Volume 13 | Issue 9 |

 Abstract

Manipur, a state renowned for its rich diversity of languages and cultures, faces significant challenges in managing its library systems effectively due to the multilingual makeup of its population. The presence of numerous indigenous languages and cultural practices calls for libraries that cater to the specific needs of various communities. In this scenario, Artificial Intelligence (AI) offers a promising solution to enhance library services by facilitating better access to information, addressing language barriers, and supporting the preservation of native languages and cultural traditions. AI technologies can streamline information searches, provide personalized learning experiences, and foster inclusivity by bridging the gap between different linguistic groups. This paper delves into the potential applications of AI in Manipur's libraries, focusing on overcoming language barriers, ensuring cultural inclusivity, and safeguarding local knowledge. Drawing on relevant case studies and existing research, the paper emphasizes the importance of developing AI systems that are culturally sensitive and customized to meet the needs of Manipur's unique demographic. It discusses the practical aspects of implementing AI, such as creating language models for local dialects, promoting equal access to digital content, and involving local communities in the AI development process. The paper concludes by arguing that AI can revolutionize library services in Manipur, making them more inclusive and accessible while preserving the state's cultural heritage for future generations. However, the successful integration of AI into these systems requires a deep understanding of Manipur's cultural context and careful consideration of the challenges posed by its multilingual population.

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AI

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Creative Commons Attribution 4.0 and The Open Definition

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India has hosted successive refugee movements for more than seven decades--from Partition (1947) and Tibetans (1959) to Sri Lankan Tamils (1980s-2000s), Afghans, Chakma-Hajong communities, and, most recently, Rohingya from Myanmar. Yet India has neither acceded to the 1951 Refugee Convention and its 1967 Protocol nor enacted a national refugee law. Protection, therefore, relies on a patchwork of constitutional guarantees, ordinary immigration statutes, executive practice, UNHCR's limited mandate operations, and case-by-case judicial interventions. This paper critically examines India's refugee policy through a human-rights lens. Methodologically, it synthesizes constitutional doctrine, statutes (especially the Foreigners Act, 1946), Supreme Court and High Court decisions, UNHCR practice in India, and recent executive measures, including the rules operationalizing the Citizenship (Amendment) Act, 2019 (CAA). It finds persistent structural gaps--legal uncertainty around status determination, risks of detention and deportation, uneven access to socio-economic rights, and gendered vulnerabilities--exacerbated by a securitized approach to "illegal migration." Results indicate that while Articles 14 and 21 of the Constitution have provided a floor of protection, their application remains contingent and inconsistently enforced across refugee groups. The paper argues that adopting a rights-based, uniform refugee framework--aligned with India's constitutional values and international obligations--would reconcile humanitarian commitments with legitimate security concerns. It concludes with actionable reforms: a comprehensive refugee statute codifying non-refoulement, procedural safeguards against arbitrary detention, clear roles for UNHCR and domestic institutions, calibrated use of the CAA to avoid discrimination or statelessness, and regionally coordinated burden-sharing.

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 Keywords

Refugee protection, Non-refoulement, Securitized migration policy, Detention and deportation, UNHCR in India

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Creative Commons Attribution 4.0 and The Open Definition

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India being most populous nation in the world, with a population of approximately 1.428 billion people where in notably, over 40% of this population falls under the age group of 25, placing India with a median age of 28 years. This growth has contributed significantly to the nation's GDP which enabled India to secure the world's fifth-largest global economy with an estimated 4.0 trillion USD (Time- 2023, pewresearch -2023). Contrary to the demographic advantage, there are persistent scarcity of skills for employments. According to a modern study, only 45% graduates are employable with required skill set which indicates a huge gap between the academic curricula and industry demands. Recruiters are reluctant to recruit Gen Z candidates as they lack in teamwork, communication skills and managing conflicts at workplace due to inefficiency of applicability skills to incorporate acquired theoretical knowledge into practice. (time.com, 2024) In order to bridge this gap between the industry expectations and academic supply of Gen-Z, it is crucial to adapt activity-based classroom teaching pedagogy to empower the Gen-Z with employability skills. Addressing this challenge aligns with SDG 4 framed by United Nations General Assembly, 2015 which emphasizes the inclusive and equitable quality education and promote life-long learning opportunities for all (UN- DESA-SDG-2015). The framework of bloom's taxonomy theory, popularly accepted model across the educational bodies to deliver the curriculum efficiently. On the other hand, the levels of its applicability have impacted over the employability skills acquired by the students. The present study aims to understand the importance of Activity Based Classroom teaching strategies using the bloom's taxonomy framework towards the development of employability skills among Gen-Z using the statistical tool JAMOVI version 4 application and inferential statistical test.

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Creative Commons Attribution 4.0 and The Open Definition

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SDG-4, ABC teaching, employ-ability skills and Blooms Taxonomy, Sustainable Strategy.

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Creative Commons Attribution 4.0 and The Open Definition

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Abstract-- In order to enhance signal processing, this work explores the integration of neural networks (NN) at very-large-scale integration (VLSI) levels. In terms of SNR, processing time (latency), energy consumption, and accuracy of signal processing tasks, we suggested and implemented a neural-network-based VLSI based on NNs and compared it with a traditional DSP. Our findings show that, in comparison to DSP techniques, the NN-based circuit efficiently boosts SNR by up to 7 dB. According to latency measurements, the NN-based circuit outperforms the DSP, which has average latencies of 3.5 and 6.2 microseconds for single and complex jobs, respectively, with average latencies of 1.2 and 2.8 microseconds. Additionally, with a 45 mW power usage, the NN implementation reduces the average power by 40%. In contrast, DSP uses 75 mW. With 98.2% accuracies for signal filtering, 97.5% for denoising, and 99.1% for pattern recognition, NN-based circuits outperform DSP, which has accuracies of 95.7%, 92.3%, and 96.8%, respectively, according to accuracy study. These results highlight the better precision, throughput, and energy efficiency that the NN-based circuit can provide. In conclusion, neural network implementation on VLSI can offer notable benefits over DSP-based implementation, making it seem like a promising technology for advanced signal processing that demands high precision, high efficiency, and high performance. By using NN-based circuits in general signal processing applications like communications, imaging, and real-time data analysis, this study lays the groundwork for future advancements in VLSI.

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 Keywords

Networks (NN), Very-Large-Scale Integration (VLSI) Circuits, Digital Signal Processing (DSP), Signal-to-Noise Ratio (SNR), Latency and Processing Speed.

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The present study was conducted to investigate the profile characteristics of Soil hea;th card holders Khandwa (M.P.). One hundred twenty SHC Holders from Khandwa and Punasa were selected for the present study. The following conclusion has been drown from the finding of the study. Most the respondents who fall under the category of middle age group, educated Intermediate, medium level of farming experience, medium size of land holdings, Agri+ Animal Husbanry occupaied, medium level of annual income, medium level of social participation , medium level extension contact, mass media exposure, scientific orientation and innovativeness. The secondary data were obtained from various government offices, soil test laboratory, Krishi Vigyan Kendra and available publications. Data was analyzed with the help of suitable statistical tools. Analyzed data was tabulated and presented in the form of tables and charts. Percentage were used in the study .

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Creative Commons Attribution 4.0 and The Open Definition

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SHC, Socio-Economic, Respondents

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Creative Commons Attribution 4.0 and The Open Definition

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Titanium dioxide (TiO?) has emerged as one of the most promising photocatalysts for industrial wastewater treatment due to its exceptional properties including chemical stability, non-toxicity, and high photocatalytic activity. This review comprehensively examines the various synthesis methods for TiO? catalysts, their characterization techniques, and applications in removing diverse industrial pollutants. The paper discusses recent advances in TiO? modification strategies to enhance visible light activity and addresses the challenges and future perspectives in scaling up photocatalytic systems for industrial applications. Recent developments focus on overcoming TiO?'s inherent limitations such as low efficiency under visible light and rapid recombination of electron-hole pairs.

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Creative Commons Attribution 4.0 and The Open Definition

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TiO? photocatalyst, industrial wastewater, synthesis methods, photocatalytic degradation, environmental remediation

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Creative Commons Attribution 4.0 and The Open Definition

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The rapid growth of online financial transactions has increased the prevalence of fraudulent activities, posing significant challenges for banking and financial institutions. Traditional fraud detection systems often rely on static rule-based models that fail to adapt to evolving fraud patterns. This paper presents an advanced credit card fraud detection framework integrating state-of-the-art machine learning and deep learning techniques for improved accuracy and robustness. The proposed approach utilizes the publicly available Credit Card Fraud Dataset, which contains real-world anonymized transaction data with highly imbalanced class distribution. Multiple algorithms, including Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Artificial Neural Networks (ANN), were implemented and evaluated. Various data preprocessing strategies were applied, including feature scaling, data balancing using SMOTE (Synthetic Minority Oversampling Technique), and outlier removal. The comparative analysis reveals that ensemble-based methods, particularly XGBoost, achieved superior classification accuracy and F1-scores while maintaining low false positive rates. Furthermore, the study emphasizes the importance of handling data imbalance, hyperparameter optimization, and evaluation metrics tailored to fraud detection. The proposed framework offers a scalable, adaptive, and efficient solution for real-time fraud prevention in financial systems.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Credit Card Fraud Detection, Machine Learning, Deep Learning, Random Forest, XGBoost, Support Vector Machine, Artificial Neural Network, SMOTE, Class Imbalance, Financial Transaction Security.

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Creative Commons Attribution 4.0 and The Open Definition

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Anger, one of the seven deadly sins in the Bible, is a recurrent motif in the myths of other cultures as well. Indian epics are rife with examples of overwhelming rage. This paper analyses the psychology and actions of Amba, a relatively lesser-known character in the Mahabharata, whose anger at her mistreatment is so intense that she chooses to carry it to her next birth. Amba voluntarily undergoes severe penances and then immolates herself to hasten her rebirth to seek revenge that will assuage her unappeasable wrath. The root of Amba's self-destructive fury deserves a closer study, which has been attempted in this paper.

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Rage, revenge, Amba, Bhishma, The Mahabharata, svayamvar, marriage, gender

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Creative Commons Attribution 4.0 and The Open Definition

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ABSTRACT: The Integration of Artificial Intelligence (AI) into Knowledge Management (KM) systems is reshaping how organizations manage intellectual capital, enhance decision-making, and foster innovation. This paper aims to present the results of a systematic literature review (SLR), based on 745 peer-reviewed articles retrieved from ScienceDirect between 2000 and 2024, to investigate the evolving relationship between AI and KM. The review followed a structured methodology, including a predefined search strategy, selection criteria, and data analysis procedures, to ensure a comprehensive and unbiased assessment of current trends. Articles were selected based on their relevance to key AI technologies and their applications in KM processes, including knowledge creation, retrieval, and dissemination. Our analysis revealed that AI significantly enhances KM by automating knowledge processes, improving the conversion of tacit knowledge into explicit knowledge, and fostering collaborative learning and innovation (Williams & Li, 2023). AI-powered KM systems also enable the processing of large volumes of unstructured data, providing real-time, actionable insights that enhance decision-making and organizational agility (Jones et al., 2021). Furthermore, while AI can augment decision-making, there are limitations in how it captures the nuances of human tacit knowledge and cultural context. We also addressed future research directions, such as the development of more transparent and ethical AI frameworks and the need for further empirical studies that examine AI's role in KM across diverse cultural and industry contexts. By offering a comprehensive synthesis of recent literature, this study provides valuable insights into the opportunities and challenges of integrating AI into KM. The paper contributes to the understanding of AI's transformative potential in organizational learning and innovation while acknowledging the limitations and ethical considerations that must be addressed for sustainable AI-KM integration.

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Key words: Artificial Intelligence, Knowledge Management, Sustainable Growth, Augmented Productivity.

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Creative Commons Attribution 4.0 and The Open Definition

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This paper presents the Adaptive Dream Journey Analyzer (ADJA). This advanced AI platform leverages Natural Language Processing (NLP) and adaptive algorithms to interpret and analyze user-described dream experiences with remarkable contextual accuracy. In the modern era, where understanding The subconscious mind is growing in both clinical and personal significance. ADJA distinguishes itself by enabling dynamic, human-like interpretation of highly subjective dream narratives. [1] The system is engineered to comprehend, process, and generate insightful feedback on dream content, facilitating automated analysis for psychological insight, creativity enhancement, or digital self-reflection. ADJA employs sophisticated NLP techniques, including intent recognition (to understand the user's reflection aims), named entity recognition (extracting dates, characters, and settings from dreams), sentiment analysis (capturing the emotional undertone), and advanced contextual modeling, which preserves narrative coherence throughout a session. [2] personalized recommendations, and automating routine tasks. Blending state-of-the-art machine learning with rule-based interpretation patterns, ADJA adapts to unique user profiles and personal symbolism, delivering an evolving and personalized interpretation experience over time. Its applicability spans clinical psychology (assisting therapists with dream content exploration), creative industries (stimulating story generation and art), wellness technology (aiding with self-awareness and nightly mood tracking), and consumer-facing dream journaling tools. [3] (keeping the conversation coherent). The system combines machine learning models and rule-based logic, creating a hybrid approach that improves its adaptability and performance over time This work demonstrates how combining adaptive AI and modern NLP expands our potential to decode dream experiences, making subconscious analysis more accessible, engaging, and actionable than ever before. This project shows how using modern AI and NLP can improve user interaction and engagement through advanced automated dialogue systems.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Adaptive Dream Journey Analyzer, Artificial Intelligence (AI), Natural Language Processing (NLP), Dream Analysis, Conversational Agents, Adaptive Systems, Machine Learning (ML), Deep Learning, Intent Recognition, Named Entity Recognition (NER), Sentiment Analysis, Emotion Detection, Personalization, Contextual Understanding, Natural Language Understanding (NLU), Natural Language Generation (NLG), Human-Computer Interaction (HCI), Automated Interpretation, Dialogue Management, Psychological Asses

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Creative Commons Attribution 4.0 and The Open Definition