Volume 14 | Issue 2 |

 Abstract

According to S??khya Philosophy prak?ti and puru?a are two ultimate realities. They have their own independent existence. This is the reason why this philosophy is known as dualistic realism. Prak?ti stands for matter and puru?a stands for self. S??khya is pluralistic because of its teaching that puru?a is not one but many. The S??khya distinction between puru?a and prak?ti is fundamentally that between the subject and the object. The subject can never be the object, and the object can never be the subject. The self and the non-self are radically different from each other. Thus, the dualistic metaphysics of S??khya is founded on the undeniably bipolar character of our everyday experience as made up of the experience and the experienced. The S??khya recognizes twenty-five principles of reality. Of these an individual soul is neither a cause nor an effect. Causation is transformation of the gunas sattva, rajas, and tamas. The soul is not composed of them, and is therefore neither a cause nor an effect. Prak?ti is the First Cause of the aggregate of all effects in the world. It is their ultimate cause, which is not the effect of any other cause. If it had any other cause, it would lead to infinite regress. Prak?ti is a cause but not an effect. It is not a modification of any other ultimate cause.

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Prak?ti, Three Gunas of prak?ti, The relationship between Prak?ti and the Gunas, Existence of prak?ti, Puru?a, Existence of puru?a. The Relation between puru?a and prak?ti.

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Mental health has emerged as a pressing global challenge, with escalating cases of stress, anxiety, depression, and other psychological disorders affecting individuals across all age groups. Traditional mental health support systems depend largely on human therapists, counselors, and clinical infrastructure. While effective, these systems face significant bottlenecks such as limited accessibility in remote areas, high consultation costs, long waiting times, and persistent social stigma that prevents many from seeking timely help. To overcome these challenges, ManasAI is developed as an AI-powered mental health chatbot designed to provide real-time emotional support, personalized mood tracking, and preliminary well-being guidance. Leveraging advanced Natural Language Processing (NLP) and sentiment analysis, the chatbot identifies underlying emotions in conversations and generates empathetic responses tailored to user needs. In addition, ManasAI integrates handwriting-based psychological pattern recognition, which enables it to analyze handwriting variations associated with different emotional states, thereby enhancing multimodal emotion detection. The technical framework of the system employs FastAPI as the backend for efficient and scalable deployment, MongoDB for secure and structured data storage, and AI/ML models including Transformer-based architectures for text analysis, Convolutional Neural Networks (CNNs) for handwriting recognition, and anomaly detection models to identify inconsistencies or potential cases of user deception. A fusion model is incorporated to combine multimodal signals, leading to more robust and accurate emotion recognition. Beyond simple conversational responses, ManasAI offers features such as mood trend visualization, risk scoring mechanisms, and safety escalation protocols to detect critical conditions like suicidal ideation or severe distress proactively.

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Mental Health, Chatbot, NLP, Sentiment Analysis, Pattern Recognition, Handwriting Analysis, Deep Learning, Anomaly Detection, Emotion Recognition, AI in Healthcare.

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As civilization grows advanced, man has become more vulnerable for many neurological conditions due to lifestyle, high expectation, unwanted stress, competition and pseudo society status. So that he adopted new habits like alcohol, smoking, tobacco and drug abuse. All these leads to the many life style disease. Neurological disorders are one of them. In Ayurveda, neurological disorders can be correlated with Vatvyadhi, Pakshaghata is one of them, caused due to vitiation of Vata Dosha. It can be correlated with Hemiplegia in modern science.Hemiplegia is commonest manifestation of stroke with the neurological defict, affecting face, limbs and trunk on one side or either side of body. In Pakshaghata Vata getting aggrevated dries up the Strotas and Snayu of one sideof body makes the organ of that side incapable of functioning and loss of sensation. Basti Karma is an ultimate treatment modality advised for Vatvyadhi, so in present study tried to find out mode of action of Basti chikitsa in Pakshaghata as being a neurological disorder.

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 Keywords

Neurological Disorder, Stroke, Hemiplegia, Vatvyadhi, Pakshaghata, Basti.

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?????? ????? ??????? (Indian Knowledge System - IKS) ???? ?? ??????, ???????? ?? ?????? ?? ????? ??? ?? ? ??????? ???? ??? ?????? ?? ???????? ???? ?????? ????? ???? ??, ????? ?????? ???????, ????-????? ?? ??????? ???????????? ?? ????? ???? ?? ? ????????? ??? ??? ?????? ?????? ???????? ???? ??? ?????? ?? ??? ???? ??? ??, ????? ???? ?????? ????? ??????? ?? ?????? ??? ???? ????? ???? ???? ? ???? 2020 ??? ????? ????????? ?????? ???? (NEP 2020) ?? ?????? ????? ??????? ?? ???? ?????? ?? ?????? ??? ???? ?? ????? ?????? ???? ?? ? ???? ??? ???, ??????, ?????? ?????, ???, ????????, ????, ???? ???????, ???, ???????, ??????? ?? ???? ??????? ?? ?????? ?????? ?? ?????? ?? ????? ?? ???? ??? ?? ? ???????? ??????? ??? NEP 2020 ?? ?????? ??? ?????? ????? ??????? ?? ???????, ???????? ?????????, ?????? ????????, ??????? ?????, ????? ?????? ??? ??????, ??????? ???????????, ????????? ??? ????????? ?? ??????? ??? ??? ???? ??? ???????? ???? ??? ?? ?

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????????? ?????? ???? 2020, ?????? ????? ???????, IKS, ????? ??????, ?????? ?????? ????????

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Microfinance Institutions (MFIs) have proved to be the best support for low-income people in achieving their financial goals. However, there has been always a risk for loan defaults which can threaten the stability of MFIs. Previous techniques have less emphasized on this problem. They were much more into data analytics. Therefore, in this study we used machine learning and deep learning techniques to improve loan default prediction using a large FinTech dataset. Four machine learning and one deep learning technique was developed and compared. The results show that Logistic Regression performed best overall with a ROC-AUC of 0.7186 and recall of 0.65. XGBoost achieved high accuracy of 84% but performed poorly in identifying defaulters. The Neural Network showed competitive performance with a ROC-AUC of 0.7055 and recall of 0.58. The study concludes that Logistic Regression remains a strong and explainable baseline model for MFIs, while advanced models like Neural Networks have good potential for loan default classification.

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Microfinance, Loan Default Prediction, Machine Learning, FinTech, SMOTE, Neural Networks, Logistic Regression.

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This study analyses Ashok Banker's Demons of Chitrakut via the framework of historical consciousness, exploring the novel's reinterpretation of conventional depictions of evil in the Ramayana. The study examines the intricate characterisation of demons and their socio-political contexts, illustrating how Banker opposes binary distinctions of good and evil, while elucidating the interplay between myth, memory, and cultural identity. The analysis demonstrates how the novel' s depiction of demons as multifaceted entities influenced by marginalization and structural inequities challenges conventional power dynamics and narrative legitimacy. Through this recontextualization, Banker elevates the epic from a mere moral narrative to a sophisticated examination of resistance, agency, and moral ambiguity. The study contends that this reinterpretation of the Ramayana illustrates the fluidity of historical consciousness in influencing cultural narratives and collective identity, while emphasizing literature's capacity to promote critical engagement with inherited cultural legacies. This study enhances our comprehension of how modern adaptations of ancient epics might function as mediums for tackling contemporary societal issues and reinterpreting conventional moral paradigms.

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historical consciousness, epic, Ramayana, narratives etc

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One of the main pillars of a progressive country is education. Teachers are the driving force behind the empowerment of individuals with knowledge, beliefs, and skills that significantly contribute to the development of a nation. Teachers stand as one of the most vital components of education. In order to make education more accessible and engaging, technology-enhanced learning has replaced traditional teaching methods. To ensure this, a dynamic teacher education programme is essential. The National Education Policy (NEP) 2020 emphasizes teacher education programmes and recognizes teachers as a key component of high-quality education. To guarantee professionally prepared and skilled teachers, a four-year Integrated Teacher Education Program (ITEP) was suggested. Teachers are prepared for both primary and secondary education through the four-year undergraduate Integrated Teacher Education Programme (ITEP). It integrates pedagogy, disciplinary knowledge, and practical training into one programme, giving teacher candidates a comprehensive education. The researcher tries to investigate the relevance of ITEP in transforming teacher education and its prospective opportunities and future prospects of ITEP in redefining teacher education in India and identifies important challenges linked to it and also provides some suggestion measures regarding it. Using an exploratory strategy, the researcher gathered data from a variety of government documents and publications that were sourced from different books and periodicals. The 4-year Integrated Teacher Education Programme (ITEP) signifies a transformational approach to teacher training in India as per NEP-2020. A Bachelor of Arts/science/commerce joined with a Bachelor of Education as an inclusive 4-year course, ITEP targets to produce teachers who are subject experts & Capable of pedagogical Skills. ITEP holds Significant relevance in the present educational context, especially light of evolving educational goals, pedagogical needs and national reforms such as the NEP-2020.This paper aimed to critically analyze the Problems, prospects and relevance associated with the implementation of ITEP in India, focusing on its implementation & potential to address systemic inefficiencies in teacher Education. In general, the ITEP course is deliberated to encounter the current & future educational requirement, generating highly qualified& adaptable teachers.

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ITEP, Teacher Education reforms, NEP-2020, relevance of ITEP

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Breast cancer develops when abnormal cells accumulate in the breasts. It was the most common cancer in women worldwide in 2020, with an estimated 2.3 million new cases being diagnosed. The aetiology of breast cancer is not yet fully understood; however, it is established that advancing age, familial history of breast cancer, genetic abnormalities, exposure to radiation, and hormonal factors are all recognised as potential risk factors for the development of this disease. The symptoms of breast cancer encompass the presence of a lump or mass in the breast, alterations in breast size or shape, skin dimpling or puckering, nipple discharge or inversion, and breast pain or tenderness. However, not all breast cancers manifest themselves in obvious ways; others are detectable only by mammography or other imaging procedures. Better patient outcomes and lower mortality rates can be achieved through early detection and precise diagnosis. Predicting breast cancer risk, recurrence, and survivability is an area where machine learning algorithms have made significant strides in recent years. This study focuses on utilising machine learning to create precise predictions regarding a range of outcomes related to breast cancer. To begin, a model is constructed to anticipate the probability of acquiring breast cancer before the onset of the disease. This is accomplished through the use of algorithms like Logistic Regression (LR), Decision Trees (DT), and Neural Networks (NN) to examine parameters including age, family history, hormone considerations, and lifestyle factors. After the model has been trained and tested on a sizable dataset of breast cancer patients and healthy individuals, a variety of metrics are used to evaluate the model's performance, including accuracy, sensitivity, specificity, and area under the receiver operating characteristic (AUC-ROC). Second, a model is constructed to foretell the likelihood of a return of breast cancer following initial clearance of the disease. This is accomplished through the use of algorithms like RF, gradient boosting, and deep learning to examine characteristics such tumour size, grade, receptor status, and treatment history. The model's performance is assessed in terms of a number of different outcomes, and it is trained and tested using data from breast cancer patients who have already had treatment and have been followed up on. Finally, a model is created to foretell how breast cancer patients will respond to treatment and whether they will survive. Support vector machines (SVM), Naive Bayes (NB), and K-nearest Neighbours(k-NN) are some of the algorithms used to analyse variables such patient demographics, tumour characteristics, and treatment history to reach this goal. Overall survival, disease-free survival, and progression-free survival are a few of the measures used to assess the model's performance once it has been trained and tested on a dataset of breast cancer patients with known outcomes. The overall goal of developing machine learning models for breast cancer prediction and survivorship is to enable earlier detection, personalised treatment planning, and improved patient outcomes, all of which have the potential to revolutionise breast cancer care.

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Background: Physical activity of an individual is positively related to aerobic capacity of cardio-respiratory system to supply oxygen to the skeletal muscle. Stroke reduces the physical activity of individual and many aerobic exercises are used to rehabilitate the stroke patients. So, there is a need to find which aerobic exercises will helpful to improve physical functional performance in Hemiparetic individuals. Objectives: To find the effect of aerobic exercises on physical endurance and functional performance of subjects. Methodology: Clinically diagnosed subjects (N=25) with hemiparesis were randomly assigned for study. They were treated with conventional therapy and aerobic exercises for 8 weeks. Assessments were done on first day and reassessed on fourth and eight weeks. Pre and post intervention outcomes were measured using Continuous scale Physical Functional Performance (CS-PFP). Result: Aerobic exercise intervention had significant effect (p<0.0001) on functional recovery in hemiparetic patients both statistically and clinically. Conclusion: Aerobic exercise intervention improves physical endurance and functional performance in hemiparetic patients. Key words: Aerobic Exercise, Hemiparesis, Functional Recovery, CS-PFP Scale.

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: Aerobic Exercise, Hemiparesis, Functional Recovery, CS-PFP Scale.

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