (T) extensions (?AIC'?2), the comprehensive joint analysis renders them statistically indistinguishable from ?CDM based on the Akaike Information Criterion (?AIC ? 2). Furthermore, the Bayesian Information Criterion (BIC) finds moderate to strong evidence against the extensions (4 ' ?BIC ? 7.5) due to the penalty on model complexity. We conclude that while current precision data accommodate late-time torsional modifications to gravity, the standard ?CDM model remains a statistically sufficient and more parsimonious description of the cosmic expansion when considering the full evolutionary history. 2026 Elsevier B.V. -
Groundwater Exploitation in India for Quenching Thirst and Supporting Water for Food: Uncontrolled Anthropogenic Water Demands and Reliance Leading to Eco-crime
The extent of groundwater reliance in India for agriculture and drinking water is enormous, making the country the most groundwater-reliant and exploiting country in the world. This water source supports the drinking water supply and irrigation in the country. The hydrogeological, climatic and legal factors contribute to this extensive reliance on groundwater. These current groundwater legal frameworks based on land-water nexus with a robust property rights framework lead to uncontrolled extraction, resulting in a surge of over-exploited zones in the country. Despite attempts by the courts to regulate water governance using principles like public trust and precautionary principles and the attempts of the State to enact new legislations that move away from this property rights linked land-water nexus regime in groundwater regulation, the current legislative framework that controls groundwater extraction in the State adopts a curative approach. The land-water nexus in groundwater not only exaggerates the inherent socio-economic divide between water users but also threatens the groundwater sources and their sustainability, impairing the ecosystem balance. This chapter examines this dilemma, where the groundwater legal framework-inspired exploitation leads to depletion of groundwater resources and aquifers. This chapter argues that uncontrolled exploitation without concern for the rights of aquifers constitutes an instance of eco-crime, and this anthropogenic encroachment over the ecosystem spaces should be categorised as an element of eco-crime. This chapter adopts a socio-legal approach and provides insights from the experiences gathered from the arid State of Rajasthan, India. 2026 selection and editorial matter, Shanthakumar Sanjeevi and Dhanya S, individual chapters, the contributors. -
High performance symmetric supercapacitor based on microporous PANI@?-Fe2O3/MXene hybrid nanocomposite
MXene (Ti3C2Tx), a 2D layered material, has become a trending topic in the field of energy storage, due to its high-power density, flexibility, hydrophilic nature, and ease at which it can form composites with polymers, CNTs, metal oxides, and more. However, the layers of MXene restacks quite easily restricting the number of active sites to the flow of charges. Herein, we have synthesized tri-composite of Ti3C2Tx, ?-Fe2O3, and polyaniline (PANI@?-Fe2O3/Ti3C2TX) via hydrothermal treatment followed by oxidative polymerization. The insertion of ?-Fe2O3 broadens the interlayer distance allowing easy charge/discharge of ions, further addition of PANI enhanced MXenes energy density without altering its power density making MXene more reliable material for energy storage application. The composite exhibits a very high and notable electrochemical performance with 2689.3 Fg?1 specific capacitance at a current density of 1 Ag?1 compared to that of bare MXene (402 Fg?1 at 1 Ag?1). The retention capacitance of 120.7% over 5000 cycles is reported making MXene a promising material in reducing the volume expansion of PANI. A symmetric supercapacitor was fabricated exhibiting a very high energy density of 75.60 Wh kg?1. This work intends to increase MXenes performance favoring hybrid energy storage systems. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025. -
Comprehensive investigation on mechanical properties of mango seed shell short fiber-reinforced epoxy based polymer composites
The mechanical properties of discarded Mango Seed Shell Fiber (MSF)-reinforced epoxy composites are studied in this work. MSF, which was obtained through agricultural wastes, was added to the epoxy matrix in varying weight fractions viz., 5%, 10%, 15%, 20%, and 25% using the hand lay-up method. The outcome shows that the best mechanical performance is reached at the 15% MSF content, i.e., the tensile strength of 29.35?MPa and tensile modulus of 758?MPa, an improvement of 24% in comparison with the unreinforced (neat) epoxy. The modulus and flexural strength were 2962?MPa and 48.13?MPa for 15% MSF content which was 68% and 42% more than neat epoxy. The highest impact strength of 75.93?J/m for 15% MSF which corresponds to 148% higher than the neat epoxy, and the hardness was between 47 RHN and 56 RHN and was maximum for 10% MSF. The novelty of the current study lies in the utilization of mango seed shell fiber, which is an underutilized agro-waste product that has been utilized systematically as a reinforcing element in epoxy composite and the determination of optimal fiber loading by thorough mechanical testing is accomplished in the present work. The results provide the base of mechanical performance data of MSF-reinforced eco-composites and confirm its opportunities as sustainable and cost-efficient reinforcement for lightweight and environmental-friendly structural applications. 2026 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license. http://creativecommons.org/licenses/by-nc-nd/4.0/ -
A novel route for isomerization of ?-pinene oxide at room temperature under irradiation of light-emitting diodes
Present investigation demonstrates the potential use of HY-zeolite for photochemical applications in the selective isomerization of ?-pinene oxide to carveol. In this study, ultraviolet lamp and LED (390 nm) light sources were employed under atmospheric conditions. The results revealed that light penetration through protonated zeolite cavity promotes the hydrogen radical formation, facilitating the isomerization reaction in the presence of dimethylacetamide solvent to achieve up to 60% and 40% conversion of ?-pinene oxide to selective carveol (71%) under light irradiation. Here, using in situ spectroscopic studies (EPR and fluorescence), to confirm the hydrogen radical generation after light irradiation on the reaction mixture. Besides, the mechanistic pathway is proposed based on the experimental evidence of the formation of radicals, which is validated by the Density Functional Theory (DFT). By comparing electrical energy consumption for the same reaction using different reaction setups, it is understood that the energy requirement is nearly the same in the case of a reaction performed using a thermal reactor. The power consumption in reactions conducted using thermal, UV lamp and LED-based reactors was 1.6 kW/h, 1.5 kW/h, and 0.00144 kW/h, respectively. It is clear that the energy consumption in thermal and UV lamp-based reactors is higher than that of LED-based reactors, which was 1111 and 1041 times more than LED reactors respectively. Notably, the catalyst was found to be recyclable at least five consecutive runs, and the successful protocol was demonstrated up to 50 g scale. 2023 Elsevier Ltd -
Algorithms for better decision-making: a qualitative study exploring the landscape of robo-advisors in India
Purpose: This paper explores the current state of Robo-advisory services in India. This paper further highlights the problems experienced by the service providers in disseminating the innovative business model among the Indians. Design/methodology/approach: The study adopts a qualitative approach to investigate the industry experts by conducting semi-structured interviews. The data collected were transcripted and further analyzed using the content analysis technique. Finally, the authors utilized categorization and coding techniques to frame broad study themes. Findings: The study findings reveal that the three pillars of Robo-advisory are ease and convenience, the time factor and transparency in operations. Robo-advisory services are still at a nascent stage in India. Furthermore, keeping the sentiments of Indians in mind, FinTech companies could combine automated Robo-advisory with a human touch of a wealth manager for optimal advisory services. Research limitations/implications: Since the present study is qualitative, the authors cannot generalize the study results. Future research can focus on empirically proving the constructs of the study using quantitative methods. Practical implications: Robo-advisors have a well-established market in developed nations but are still nascent in developing countries like India. The current focus of service providers and regulatory authorities must be to increase awareness among investors by educating the investors and building trust. Originality/value: The present study is the first to qualitatively synthesize the challenges faced by the FinTech service providers in the Indian market. 2023, Emerald Publishing Limited. -
A Scoping Review on the Factors Affecting the Adoption of Robo-advisors for Financial Decision-Making
Robo-advisors have recently gained popularity as an algorithm-based method of simplifying financial management. The present study explores the factors that lead many potential consumers to use Robo-advisors in financial decisions. Adopting a scoping review approach formulated by Arksey and O'Malley, the study examines the factors affecting the acceptance and usage of financial Robo-advisors in different parts of the world. The results suggest that performance expectancy, effort expectancy, trust in technology, financial knowledge, investing experience, cost-effectiveness, facilitating conditions, and intrinsic motivation are positively related to adopting Robo-advisors. On the contrary, anxiety, risk perception, investor age, data security, and behavioral biases negatively influence the investor attitude toward Robo-advisors. This creates a barrier to the diffusion of financial Robo-advisors among the investors. The study concludes by providing recommendations to service providers, policymakers, and marketers for the speedy distribution and acceptance of algorithms for the public's financial decision-making. The study identifies gaps in the existing literature and suggests areas for future research for aspiring academics. 2024 University of Pardubice. All rights reserved. -
An empirical analysis of the antecedents and barriers to adopting robo-advisors for investment management among Indian investors
This study aims to provide a research framework to understand the antecedents and barriers to adopting Robo-advisors for investment decision-making in India. The study employed a research model based on the extended UTAUT 2, along with three additional constructs, i.e. personal innovativeness (PI), perceived risk (PR), and technological anxiety (TA). Data collected were analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM) with the help of SmartPLS 4.0 software. This research will help banks, wealth management service providers, FinTech companies, and Robo-advisor developers improve their platforms, offers, products, and marketing tactics for these automated advisory services. 2024 Informa UK Limited, trading as Taylor & Francis Group. -
Distributed denial-of-service detection and mitigation using software-defined network and internet of things
Internet of Things (IoT) is one of the promising technologies that are developing quickly in various fields such as automation, safety and health. It is a heterogeneous network that links various physical devices. It consists of a variety of vulnerabilities due to its heterogeneous nature. It makes a different level of security issues. Distributed Denial-of-service (DDoS) attack denies services to an authentic user and makes the resources of network inaccessible. DDoS attack is a significant problem for IoT. It is easy to carry out this attack on an IoT network. Main aim of the proposed methodology is to use Software-defined Network (SDN). The primary structure of proposed system is to integrate SDN and IoT technology. This combination is to provide a more secure infrastructure compare to traditional system. The secondary structure of proposed system is used to detect and mitigate the DDoS attacks. The proposed methodology is to check associativity of MAC IP address, source IP address and destination IP address. It was able to detect and mitigate the attack in short span of time. The results are compared on different parameters. That parameters are packet delay time, flow entries and average packet received per second by the controller. This hybrid method is to provide higher security and improve the Quality of Service (QoS). 2019, Institute of Advanced Scientific Research, Inc.. All rights reserved. -
Content Based Deep Factorization Framework for Scientific Article Recommender System
With the advancement in technology and the tremendous number of citations available in the digital libraries, it has become difficult for the research scholars to find a relevant set of reference papers. The accelerating rate of scientific publications results in the problem of information overload because of which the scholars spend their 70% of the time finding relevant papers. A citation recommendation system resolves the issue of spending a good amount of time and other resources for collecting a set of papers by providing the user with personalised recommendations of the articles. Existing state of art models do not take high-low order feature interactions into consideration, due to which the recommendations are not up to the desired level of performance. In this paper, we propose a content-based model which combines Deep Neural Network (DNN) and Factorization Machines (FM) where no pre-trainings are required for providing the citation recommendations. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Content Based Scientific Article Recommendation System Using Deep Learning Technique
The emergence of the era of big data has increased the ease with which scientific users can access academic articles with better efficiency and accuracy from a pool of papers available. With the exponential increase in the number of research papers that are getting published every year, it has made scholars face the problem of information overload where they find it difficult to conduct comprehensive literature surveys. An article recommendation system helps in overcoming this issue by providing users with personalized recommendations based on their interests and choices. The common approaches used for recommendation are Content-Based Filtering (CBF) and Collaborative Filtering (CF). Even though there is much advancement in the field of article recommendation systems, a content-based approach using a deep learning technology is still in its inception. In this work, a C-SAR model using Gated Recurrent Unit (GRU) and association rule mining Apriori algorithm to provide a recommendation of articles based on the similarity in the content were proposed. The combination of a deep learning technique along with a classical algorithm in data mining is expected to provide better results than the state-of-art model in suggesting similar papers. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Document Classification for Recommender Systems Using Graph Convolutional Networks
Graph based recommender systems have time and time again proven their efficacy in the recommendation of scientific articles. But it is not without its challenges, one of the major ones being that these models consider the network for recommending while the class and domain of the article go unnoticed. The networks that embed the metadata and the network have highly scalable issues. Hence the identification of an architecture that is scalable and which operates directly on the graph structure is crucial to its amelioration. This study analyses the accuracy and efficiency of the Graph Convolutional Networks (GCN) on Cora Dataset in classifying the articles based on the citations and class of the article. It aims to show that GCN based networks provide a remarkable accuracy in classifying the articles. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Similarity Analysis for Citation Recommendation System using Binary Encoded Data
Citations are a crucial part of an academic dissertation, project or scientific work. The most time-consuming task for any scholar is to find suitable citations for any work. Thus, a convenient citation recommendation system provides completeness and fulfillment for citing the giants' works. Moreover, attaining high quality for any citation recommendation system is challenging as it should not only recommend relevant papers but also should match the context of the paper. An advanced algorithm SABED (Similarity Analysis using Binary Encoded Data) has been proposed that converts text metadata of the article like author name, doi of the paper, keywords, abstract and content of the paper into the binary format and is fired into the database. The binary formatted query fired fetches the accurate matches thereby increasing the accuracy of search probability and similarity analysis. This similarity analysis can be further used to provide recommendations to the users. The proposed system concentrates on the similarity of the content and hence the context of the papers is not taken into consideration. 2020 IEEE. -
A Citation Recommendation System Using Deep Reinforcement Learning
Recommender systems have seen tremendous growth in the last few years due to the emergence of web services like YouTube, Netflix, and Amazon, etc. An excessive amount of data is being utilized to give proper recommendations to the users. The number of research articles getting published every day is increasing exponentially and thus an efficient model is required to provide accurate and relevant recommendations to the research scholars. The proposed Deep Reinforcement Recommender for Citations (DRRC) model uses reinforcement learning to train the available citation network to achieve the most relevant recommendations. The proposed DRRC model outperforms the state-of-the-art models. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Similarity analysis of court judgements using association rule mining on case citation data-a case study
Information Retrieval System (IRS) is an automated mechanism of retrieving required information from a collection of unstructured or semi-structured data. IRS reduces the efforts of identifying the required information from an enormous database. Legal domain is one of the major producers of complex information which consist of semi-structured and unstructured data. Knowledge based legal information systems are revolutionizing all processes involved in this domain and hence need for more effective legal knowledge management approaches are increasing. This paper proposes association rule mining as knowledge extraction technique that can be used effectively for analyzing relatedness of documents in legal domain. Through this work, authors present their efforts in analyzing similarity in legal documents from the citations done in court judgement by applying Association rule mining. International Research Publication House. -
Melamine derived N-doped Carbon nanotubes: A durable catalyst support for Pt nanoparticles in proton exchange membrane fuel cell
A cost-effective thermal pyrolysis route was adopted to synthesize N-doped carbon nanotube (NCNT) in a single step with the aid of melamine (carbon and nitrogen source) and cobalt catalyzed growth for the formation of N-doped carbon nanotubes. The NCNT was acid treated (fNCNT) to remove the metallic Co from the CNT which was elucidated using X-ray diffraction. Even though these noble metal-free materials are explored as Oxygen reduction reaction (ORR) electrocatalyst, for it to be employed in actual fuel cell the cathode requires noble metals such as Platinum (Pt) nanoparticles to improve its sluggish kinetics. Thus, this study is mainly focused on employing fNCNT as catalyst support in PEMFC, wherein the electrocatalyst was synthesized using microwave-assisted polyol method to decorate Pt nanoparticles on fNCNT, demonstrating its excellent durability of 32% electrochemical active surface area (ECSA) loss when subjected to standard protocols, and full cell performance of hybrid ((Pt/fNCNT) + CB) 412 mW cm?2 (better than commercial Pt/C) when deployed as electrocatalyst for ORR in Polymer electrolyte membrane (PEM) fuel cell, thus our findings open new avenues to explore, design and develop N-doped carbon nanotubes as durable catalyst for fuel cells. Graphical abstract: (Figure presented.) The Author(s), under exclusive licence to Springer Nature B.V. 2024.