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Fate of AI for Smart City Services in India: A Qualitative Study
With the rollout of the smart city initiative in India, this study explores potential risks and opportunities in adopting artificial intelligence (AI) for citizen services. The study deploys expert interview technique, and the data collected from various sources are analyzed using qualitative analysis. It was found that AI implementation needs a critical examination of various socio-technological factors to avoid any undesirable impacts on citizens. Fairness, accountability, transparency, and ethics (FATE) play an important role during the design and execution of AI-based systems. This study provides vital insights into AI implications to smart city managers, citizen groups, and policymakers while delivering promised smart city experience. The study has social implications in terms of ensuring that proper guidelines are developed for using AI technology for citizen services, thereby bridging the ever-critical trust gap between citizens and city administration. Copyright 2022, IGI Global. -
Diseased Leaf Identification Using Bag-of-Features and Sigmoidal Spider Monkey Optimization
Agricultural products decide the economy of a country like India. The agricultural business has the involvement of a large population. The quality and quantity of agricultural products highly depend on environmental conditions and facilities provided to farmers. Timely and efficient detection of diseases in plants and crops is one of the most critical issues that affect crop production. Therefore, it is highly desirable to develop some cheap and easy-to-handle automated plant disease detection systems for the timely treatment of plants. Leaves are considered a primary source of information about the health of plants. In the case of plants, the disease may be easily visualized and identified by observing its effect on leaves. Therefore, this paper introduces a bag-of-features in sigmoidal spider monkey optimization to identify a diseased leaf, separating the diseased leaf from a healthy leaf. The investigational outcomes show the superiority of the anticipated technique in contrast to other meta-heuristic-based systems. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024. -
Prediction and modeling of mechanical properties of concrete modified with ceramic waste using artificial neural network and regression model
Over two centuries, concrete has been crucial to building. Thus, eco-friendly concrete is being developed. Emulating these tangible traits has recently gained popularity. Ceramic waste concretes mechanical properties were modeled in this study. Ceramic waste percentages ranged from 5 to 20%. Compressive and tensile concrete strengths were modeled. To predict concrete hardness, regression modeling and artificial neural network (ANN) were used. Model performance was evaluated using prediction coefficients and root-mean-square error (RMSE). ANN models outperformed linear prediction with a coefficient for determination (R2) of 0.97. ANN models achieved root-mean-square errors (RMSEs) of 1.22MPa, 1.21MPa, and 1.022MPa after 7, 14, and 28days of retraining, respectively. Linear regression model showed RMSE values of 1.21, 1.32, and 1.27MPa at 7, 14, and 28days, respectively. In determining the compressive and tensile strength, the R2 was 0.70, meanwhile the ANN model achieved 0.87. Given its accuracy in predicting the strength qualities of ceramics cement and structural stiffness, the ANN model presents a promising tool for representing various types of concrete. The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024. -
Blockchain-Driven Architecture for Decentralized Energy Transactions in Smart Grids
The versatility of blockchain technology enables its capabilities to protect decentralized energy trading and transform modern smart grids by removing all dependencies on centralized utility operators and eliminating vulnerabilities that stem from data tampering, pricing manipulation, and single-point failures. The purpose of this paper is to present a fully virtualized and software-implemented architecture of a blockchain that incorporates a lightweight Proof-of-Authority (PoA) consensus model, dynamic pricing smart contract(s), and a multi-layer energy ledger, tailored specifically for seamless peer-to-peer energy trading. The proposed energy trading model is built using an entirely virtualized architecture and is validated through simulation, as opposed to previously proposed models that are based on expensive consensus mechanisms and require hardware-assisted metering. The proposed model delivers significant improvements (37.4% reduction in transaction latency, 52.8% improved throughput, and 41.6% lower computational overhead) when compared to traditional Proof-of-Work and DAG-based models. The smart contract engine ensures energy-pricing fluctuations remain stable, and the system as a whole achieves 95.2% transaction validity, all while preserving ledger immutability, user anonymity, and high scale performance. The results achieved from this innovative software-defined architecture ensure its decentralized smart-grid deployments and high scalability exceed market expectations. 2026 IEEE. -
Analysis of Statistical and Deep Learning Techniques for Temperature Forecasting
In the field of meteorology, temperature forecasting is a significant task as it has been a key factor in industrial, agricultural, renewable energy, and other sectors. High accuracy in temperature forecasting is needed for decision-making in advance. Since temperature varies over time and has been studied to have non-trivial long-range correlation, non-linear behavior, and seasonal variability, it is important to implement an appropriate methodology to forecast accurately. In this paper, we have reviewed the performance of statistical approaches such as AR and ARIMA with RNN, LSTM, GRU, and LSTM-RNN Deep Learning models. The models were tested for short-term temperature forecasting for a period of 48 hours. Among the statistical models, the AR model showed notable performance with a r2 score of 0.955 for triennial 1 and for the same, the Deep Learning models also performed nearly equal to that of the statistical models and thus hybrid LSTM-RNN model was tested. The hybrid model obtained the highest r2 score of 0.960. The difference in RMSE, MAE and r2 scores are not significantly different for both Statistical and Vanilla Deep Learning approaches. However, the hybrid model provided a better r2 score, and LIME explanations have been generated for the same in order to understand the dependencies over a point forecast. Based on the reviewed results, it can be concluded that for short-term forecasting, both Statistical and Deep Learning models perform nearly equally. 2024 Bentham Science Publishers. -
Enhancements of women's entrepreneurship: A theme-based study
Woman entrepreneurs are defined as a group of women who initiate, organize, and run a business concern, from a situation where a woman was not even allowed to get out of their home, to today, running most of the successful brands of the world, contributing a major part to the economic growth, and breaking the stereotypes by providing a reality check to the male dominance. There has been a wide range of public policies enrolled out to facilitate and encourage the growth of women's entrepreneurship. A few such policies from India have proved to be successful, which will be outlined in this book chapter. From the past times of not gaining adequate recognition for their support, women have emerged successful in overcoming hardships such as lack of visibility, lack of training and educative support about public policies provided by governments to women entrepreneurs, fewer opportunities, and walking out of the social stigma. 2023, IGI Global. All rights reserved. -
STUDY OF THE LINEAR AND NONLINEAR REGIMES OF NATURAL CONVECTION WITH WEAK OR DOMINATING INTERNAL HEAT GENERATION FOR RIGID-FREE BOUNDARIES
The paper presents the linear and non-linear regimes of natural convection in the presence of uniform internal heat generation for rigid-free boundaries. A linear stability analysis followed by nonlinear stability analysis is carried out for using a novel procedure. The eigenvalue of the two problems are different. The first one has Rayleigh number based on internal heat generation as the eigenvalue while the second, which is of the classical Bard type, has a buoyancyRayleigh number. The critical Rayleigh number in both problems is initially determined using the single-term Galerkin method, followed by a refinement of the value by the Maclaurin series method. The findings indicate that the system becomes stable with increasing values of the porous parameter and the Brinkman number. The percentage relative error in the eigenvalue obtained by the single-term Galerkin method relative to that obtained by the Maclaurin series method is presented. In the second natural convection problem, we have two Rayleigh numbers, viz., the weak internal Rayleigh number, RI, and the external Rayleigh number, Ra. The effect of RI on Rac is to reduce it in the case of a heat source and increase it in the case a of heat sink. Additionally, conditions facilitating the transition from Brinkman Bard convection to DarcyBard convection are presented. The GinzburgLandau equation is obtained for both the problems and the scaled Lorenz model is derived in the case of second problem. The solution from the GinzburgLandau equation is used to plot for the amplitude and results are illustrated. 2025 by Begell House,. -
Study of internal heat source generated natural convection with sinusoidal and non-sinusoidal time-periodic vertical oscillations
This study explores the effect of gravity modulation on natural convection induced by a uniform internal heat source within a fluid-saturated porous medium, a topic of growing relevance in advanced thermal management applications. Four distinct gravity waveforms, square, sinusoidal, triangular, and sawtooth, are examined under three boundary condition combinations: Rigid-Adiabatic-Rigid-Isothermal (RARI), Rigid-Adiabatic-Free-Isothermal (RAFI), and Free-Adiabatic-Free-Isothermal (FAFI). A novel analytical framework is developed by integrating a Maclaurin series expansion with a minimal FourierGalerkin approach to derive a generalized Lorenz model. Linear stability analysis, via a modified Venezian method, to determine the critical internal Rayleigh number and its correction due to modulation. A weakly nonlinear analysis based on the GinzburgLandau equation also provides closed-form expressions for the mean Nusselt number, capturing heat transfer characteristics. The findings demonstrate that square wave modulation most effectively enhances heat transport, followed by sinusoidal, triangular, and sawtooth forms. The influence of key physical parameters reveals that increasing porous parameter (?2) and Brinkman number (?) suppress heat transfer, as do higher Prandtl numbers (Pr) and modulation frequencies (?). FAFI yields the highest heat transfer among the boundary types, while RARI performs the least. The novelty of this work lies in the combined analytical treatment of diverse waveform modulations while considering a uniform internal heat source and boundary condition for natural convection. 2025 The Author(s) -
Cognitive marketing and purchase decision with reference to pop up and banner advertisements
The aim of this research paper is to employ a mixed research approach and to check how the past data differs from the present and hence it uses an argument mapping to find the reality using focus group. Since genders have different opinion on pop-up and banner advertisements, two focus groups, one group consisting the female gender and the other focus group consisting the male respondents have been taken for the data collection. Small sample has been used for the argument mapping (N=45/Male) and (N=47/Female). A series of steps has been conducted in the argument mapping and relevant maps have been developed for drawing inference. It is found that, male have no patience to deal with the pop-up and banner advertisements but women are keener and patient enough to make the best use of these advertisements. On the other hand a questionnaire was framed from the variables found from the literature review and the same was distributed to both the genders and it was found collectively that though pop-up advertisements and banner advertisements are useful in some way, it is always considered to be a negative aspect. Misleading advertisements, data security scam are a few negative aspects of such advertisements and hence, there are a lot of ugly truth behind pop up and banner advertisements. The mixed research approach (triangulation) between the quantitative and qualitative is a new initiative taken by the researchers in this research and holds originality of the study. 2018 Academic Research Publishing Group. -
Analysis of Routing Protocols in MANET Networks
The scientific article is a review and comparative analysis of routing protocols for MANETs. The study examines the main protocols connected to mobile ad hoc networks such as B.A.T.M.A.N, BMX7, OLSRv1, Babel and provides a detailed analysis of their characteristics, advantages and disadvantages. To empirically evaluate performance, tests were carried out in a network simulator. The results of the study allow us to draw conclusions about the effectiveness and reliability of each of the monitoring protocols under various operating conditions of MANET. This article is a valuable contribution to the field of MANET research and can be used in the development of new technologies and solutions for mobile wireless networks. The work is relevant and practically significant because it helps researchers and engineers make informed decisions when choosing the optimal routing protocol in MANET networks. The results obtained can be useful in the design of mobile applications, emergency communication systems, transport management and other areas where the efficient operation of wireless networks is important. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
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Multilingual Voice-Assisted for Traffic Sign Detection and Classification in Adverse Weather Conditions
In a world where millions of people are wounded in auto accidents each year due to negligence, a lack of understanding of traffic laws, and bad weather, there is an urgent need for greater road safety. This is particularly the case in India, where a disproportionately high number of traffic accidents lead to numerous fatalities. Ignoring traffic signs raises these risks and endangers not only vehicles but also passengers and pedestrians. This project addresses the significant issue of traffic sign recognition in bad weather and offers voice-based instruction in many languages to increase road safety. Using a mix of state-of-the-art technologies, including YOLOv8 for real-time sign detection and the Google Translate API, which supports NLP tasks, this research offers a full solution. The model's remarkable precision and efficacy underscore its capacity to revolutionize traffic safety and furnish a more secure and expedient driving encounter. With the world moving towards more autonomous mobility, this study is laying the groundwork for safer and more effective driving in the future. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Representation of moral crisis and social order across cultures An analysis of three texts goldings lord of the flies camus s the plague and U R Ananthamurthy s samskara
The growth of human civilization has always been accompanied by unexpected turns and twists caused by individuals and communities exhibiting inexplicable behavior. For every effort of greatness, there has been an equal amount of meanness and debauchery making the humankind more and more inscrutable. Human beings have defied all definitions about themselves and still go about with a perplexing image of both noble and brutal. They create a social order to ensure a newlinecohesive existence but end up breaking it, unable to face the moral crisis caused by the extreme turn of events. This study attempts to see the staging of human recovery from such situations through the fictional narratives of three writers belonging to three cultures and the crises faced by those societies, through a study of The Plague (1947 ) by the French writer, Albert Camus (1913 -1960 ), Lord of the Flies (1954) by the English writer, William Golding (1911 1993) newlineand Samskara (1965) by the Indian writer, U. R. Ananthamurthy (1932 - newline2014). newlineNo society or culture has escaped the throes of crisis be it moral, social, political or otherwise. The crisis that may be surmountable in one culture may shake the foundation of another. Of all the crises prevalent in society, one of the major causes for concern in the eyes of the participants newlineof the New Dialogue is moral crisis . Studies undertaken across the globe have thrown up the alarming fact that this is a crisis which could jolt the social order with its amoral way of thinking. America s moral integrity has been eroded by an anything goes culture abetted by the moral permissiveness of contemporary liberalism. The concern that the waning of tradition is giving way to moral confusion and anarchy is shared the world over including China and India despite their strong traditions. The reasons could be aplenty-ranging from the outbreak of wars, outbreak of epidemics and even crumbling of societies under the burden of orthodoxy of religion and caste. -
From knowledge tradition to knowledge economy : Positive interludes in India higher education /
International Journal Of Educational Planning & Administration, Vol.5, Issue 1, pp.19-23, ISSN No: 2249-3093. -
U.R Ananthamurthy - A man more sinned against than sinning? /
Indian Literature, Vol.59, Issue 6, pp.138-147, ISSN No: 0019-5804. -
Unopened windows: European existentialism and Indian classrooms /
International Journal Of English Language Literature and humanities, Vol.3, Issue 9, pp.434-440, ISSN No: 2321-7065. -
Crisis and Man: Literary Responses Across Cultures
Journal of Business Management & Social Sciences Research, Vol-1 (3), pp. 29-31. ISSN-2319-5614 -
Countering educational disruptions through an inclusive approach: Bridging the digital divide in distance education
The COVID-19 pandemic has created havoc across the globe, irrespective of governments, industries, and societies. The education sector is one of the most extensively affected by the global health crisis, manifesting expansive negative consequences to learners from various age groups and socioeconomic statuses. Despite the predicaments posed by the pandemic, academic institutions continue to provide education through a distance learning approach. However, the educational disruptions have underscored the lack of digital resources and competencies, excluding poor and unconnected students. Likewise, transitioning to remote education exposed the digital divide and inequalities that have been neglected for a long time. If the ultimate objective is to provide distance education, it is vital to devise solutions to problems faced by underprivileged students. This chapter investigates these challenges that impede the successful adoption of distance education and offers strategies to counter the disruptions as it seems apparent that online education is here to stay. 2022, IGI Global. All rights reserved. -
Efficient Load Balancing and Resource Allocation in Networked Sensing SystemsAn Algorithmic Study
In the current environment, data generation and data transmission are increasing exponentially in day-to-day life. These exponentially growing data might create heavy traffic when transmitted between systems. Also, this affects many functionalities like configuration of networked systems, system and routing configuration parameters, load managing factors of network devices, etc. A dynamic traffic control mechanism needs to be adopted with the help of load-balancing algorithms and efficient resource allocation mechanisms to deal with heavy data traffic. Load balancing algorithms in networked sensing systems aim to distribute the workload evenly among sensor nodes to optimize network performance and energy efficiency and prolong the network lifetime. Resource allocation mechanisms in a networked sensing system involve allocating and distributing network resources efficiently, such as energy, bandwidth, processing power, etc., to optimize performance and increase the networks lifetime. To achieve efficient resource allocation with a balanced load, notable works have been done in optimization and machine learning. The work gives a scientific analysis of traditional and Artificial Intelligence algorithms from a centralized and distributed perspective. Researchers can take this analysis forward when deciding on algorithms based on their application and infrastructural needs. 2025 Scrivener Publishing LLC. -
Epilepsy Detection Using Supervised Learning Algorithms
In the current scenario, people are suffering and isolated by themselves by seizure detection and prediction in epilepsy. Also, it is highly essential that it needs to be identified through wearable devices. Researchers discussed this issue and outlined future developments in this field, suggesting that Machine Learning (ML) techniques could radically change how we diagnose and manage patients with epilepsy. However, as data availability has increased, Deep Learning (DL) techniques have become the most cutting-edge approach to adopt and use with wearable devices. On the other hand, large amounts of data are needed to train DL models, making overfitting problematic. DL models are created with open-source toolboxes and Python, allowing researchers to create automated systems and broaden computational accessibility. This work thoroughly overviews deep learning (DL) methods and neuroimaging modalities for automated epileptic seizure identification. It covers several MRI and EEG techniques for epileptic seizure diagnosis and treatment programmes designed to treat these seizures. The study also covers the difficulties in precise detection, the benefits and drawbacks of DL-based strategies, potential DL models and upcoming research in this area. 2024 IEEE.