VOLUME 2, ISSUE 11, NOVEMBER 2025

Abstract: Artificial Intelligence (AI) in education refers to the use of intelligent technologies to enhance teaching and learning processes. AI can personalize learning by adapting content to individual students’ needs, provide real-time feedback, automate administrative tasks, and support educators in designing effective instructional strategies. The study explores the level of Artificial Intelligence (AI) literacy and digital competencies among the B.Ed teacher trainees from five districts of Karnataka such as Mysuru, Shivamogga, Hassan, Mandya, and Bengaluru. The present study uses a descriptive survey design by using self constructed Artificial Intelligence Assessment Scale (AIAS) which measure the three dimensions such as AIKU (AI knowledge & understanding), AIUA (AI use & apply), AISE (AI self-efficacy) and IE (AI ethics), data collected from 436 trainee and collected data analyses by using t-test and regression analysis. The result of the study revealed that the gender and the geographical location or background does not significantly impact on the learning and enhancing AI-related competencies but both I year & II Year exhibited similar mean scores, indicating consistent AI awareness and readiness regardless of their academic year. The study also revealed that the strong and positive predictive relationship between B.Ed teacher trainees’ understanding of AI concepts and their ability to apply AI in educational contexts, teacher trainees who possess stronger knowledge and understanding of AI tend to show higher confidence (self-efficacy) and stronger ethical awareness in using AI applications. The study recommends that integration of AI focused curriculum, ethical training of AI, pedagogical innovation and professional development for prepare future educators for AI- driven classrooms.

Keywords: Artificial Intelligence (AI) Literacy, B.Ed teacher trainees, TPACK, Digital competence.

Abstract: In this paper, we design a digital DSSS System using Cyclone II EP2C20F484C7 FPGA from ALTERA placed on education and development board DE-1 for the DSSS system with the transmitting and receiving sections according to the following parameters: -Clock frequency of the system 10 KHz.
-Length of spreading pseudo-noise code (PNC): is (16) chips.
Type generation of spreading code: Walsh codes (H16).
-Length of data bit: is 3 bits with (16) chips for every one bit.
-spread operation: X -Number of users: is eight.
-User 1 with Data (00) and pseudo-noise code:

Code 1= [ 1111111111111111]

1 → +1 , 0 → −1 ⟹ Code 1= [ 1111111111111111]

-User 2 with Data (01) and pseudo-noise code: Code 2= [ 1010101010101010]

1 → +1 , 0 → −1 ⟹ Code 2= [ 1 -1 1 -1 1 -1 1 -1 1 -1 1 -1 1 -1 1 -1]

-User 3 with Data (10) and pseudo-noise code: Code 3= [ 1100110011001100]

1 → +1 , 0 → −1 ⟹ Code 3= [ 1 1 -1 -1 1 1 -1 -1 1 1 -1 -1 1 1 -1 -1]

-User 4 with Data (11) and pseudo-noise code: Code 4= [ 1001100110011001]

1 → +1 , 0 → −1 ⟹ Code 4= [ 1 -1 -1 1 1 -1 -1 1 1 -1 -1 1 1 -1 -1 1]

-User 5 with Data (00) and pseudo-noise code: Code 5= [ 1111000011110000]

1 → +1 , 0 → −1 ⟹ Code 5= [ 1 1 1 1 -1 -1 -1 -1 1 1 1 1 -1 -1 -1 -1]

-User 6 with Data (01) and pseudo-noise code: Code 6= [ 1010010110100101]

1 → +1 , 0 → −1 ⟹ Code 6= [ 1 -1 1 -1 -1 1 -1 1 1 -1 1 -1 -1 1 -1 1]

-User 7 with Data (10) and pseudo-noise code: Code 7= [ 1100001111000011]

1 → +1 , 0 → −1 ⟹ Code 7= [ 1 1 -1 -1 -1 -1 1 1 1 1 -1 -1 -1 -1 1 1] e-ISSN 3108-026X International Multidisciplinary Research Journal Reviews (IMRJR) A Peer-reviewed journal Volume 2, Issue 11, November 2025 | DOI 10.17148/IMRJR.2025.021102 -User 8 with Data (11) and pseudo-noise code: Code 8= [ 1001011010010110]

1 → +1 , 0 → −1 ⟹ Code 8= [ 1 -1 -1 1 -1 1 1 -1 1 -1 -1 1 -1 1 1 -1]

These codes must be orthogonal to each other, so they are chosen according to Welch's codes via the HADAMARD matrix, for a (16-bit) codes, the H16 array should be chosen.

Keywords: CDMA, DSSS, Walsh Codes, PNC, PNCG, FPGA.

Abstract: Deepfake technology undermines the authenticity and integrity of digital media, requiring the creation of advanced autonomous detection systems. This study investigates the amalgamation of Vision Transformers (ViTs) and statistical artifact analysis to establish a resilient deepfake authentication framework. ViTs' capacity to extract global characteristics is combined with other statistical techniques in the suggested strategy to achieve high detection accuracy, generalisation, and attacker resistance. Tests on two benchmark datasets, Face Forensics++ and DFDC, demonstrate superior performance compared to previous convolutional models. Future studies on autonomous deep fake identification will focus on transparency, equity, and scalability.

Keywords: DFDC++, Face Forensics++, Visual Transformer, statistical analysis, autonomous authentication, and deep fake detection.

Abstract: This study investigates how social media platforms contribute to constructing, transforming, and transmitting collective cultural memory. It explores how these digital spaces preserve and alter cultural narratives surrounding historical events, social movements, and national identities. The article examines the mechanisms through which social media fosters memory sharing, amplifies specific cultural stories, and explores the selective nature of digital memory. By analyzing case studies across diverse regions and periods, the research will highlight how social media both consolidates and challenges traditional notions of cultural memory, particularly relating to modern digital archiving, citizen journalism, and the rise of usergenerated content. The study will also address issues of memory fragmentation and the implications of social media’s role in constructing and shaping collective identities in the digital age.

Keywords: Social Media, Cultural Memory, Collective Identity, Digital Archiving, Citizen Journalism, User-Generated Content, Memory Fragmentation.

Abstract: This paper explores the integration of Explainable AI (XAI) into healthcare to enhance transparency and trust in AI-driven diagnostic, risk assessment, and personalized treatment. Techniques applied include SHAP, LIME, and Grad-CAM towards interpretability enhancement without diminishing predictive accuracy by much. The study discusses the way XAI would help clinicians enable actionable insights that build patient trust through clear explanations. It addresses challenges such as model complexity and interpretability while highlighting the need for interdisciplinary efforts in designing user-friendly systems. A structured framework for XAI integration is proposed to enhance clinical decision-making, regulatory compliance, and patient engagement, thereby ensuring AI systems remain ethical, transparent, and inclusive

Keywords: Explainable AI (XAI), Healthcare AI, Clinical decision-making, Ethical AI

Abstract: The rapid emergence of new legal terminology – driven by technological innovation, digital governance, and cross-border regulatory expansion – has intensified challenges in multilingual legal communication. Traditional lexicographic and doctrinal approaches struggle to capture the dynamic evolution of terms in areas such as cyber law, fintech regulation, and AI governance, particularly where meanings shift across jurisdictions and languages. This study addresses the problem by modelling the emergence, transformation, and conceptual grounding of modern legal terminology through a combined computational–ontological framework. The research is theoretically anchored in computational linguistics, legal semiotics, and ontology engineering, positioning legal terms as both linguistic signs and nodes within structured conceptual systems. To operationalise this perspective, the study integrates neural language models (BERT, GPT, RoBERTa) with domain-specific ontologies to detect emergent terminology, trace semantic change, and align new terms with established legal concepts. The methodology employs multilingual corpus construction, corpus-driven term extraction, contextual semantic embedding analysis, clustering techniques, and ontology alignment using OWL and SKOS-based frameworks. Semantic drift and conceptual novelty are quantified through embedding distance metrics and expert-validated mappings. The results are expected to contribute to the harmonisation of legal terminology across languages, reduce ambiguity in legal translation, and support more consistent legislative drafting. By combining the predictive and discovery capacities of neural models with the formal structure of ontologies, the proposed framework offers a scalable approach for monitoring terminological evolution and enhancing semantic interoperability in contemporary legal systems.

Keywords: Neural language models, legal terminology emergence, ontological frameworks, semantic interoperability; legal semiotics; BERT, GPT, RoBERTa, Corpus-based term extraction, semantic embeddings, ontology alignment.

Abstract: Cloud computing is entirely dependent on the internet. Cloud service providers (CSPs) such as Google, Amazon, Salesforce, Microsoft and others store and manage client data in their data centers. Maintaining and keeping an eye on the outsourced data is the responsibility of CSP. Data leakages insecure interfaces resource sharing data availability and inside attacks are just a few of the security problems and threats that can arise from having little control over the data. Security availability performance lack of standards higher usage costs and difficulty integrating with on- premise IT resources are the initial problems. Because they are unaware of where their data will be stored whether it will be available when they need it and whether it will be processed by others a large number of clients do not fully rely on the cloud environment. Cloud security is a significant issue that needs to be taken seriously because cloud users are more concerned with their data. Data security is a major cloud concern. Confidentiality integrity and availability are some of the factors that can help achieve data security. The study also looks at how hybrid frameworks and steganography can enhance authentication and secrecy in cloud environments. In order to improve the security of data while it is at rest a number of algorithms have been investigated.

Keywords: Cloud computing, Cloud Service Provider, Cryptography, Security.