Volume 14 | Issue 5 |

 Abstract

Tax computation and filing are among the most critical yet cumbersome financial responsibilities for individuals and small businesses. The Indian tax system, governed by the Income Tax Act of 1961, encompasses a complex web of income categories, deduction provisions, exemptions, and frequently updated tax slabs that make the process highly prone to errors when performed manually. Millions of taxpayers across the country struggle annually to correctly compute their tax liability, identify eligible deductions, and complete their returns before the stipulated deadlines

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 Keywords

ClearTaxers-AI enables taxpayers to go from uploading their financial documents to receiving a comprehensive, filing-ready tax report in a fraction of the time it would take using manual or traditional software methods.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The rapid digital transformation in Karnataka's modern business enterprises has significantly enhanced operational efficiency and market reach. However, this shift has also exposed organizations to escalating cyber security threats, including data breaches, ransom-ware attacks, phishing scams, and insider threats. As businesses in Karnataka increasingly adopt cloud computing, IOT, and AI-driven technologies, the complexity of cyber risks intensifies, necessitating robust security frameworks. This research paper examines the key cyber security challenges faced by enterprises in Karnataka, including inadequate security infrastructure, lack of employee awareness, regulatory compliance gaps, and emerging threats from advanced persistent threats (APTs). The study also explores mitigation strategies such as AI-based threat detection, cyber security training, and adherence to global standards like GDPR (General Data Protection Regulation) and India's DPDP (Digital Personal Data Protection) Act. By analysing real-world case studies and industry trends, this paper provides actionable insights for businesses to strengthen their cyber resilience and safeguard critical assets in an evolving threat landscape.

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 Keywords

Cyber security, Digital Transformation, Karnataka Enterprises, Data Breaches, AI in Security, Regulatory Compliance, Cyber Resilience.

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Creative Commons Attribution 4.0 and The Open Definition

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The integration of Artificial Intelligence (AI) in recruitment and talent acquisition has marked a significant transformation in how organizations identify, assess, and hire candidates. This paper explores the impact of AI technologies, such as chatbots, automated resume screening, predictive analytics, and AI-powered video interviews, on the recruitment process. It delves into the efficiency, cost-effectiveness, and bias reduction that AI offers while highlighting ethical concerns like algorithmic bias, lack of human touch, data privacy, and transparency. The research further examines case studies of companies like Unilever, Hire Vue, and IBM, showcasing how AI tools have streamlined their hiring practices, improved candidate experience, and enhanced decision-making.

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 Keywords

AI in recruitment, Talent acquisition technology, Artificial Intelligence in hiring, AI-powered recruitment tools, Automated resume screening, Candidate sourcing with AI, HR analytics with AI, Future of recruitment technology, End-to-end recruitment automation, AI and workforce planning, AI in job matching, Automated interview analysis. AI in diversity hiring, Recruitment process innovation, Talent pool management with AI, Machine learning hiring algorithms, Candidate profiling with AI, Recruitm

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 Abstract

Artificial Intelligence is enabling personalized learning, which is quickly changing the face of higher education, enhancing academic efficiency, and automating tasks. This study investigates students' perceptions of AI integration in higher education, with a specific focus on engagement, trust, and ethical concerns. Conducted in Shivamogga, India, the study adopts a quantitative and descriptive approach, collecting primary data from 50 college students using structured questionnaires. The findings reveal that students widely use AI tools like ChatGPT, Gemini, and Grammarly, appreciating their role in improving academic performance, understanding, and feedback. However, notable concerns include data privacy, reduced human interaction, and over-reliance on AI, which may hinder critical thinking and job security. An independent t-test confirmed no significant gender-based differences in perceptions. The study suggests the necessary for ethical AI integration, inclusive training, and transparent usage policies. These insights can guide educators, policymakers, and AI developers in implementing student-cantered AI strategies that promote equity, efficiency, and ethical use in academic environments.

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 Keywords

Artificial Intelligence in Education, Student Perception, AI-Based Learning Tools.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The adoption of Artificial Intelligence (AI) in cybersecurity has transformed how organizations detect and respond to digital threats. AI systems process vast data in real-time, identify anomalies, and automate responses with great speed. However, this integration also introduces new risks--such as adversarial attacks, data poisoning, and the misuse of AI by malicious actors. The opaque nature of AI models complicates accountability, and reliance on high-quality data raises ethical and privacy concerns. This paper explores these challenges, presents real-world cases like Microsoft's Tay and IBM's DeepLocker, and suggests mitigation strategies such as adversarial training, secure data pipelines, and regulatory frameworks. While AI enhances cybersecurity, responsible deployment is crucial to prevent new vulnerabilities

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Cyber Security Challenges with AI Adoption

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The integration of Artificial Intelligence (AI) into performance management systems is transforming how organisations assess, monitor, and enhance employee performance. This study aims to explore the real-world applications of AI in performance management among organisations operating in Karnataka, India. Using a structured, questionnaire-based research design, primary data was collected from employees, HR professionals, and managers across various sectors including IT, finance, education, and manufacturing. The study investigates the extent of AI adoption, the perceived benefits and challenges, employee attitudes towards AI-driven evaluations, and concerns related to transparency, bias, and data privacy. Findings indicate that while AI tools have improved the objectivity and efficiency of performance reviews, concerns persist regarding ethical implications and the need for human oversight. The study contributes valuable insights into the practical implementation of AI in the Indian workplace and offers recommendations for more effective, ethical, and employee-friendly performance management systems.

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Artificial Intelligence, Performance Management, AI tools, HR Technology, Employee Appraisal, AI in HR, Organisations in Karnataka, Questionnaire-Based Research, Ethical Concerns, Feedback, HR Innovation

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The development of personal security apps is plagued by an important design flaw - their dependence on consistent internet connectivity, making these applications unusable in "dead zones" or when network services are throttled. In this paper, we propose 'Her Haven', a comprehensive offline-first Progressive Web Application (PWA) which provides support for women's safety needs, reproductive health, and mental well-being. The inclusion of React/Vite front-end, Workbox service workers for local caching, and Capacitor JS for full hardware access allows Her Haven to operate without any connection to the internet. A new Fallback State-Machine Routing Algorithm is developed, which constantly analyzes network payloads latency. When regular API communication fails, it will automatically route through native SS7 SMS messaging within 1.5 seconds. Our experiments performed in Faraday cages show 100% delivery success in sending distress signals without any connectivity at all. Also, we present the mathematical basis behind phase-dependent health apps and CSS matrices maintaining psychological immersions on mobile devices within the scope of desktop viewports. Her Haven manages to establish an absolutely redundant architecture for intersectional women's technology.

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Creative Commons Attribution 4.0 and The Open Definition

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Progressive Web Apps (PWA), Offline-First Architecture, Service Workers, Women's Empowerment, Fallback Algorithms, Personal Area Networks (PANs), Capacitor, Mobile Health (mHealth).

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Creative Commons Attribution 4.0 and The Open Definition

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Tamil is one of the oldest living languages in the world, and it has a very rich tradition of expressing love and emotion. In Tamil culture, romantic words are not only used to show affection but also to create a deep emotional bond between two people. These phrases often carry cultural meaning, poetry, and strong feelings. Because of this, Tamil romantic expressions are more powerful than simple direct words. Traditional Tamil literature, especially Sangam poetry, has always described love through nature, beauty, and emotion. Tamil lovers often compare the person they love to the moon, lightning, gold, or other beautiful things in nature. For example, words like anbe (dear love), kannamma (a loving term of affection), and thangam (gold) are often used to show closeness and warmth. These words do more than name a person. They also show care, respect, and deep emotional attachment. In modern times, Tamil romantic phrases are used both in face-to-face conversation and in digital communication

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Tamil culture, Romatic songs, Sangam literature, Words, Emotion in songs

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This systematic review examines the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques in Intrusion Detection Systems (IDS). The review analyses 162 peer-reviewed studies published between 2015 and 2024, focusing on supervised learning, unsupervised learning, and deep learning approaches used in Network-based IDS (NIDS), Host-based IDS (HIDS), and Hybrid IDS architectures. The findings indicate that deep learning models, especially Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and Generative Adversarial Networks (GAN), outperform traditional ML methods on benchmark datasets such as NSL-KDD, UNSW-NB15, and CICIDS2017, achieving F1-scores above 0.98. However, challenges such as class imbalance, adversarial attacks, concept drift, high false positive rates, and limited dataset generalization continue to affect practical deployment. Emerging technologies including Federated Learning, Explainable AI (XAI), and Graph Neural Networks (GNN) show promising results for future IDS development. This review provides a structured overview of AI-based IDS techniques, benchmark datasets, current challenges, and future research directions.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Intrusion Detection System (IDS), Artificial Intelligence, Machine Learning, Deep Learning, Cybersecurity, Anomaly Detection, Federated Learning, Explainable AI.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The increasing generation of agro-industrial and municipal wastes presents both an environmental challenge and an opportunity for sustainable resource utilization. These waste streams, rich in lignocellulosic and organic components, can be effectively valorized into industrial enzymes through microbial fermentation. This review critically examines recent advances in enzyme production using waste substrates, focusing on fermentation strategies such as solid-state fermentation (SSF) and submerged fermentation (SmF), microbial strain improvement, and process optimization. Key enzymes including cellulases, amylases, proteases, lipases, xylanases, and pectinases are discussed in relation to substrate utilization and industrial applications. Emerging technologies such as CRISPR-based strain engineering, omics integration, artificial intelligence-driven fermentation, and nanobiotechnology are also highlighted. Furthermore, techno-economic feasibility, life cycle assessment, and sustainability aspects are analyzed. Despite challenges such as substrate heterogeneity and scale-up limitations, waste-based enzyme production represents a promising pathway toward a circular bioeconomy.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Waste valorization; industrial enzymes; SSF; SmF; lignocellulose; circular bioeconomy

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Creative Commons Attribution 4.0 and The Open Definition