Top 2% Researchers List – Indian Subset

The top 2% list of Researchers is out again. have created a publicly available database of top-cited scientists that provides standardized information on citations, h-index, co-authorship adjusted hm-index, citations to papers in different authorship positions and a composite indicator (c-score). Separate data are shown for career-long and single recent year impact. Metrics with and without self-citations, ratio of citations to citing papers, retracted papers (based on Retraction Watch database) and citations to/from retracted papers are also shown to facilitate mapping of potential research integrity issues. Scientists are classified into 20 scientific fields and 174 sub-fields (Science-Metrix classification). Field- and subfield-specific percentiles are also provided for all scientists with at least 5 papers.

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Tarangini Bank: Knowing the Customer using Artificial Intelligence

Tarangini Bank, a mid-sized private sector bank founded in 1946 in Mangaluru, serves 14 million customers through 1,150 branches. By 2026, UPI, fintech competition, a squeeze on low-cost deposits and new data-protection rules are reshaping Indian retail banking. The bank’s segmentation still rests almost entirely on account balance. As a result, campaign response rates are poor, offers miss the mark, and valuable customers leave without warning. Ananya Deshpande, the bank’s new Chief Digital and Analytics Officer, proposes a segmentation built on demographics and on how customers actually spend across categories and channels, using k-means clustering. Her team must deal with duplicate customer records, stale demographic data, unclear UPI transactions, privacy obligations and a sceptical branch network. A pilot produces six clear segments that cut across the old balance tiers. Deshpande must now recommend to the board how quickly to roll out the new segmentation, which method to use, and how to turn the insight into a more customer-focused bank.

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Customer Network Value and How It Is Estimated

Traditional customer relationship management has generally evaluated customers according to the economic value of their own transactions. Measures such as sales revenue, customer lifetime value (CLV), retention rate, and purchase frequency help organizations identify customers who generate greater direct financial returns. However, in digitally connected markets, a customer can create value that extends far beyond his or her own purchases. Customers influence friends, colleagues, online communities, social-media audiences, and other potential customers. They may provide referrals, generate reviews, create content, strengthen brand reputation, and facilitate access to other customers. The economic value generated through these relationships can be conceptualized as customer network value (CNV).

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Choosing Between Wireless Bridges, Outdoor Access Points and 4G/5G Routers for Remote Business Connectivity

A wireless bridge links two fixed locations, an outdoor access point serves Wi-Fi devices across an open area, and a 4G/5G router brings internet service to a site through a mobile network. Businesses comparing commercial outdoor wireless access point options should therefore start with the traffic path, not the largest advertised speed. This guide explains where each technology fits and when a combined design is more practical.

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How SMEs Can Build a Reliable Wi-Fi Network for Offices, Warehouses and Multi-Site Operations

Reliable business Wi-Fi does not come from one powerful router in the middle of a building. It needs a stable wired core, well-placed access point units, separate networks for different users, and simple monitoring. SMEs comparing ceiling-mounted access point options should first map where people work and which devices must stay online.

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Reliable Network Infrastructure for Scalable IoT and Edge Deployments

A scalable IoT network starts with the work each device must do. Sensors may send small readings, while cameras and edge computers can move large amounts of data. The network also needs stable power, safe access, and a clear path to business systems. Teams comparing outdoor wireless access point options should first map device locations, traffic, power sources, and physical barriers.

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Customer Management using Artificial Intelligence: The Case of NovaMart

This case study examines how NovaMart, a large omnichannel retailer, leverages artificial intelligence and machine learning to transform customer management from a largely intuition-driven activity into a data-driven and predictive process. The case focuses on the complementary application of unsupervised and supervised learning. NovaMart first integrates customer information from transactions, digital platforms, loyalty programmes, and customer-service systems to create a unified analytical data foundation. Using K-means clustering, the company identifies five behavioural customer segments: Premium Loyalists, Promotion Seekers, Occasional Explorers, Digital Enthusiasts, and At-Risk Customers. Classification models are subsequently developed to assign new customers to these segments and predict outcomes such as customer churn and campaign response.

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Agency vs. Partner Model: Which Actually Fits Thai Online Sellers?

Every growing online seller in Thailand eventually faces the same build-or-buy decision about marketing. Building in-house means salaries, management overhead, and a hiring market where good e-commerce marketers are scarce and expensive. Buying means choosing between two models that look similar on a proposal but behave very differently over twelve months: the traditional agency and the partner model. The difference isn’t branding. It’s structured how the work is scoped, how the incentives point, and who carries the thinking. Choosing wrong doesn’t just waste a retainer; it costs a year of compounding you don’t get back.

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Responsible AI: Why the Pillars of Responsible Artificial Intelligence Matter

Artificial Intelligence (AI) has rapidly evolved from a niche technological innovation into a foundational capability that influences nearly every aspect of society. Organizations increasingly deploy AI to automate decisions, augment human intelligence, personalize services, improve operational efficiency, and create new business models. AI systems now influence decisions related to healthcare, finance, education, governance, manufacturing, transportation, recruitment, and public safety. While these systems promise unprecedented opportunities for innovation and productivity, they also introduce significant ethical, legal, social, and technical challenges. Poorly designed AI systems can amplify biases, compromise privacy, generate misinformation, make unsafe recommendations, or produce decisions that are difficult to justify. Consequently, the conversation has shifted from merely developing more powerful AI models to ensuring that these models are developed and deployed responsibly. Responsible AI has to be a targeted objective by design and be central to design principles.

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Blockchain and Unclaimed Property: Can Distributed Ledgers Solve Government Data Transparency?

The conversation about blockchain has changed to the hype of cryptocurrency to the real-world, institutional use. The technology is under consideration in the public sector especially in the unclaimed property systems where it has been identified as a possible corrective to an existing transparency gap. Currently, over $100 billion in funds sit in state accounts, yet many citizens remain skeptical of government data accuracy due to opaque escheatment processes and manual reconciliation hurdles.

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