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Trivikram Ramchandra Kulkarni (19121983)
Trivikram Ramchandra Kulkarni was an influential educationist and licensed medical practitioner who passionately advocated for the study of Indian philosophy and psychology, particularly in an academic context. His pivotal contributions included establishing psychology as a formal discipline at universities in Mumbai. Kulkarnis groundbreaking research is encapsulated in his seminal works, notably Pranayama and Perception, which empirically examined the intricate link between pranayama and sensory perception as explicated by Patanjali. In Empirical Basis of Yoga, he explored the methodological applicability of Yoga within standard empirical frameworks, introducing the concept of samapattia state of focused mental function. In 1969, his work Psychosynthesis and Psycholysis compared the esoteric elements of the Rigveda with their empirical ramifications, depicting the human psyche as a dynamic interplay of goings (rta) governed by the transformative energy of agni, representing the cyclical nature of human experience and perception. 2025 selection and editorial matter, Braj Bhushan; individual chapters, the contributors. -
Shrikrishna Vasudeo Kale (19242012)
S.V. Kales research encompassed a broad range of topics, notably focusing on the mental health of officers and personnel in the Indian Merchant Marine, the dynamics of small group responses to frustration influenced by leadership behaviour, attentional deficits associated with psychiatric disorders in relation to arousal and pathology, and a psycho-social study on vocational planning that emphasized aspects of choice, decision-making, and indecision. His foresight allowed him to recognize and address pertinent social issues of his time. Through his impactful articles, he pored over critical matters such as education, the advancement of psychology, and corruption, which continue to resonate today. Additionally, he edited the influential book Child Psychology and Child Guidance and created the PSYCHRON psychometric instrument. This innovative tool evaluates human reaction time, movement time, and perceptions of time by analysing responses to controlled audiovisual stimuli differentiated by frequency, amplitude, intensity, and colour. 2025 selection and editorial matter, Braj Bhushan; individual chapters, the contributors. -
Imagined Communities in Bollywood: A Contrapuntal Reading of The Kashmir Files and Mulk
Under the Modi regime, Hindu nationalist ideologies have gained prominence, resulting in a growing utilisation of cinema as a political tool in India. This article explores Bollywoods role as a mass cultural medium in shaping communal identities through a contrapuntal analysis of two ideologically opposed films: Mulk (2018) and The Kashmir Files (2022). Utilising Benedict Andersons concept of imagined communities and Edward Saids contrapuntal reading technique, the research examines the role of these films in shaping narratives of majoritarian Hindus and minority Muslims. The Kashmir Files promotes a one-dimensional communal narrative that portrays Muslims as the aggressors and Hindus as the victims, thus bolstering Hindutva ideologies. Conversely, Mulk challenges this narrative by depicting the Muslim community as unjustly demonised and seeking justice in a pluralistic context. This article outlines Bollywoods involvement in the broader political discourse surrounding religious identity in India through a comparative analysis of themes like terrorism, visual stereotyping, and the representation of jihad. The findings are intended to contribute to the critical discussion surrounding nationalism, media representation, and communal politics in present-day South Asia. 2025, International Islamic University Malaysia. All rights reserved. -
Using Machine Learning Sentiment Analysis to Evaluate Students Learning Impact
For educational experiences and results to be improved, learning impact assessment is essential. Students' emotional reactions, which are crucial to their involvement and understanding, are frequently missed by traditional evaluation techniques. Through a review of student feedback, conversations, and course ratings, this study investigates the use of machine learning-based sentiment analysis to assess the impact of learning. Performance evaluations were conducted on a number of sentiment categorization models, including Nae Bayes, Support Vector Machines (SVM), Logistic Regression, Random Forest, Long Short-Term Memory (LSTM), and BERT. With an accuracy of 91.7%, the results show that BERT performs better than other models and offers more accurate sentiment classification. Accuracy and insights are further improved by combining textual, auditory, and visual signals in multi-modal sentiment analysis. The results show how sentiment analysis may be used to track feedback in real time facilitating adaptive learning techniques to raise student interest. Future studies should concentrate on expanding sentiment analysis applications to traditional and hybrid learning contexts, integrating multi-modal data, and ethical implications. 2025 IEEE. -
Policy Landscape and Incentives on AIPowered Green Finance
The application of AI in green finance has enormous potential to tackle climate change, but its effect would be determined by the strength and integrity of governance mechanisms as well as well- designed incentives. Good policy serves to set the bar but also incentives and responsible use in finance in AI applications. In the absence of such governance, exposures including algorithmic discrimination, data mining and unequal access to technology are likely to compound rather than advance social goals. Thus, AI governance has become a priority for governments, financial regulators and multilateral organisations. Policy frameworks achieve two parallel goals: on the one hand they provide boundaries to invest in technology and at the same time these very policy mechanisms direct capital towards environmentally friendly investment through incentives like tax benefits, financial grants, green debts etc. 2026 by IGI Global Scientific Publishing. -
Exploring AI-Driven Economic Decision Making and Role in Promoting Green Investment
Artificial Intelligence has assumed a disruptive role in the sphere of economic decision-making, specifically in the field of capital allocation towards green investments that would meet global sustainability requirements. Using machine-learning algorithms, neural networks, and big-data analytics, AI can offer greater accuracy in predicting economic patterns and risk assessment of the environment, and using AI can diversify portfolios with low-carbon assets, commercializing the old dichotomy between the financial value of profit and the eco-friendliness. This study discusses the transformations that AI-based tools are ready to make to the traditional economic paradigms, including the predictive analytics in terms of renewable-energy valuation, natural-language processing that would analyze sustainability reporting, or both in combination, a means of creating a paradigm shift where green investments would no longer be considered an act of charity, but rather a data-driven necessity of constructing long-term values. 2026 by IGI Global Scientific Publishing. All rights reserved. -
ANFIS-Based Multi-Sensor Data Fusion Model for Optimized Autonomous Vehicle Navigation Using Big Data and Filtering Techniques
The navigation of an autonomous vehicle depends mostly on the integration of multi-sensor data from sources such as LiDAR, GPS, radar, and cameras. Issues like sensor noise, data asynchrony, and fusion inaccuracies hamper reliable real-time decision-making. This paper proposes an optimized multi-sensor data fusion framework integrating big data analytics with modern filtering techniques to increase navigation accuracy and system robustness. The proposed model integrates Kalman Filter (KF), Extended Kalman Filter (EKF), and Adaptive Neuro-Fuzzy Inference System (ANFIS) for dynamic state estimation and adaptive noise accommodation. In addition, sensor reliability and position tracking are enhanced via Bayesian data fusion and Particle Filter. Simulation results show that the proposed technique is evidently superior to existing models in accuracy (1.5 RMSE), convergence time (0.98s), and latency (50 ms). The fusion system enhances stability and responsiveness in autonomous navigation and offers an intelligent transportation framework that can be deployed efficiently at a real-time scale. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2026. -
Emergency response to natural disaster victim identification: Blockchain to the rescue
Rapid, coordinated action by various stakeholders is required to respond effectively to a natural disaster. Suffice it; such efficiency has been missing in many previous rescue attempts. Is blockchain capable of making this happen? The Disaster Victim Identification (DVI) process is a sophisticated operation in which post-mortem (PM) identifying data, including fingerprints, DNA, and dental records, are acquired and matched with antemortem (AM) data from the missing people list. Although there are solutions to human identification, they must provide the tools required to achieve human identification promptly. Blockchain technology is one of the technologies that has gained much attention recently and is undergoing heavy media operations. It creates trustworthy, secure, and comprehensive ecosystems by disseminating siloed AM and PM data across systems, preventing breaches, redundancies, inconsistencies, and errors. Using real-world scenarios, the authors present several good use cases in this chapter to gain a holistic understanding of the challenges and how blockchain technology addresses such challenges and facilitates multi-jurisdictional data information sharing in conjunction with the upcoming distribution of patients electronic medical and dental records. 2025 Elsevier Inc. All rights reserved.