Volume 14 | Issue 2 |

 Abstract

Cybersecurity risk evaluation is a sophisticated decision-making challenge characterized by uncertainty and ambiguous expert opinions. Conventional models, which depend on exact numerical values, inadequately address the intrinsic vagueness of these evaluations. This paper introduces a comprehensive framework based on Interval-Valued Intuitionistic Fuzzy Soft Sets (IVIFSS), which is adept at managing such uncertainty by concurrently reflecting an expert's levels of belief, disbelief, and indecision for each criterion through intervals. We outline the IVIFSS model along with its aggregation techniques--restricted union and intersection--to amalgamate assessments from various experts, providing formal demonstrations of their algebraic characteristics. A comprehensive numerical case study evaluating IT infrastructure elements illustrates the framework's applicability. The findings reveal a clear prioritization of assets, with the customer database server identified as the most critical component. A comparative evaluation against alternative fuzzy methodologies presented in tabular format highlights the enhanced expressiveness and robustness of the proposed model. This research concludes that the IVIFSS framework offers a systematic, mathematically rigorous, and exceptionally effective approach for ranking cybersecurity risks, surpassing traditional methods.

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 Keywords

Cybersecurity, Risk Assessment, Interval-Valued Intuitionistic Fuzzy Soft Sets, Multi-Criteria Decision-Making, Uncertainty, Algebraic Properties

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The training and development function is a critical component of Human Resource Development. It is concerned with honing the skills and knowledge of employees through various learning initiatives. By investing in training and development programs, organizations empower their employees to acquire new competencies, enhance their performance, and adapt to the evolving needs of the organization and the industry as a whole. Effective training and development initiatives facilitate professional growth, boost employee engagement, and contribute to the overall success of the organization. The main objective of the study is that to know about the level of satisfaction with the benefits of the training programme is influenced by the productivity of employees working in garment industries in Tirupur District. For this purpose a sample of 200 was collected were simple percentage analysis and chi square analysis were used as a statistical tools to analyze the data and the conclusion is that the employees largely feel that management (through HR department) should endeavour to create a conducive environment for employees to successfully fulfill their learning needs while managing their work engagement. This can lead to increased employee satisfaction, engagement and overall effectiveness of the learning and development activities within the organization.

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Employees Satisfaction, Training Programmes and Tirupur District

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The colorful cosmetic retail marketing is revealing high potentiality existence in Bengaluru at present and attracting local and global players to manufacture the required cosmetics. Bengaluru is a fastest growing city in Asia and attracting one and all and is becoming home of many cultures and religions. Living styles of Bengaluru is undergoing a sea of change and mode of living of citizens of Bengaluru is ever changing. It is said that during the times of Harappa and Mohenjodaro and during Gupta dynasty ladies use to bundle their hairs and tied aromatically. Hence the 'beauty appreciation" dated back to the previous times. There exists a severe competition between local and global players as far as marketing of cosmetic products. Makers of cosmetic products must look with existing consumer behavior which is highly volatile at Bengaluru. A thorough understanding of consumers motivation, buyer behavior often means the difference of stay in the business or loose business. In this highly competitive environment, the retail manager has to collect useful information about usage pattern to prepare a workable strategy. In the light of these facts, the present study is an attempt to understand consumer behavior and other various factors influencing cosmetic usage.

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 Keywords

Cosmetics, Consumer Behavior, Competition, Global Brands, Local Brands

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 Abstract

Background: Ayurveda emphasizes the regulation of natural bodily urges (Vega) as a cornerstone of health maintenance. Among these, Adhara?iya Vega--the urges that should never be voluntarily suppressed--are described elaborately in the B?hattrayee. Suppression of these urges is considered a significant etiological factor for various systemic and psychosomatic disorders. Despite their clinical importance, a consolidated and comparative understanding of Adhara?iya Vega across the B?hattrayee remains limited. Aim: To comprehensively review the concept of Adhara?iya Vega as described in the B?hattrayi and to analyze their physiological, pathological, and clinical significance. Materials and Methods: A qualitative literary review was conducted using classical Ayurvedic texts of the B?hattrayee along with their authoritative commentaries. Results: All three treatises unanimously recognize the suppression of Adhara?iya Vega as a causative factor for disease. While Acharya Caraka provides a detailed pathophysiological explanation and disease outcomes, Acharya Sushruta emphasizes surgical and structural implications, and Vagbha?a presents a concise yet integrative overview. Variations are observed in enumeration, nomenclature, and emphasis, reflecting differences in clinical orientation. Discussion: The analysis highlights the role of Vega-dhara?a in disturbing Do?a gati, Agni, and Srotas, leading to both acute and chronic disorders. Conclusion: Adhara?iya Vega form a vital preventive and therapeutic principle in Ayurveda. A comprehensive understanding of their classical descriptions enhances clinical decision-making and reinforces the holistic approach of Ayurvedic healthcare.

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 Keywords

Vega dhaarana, Adhaaraneeya Vega, Enumerations, Symptom

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In the rapidly evolving digital environment of the present times, traditional intrusion detection systems (IDS) are increasingly struggling to detect highly advanced cyber-attacks. Traditional rule-based methods rarely detect unknown or zero-day attacks and are non-scalable or suffer from a high rate of false positives. To overcome these limitations, recent advances in artificial intelligence have introduced promising techniques i.e., machine learning (ML) and deep learning (DL) which enhance the precision and responsiveness of IDS. This work proposes a hybrid IDS model that combines the interpretability and efficiency of ML with the deep feature learning as well as pattern recognition capabilities of DL. The proposed hybrid model employs both signature-based as well as anomaly-based detection methods in an effort to maximize the detection rate of threats while minimizing false alarms. The proposed model is evaluated with standard benchmark datasets such as NSL-KDD as well as CICIDS2017 in an effort to demonstrate its functionality in real-world applications. Results demonstrate significant detection accuracy, scalability, and responsiveness enhancements compared to traditional and stand-alone AI-based approaches. The research provides a valuable and intelligent solution to modern cyber security issues and opens the door to future developments in explainable, adaptive, and real-time IDS systems.

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 Keywords

Intrusion Detection System (IDS), Machine Learning (ML), Deep Learning (DL), Hybrid Model, cyber security, Anomaly Detection, Zero-Day Attacks

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The concepts of duty, desire, and detachment occupy a highly significant place in the religious philosophies across the continents. Among the great many Indian philosophical texts, it is the Bhagavad-G?t? that offers one of the most comprehensive discussions on these concepts through its doctrine of Ni?k?ma Karma--action performed without attachment to its fruits. This paper examines Ni?k?ma Karma as articulated in the Bhagavad-G?t?, situating it within the broader framework of karma theory and its philosophical interpretations. This study also inquires how this Indian philosophical treatise reconciles the apparent contradiction between the inevitability of action and the binding nature of desire-driven deeds.

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Bhagavad-G?t? , Ni?k?ma Karma

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Kabir’s Tradition in the Context of the Bhakti Movement: Origin, Development, and Nature

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Origin

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The rapid growth of digital news platforms has intensified the spread of online misinformation, rendering manual fact-checking insufficient for real-time verification. While machine learning-based approaches enable scalable detection, they often suffer from limited interpretability, sensitivity to concept drift, and poor handling of contextual nuances such as satire and emerging events. This paper presents FakeCheck, a hybrid misinformation detection framework that integrates interpretable machine learning with a weighted, community driven human-in-the-loop verification mechanism. The system employs a Logistic Regression classifier trained on TF-IDF representations and engineered stylometric features to generate an initial credibility score. This score is subsequently refined using authenticated community feedback through a mathematically defined weighted consensus formulation. The proposed framework is implemented as a full-stack web application and evaluated on a benchmark fake news dataset. Experimental results show that the standalone classifier achieves strong baseline performance, while the hybrid framework demonstrates improved robustness in scenarios involving concept drift, satire, and low confidence predictions. The findings highlight that structured integration of human judgment with transparent machine learning models provides a practical and resilient approach for real-time misinformation verification.

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Fake News Detection, Human-in-the-Loop AI, Logistic Regression, Stylometry, Community Consensus, Explainable Machine Learning, Concept Drift.

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The Indian Knowledge System (IKS) is a comprehensive framework of thought, experience, and practice which represents a rich civilizational legacy, encompassing ancient scientific traditions, philosophical thought, linguistic heritage, traditional medicine, environmental and pedagogical approaches . It is a holistic system that emphasizes experiential learning, spiritual growth, and the pursuit of knowledge in harmony with nature and society. Modern initiatives, like the Ministry of Education's IKS Division, aim to preserve, promote, and integrate IKS into contemporary society and education. The National Education Policy (NEP) 2020 recognizes the critical role of IKS in shaping a holistic, value-based, and multidisciplinary education system. The policy emphasizes the promotion and preservation of India's diverse knowledge systems, including traditional practices in areas like Ayurveda, yoga, and sciences, and encouraging the use of modern technology to rejuvenate them. The government is establishing IKS centers in institutions and supporting research through programs like the IKS Internship Program to develop authentic and comprehensive material for textbooks and other educational resources. This paper examines the conceptual foundations of IKS, evaluates the policy provisions introduced under NEP 2020 to integrate IKS into modern education, and analyzes challenges and opportunities for implementation. Using a qualitative analysis of secondary data from government documents, academic literature, and institutional reports, the study finds that NEP 2020 provides a powerful framework to mainstream IKS through curriculum reform and institutional strengthening.

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Indian knowledge systems, NEP2020,Indegineous knowledge, Education Reforms, Curriculum of Indian education system.

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Cognitive decline, memory loss, and behavioral abnormalities are hallmarks of Alzheimer's condition (AD), a progressive neurological illness that significantly lowers a patient's quality of life and places a heavy financial burden on society. Cholinergic deficiencies, glutamatergic excitotoxicity, amyloid-beta aggregation, tau hyperphosphorylation, oxidative stress, and neuroinflammation are some of the pathways that contribute to the pathogenesis of AD. Among these, glutamatergic dysfunction and the cholinergic hypothesis are crucial for the development of emotional and cognitive disorders. N-methyl-D-aspartate (NMDA) receptor antagonists and cholinesterase inhibitors (ChEIs) are the mainstays of pharmacological treatment for AD symptoms. availability in synaptic clefts, boosting cognitive performance and cholinergic neurotransmission. By specifically preventing pathological NMDA receptor activation without interfering with normal synaptic transmission, NMDA antagonists, like memantine, control glutamatergic signaling, lowering excitotoxic neuronal damage and delaying the onset of disease.The pharmacologic mechanisms, clinical efficacy, safety profiles, and therapeutic limits of NMDA antagonists and ChEIs in the treatment of AD are thoroughly examined in this review. It also looks at new medication formulations, combination therapy trends, and possible future paths for cholinergic and glutamatergic system targeting. Gaining knowledge of the molecular interactions between various drug groups can help improve treatment plans, enhance overall Alzheimer's disease management, and improve cognitive outcomes. The NMDA-receptor blocker memantine and cholinesterase inhibitors (ChEIs), such as donepezil, rivastigmine, and galantamine, continue to be the mainstays of symptomatic medication for Alzheimer's disease (AD). While memantine is mostly used in moderate-to-severe stages of AD and may help stabilize cognition and behavior, ChEIs offer mild to moderate benefits in cognition and several daily living activities over the short to medium term. For moderate-to-severe AD, combination drugs (ChEI + memantine) is frequently utilized, and new research indicates that there may be additive benefits in certain cases. Safety profiles (dizziness, disorientation for memantine; bradycardia, GI symptoms for ChEIs) are often controllable but need to be monitored. Heterogeneity in response, limited disease-modifying effect, and the requirement for biomarkers to forecast benefit are important gaps.

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 Keywords

ChEIs, ACH, ACHE, CHEIS

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