Category Archives: Uncategorized

Authors: Suranga Jayawardene

Abstract: As customer expectations for rapid, personalized, and seamless support continue to rise, organizations are increasingly turning to advanced technologies to transform their customer service operations. AI-augmented case management, when integrated with Salesforce Omnichannel Routing, represents a paradigm shift in how businesses handle customer inquiries and support tickets. This integration leverages artificial intelligence to automate, prioritize, and intelligently route cases across multiple channels—such as email, chat, phone, and social media—ensuring that each customer interaction is handled by the most suitable agent or automated system. The result is a dramatic improvement in both operational efficiency and customer satisfaction. AI-driven tools within Salesforce analyze incoming cases based on urgency, sentiment, past resolutions, and agent skill sets to make real-time routing decisions. This automation not only reduces manual workload but also minimizes wait times and increases first-contact resolution rates. Furthermore, AI-powered chatbots and knowledge base integrations offer instant answers to common queries, deflecting a significant portion of cases before they reach human agents. Predictive analytics help identify cases at risk of escalation, enabling proactive intervention. The Omnichannel Routing feature of Salesforce provides a unified platform for managing work items from all customer touchpoints, allowing agents to work across channels without switching systems. This flexibility, combined with AI’s analytical capabilities, ensures that agents are always assigned work they are best equipped to handle, maximizing productivity and job satisfaction. The convergence of AI and omnichannel routing in Salesforce not only streamlines case management but also equips organizations with actionable insights to continuously refine their support processes. In summary, AI-augmented case management with Salesforce Omnichannel Routing empowers businesses to deliver faster, more accurate, and personalized customer service. By automating routine tasks, optimizing agent assignments, and leveraging predictive insights, organizations can address the challenges of growing support volumes and complex customer needs, ultimately driving higher customer loyalty and operational excellence.

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Published by: vikaspatanker

Authors: Andriy Petrenko

Abstract: Prompt engineering stands as a cornerstone for maximizing the efficiency and effectiveness of AI-driven assistants like Salesforce Einstein Copilot. This article explores the advanced techniques and best practices for prompt engineering that enable organizations to extract the highest value from their AI investments. By focusing on clarity, specificity, and contextual relevance, prompt engineering ensures that Einstein Copilot delivers accurate, actionable, and personalized responses across a wide range of business processes. The article delves into the integration of prompt engineering within Salesforce’s ecosystem, emphasizing how custom prompts, iterative testing, and ethical considerations contribute to seamless user experiences and robust automation. Through practical examples and expert insights, the article demonstrates how prompt engineering not only streamlines workflows but also enhances decision-making, productivity, and scalability. The discussion is grounded in real-world applications, highlighting the role of prompt engineering in automating routine tasks, supporting complex decision-making, and maintaining consistency as organizational needs evolve. Ultimately, this article serves as a comprehensive guide for Salesforce administrators, developers, and business leaders seeking to harness the full potential of Einstein Copilot through strategic prompt engineering.

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Authors: Anastasia Mikhailova

Abstract: Customer service in the digital age is no longer just about resolving queries—it is about delivering personalized, proactive, and predictive support experiences. Salesforce Service Cloud, a leading platform for customer service management, has evolved significantly with the integration of artificial intelligence through Salesforce Einstein. Among the most transformative components of this AI suite are Einstein Intent and Next Best Action models. These tools empower service teams to automate case routing, understand customer sentiment, and deliver contextually relevant recommendations to agents and customers in real time. This article delves into the depth of how these models enhance Service Cloud capabilities and revolutionize service delivery. Einstein Intent categorizes customer service interactions based on intent using natural language processing (NLP), allowing automated case classification and routing to the appropriate agent or department. Meanwhile, the Next Best Action model leverages predictive analytics and machine learning to recommend tailored actions that maximize customer satisfaction, loyalty, and business value. By embedding these intelligent tools into the Service Cloud, organizations can reduce average handling time (AHT), improve first contact resolution (FCR), and elevate customer satisfaction (CSAT) scores. This paper thoroughly explores the mechanics of these AI-driven models, their integration within the Service Cloud, real-world use cases, and best practices for implementation. It also addresses challenges in data quality, model training, and change management, providing a holistic guide for businesses aiming to harness the full potential of AI in customer service. Through technical insights and strategic frameworks, this article serves as a comprehensive resource for service leaders, Salesforce administrators, and AI architects looking to optimize their service operations and deliver intelligent, human-centric support experiences.

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Authors: Aibek Tursunov

Abstract: The rapid evolution of artificial intelligence (AI) has transformed the way organizations approach customer relationship management, service delivery, and workflow automation. Salesforce, as a leader in enterprise software, has pioneered the integration of agentic architectures and hybrid AI-human collaboration models through platforms like Agentforce. This article explores the foundational principles of agentic architecture, its implementation within Salesforce, and the transformative impact of hybrid AI-human collaboration on business operations. Agentic architecture refers to the design of intelligent systems capable of sensing environments, making decisions, and acting autonomously within predefined parameters. These architectures are not monolithic; they encompass a spectrum of models, including single-agent, multi-agent, vertical, and horizontal systems, each suited to different operational needs. Hybrid architectures, which blend the strengths of these models, are particularly relevant in modern business environments where flexibility, scalability, and adaptability are paramount. Salesforce’s Agentforce platform exemplifies this hybrid approach by enabling seamless integration between AI agents and human workers. The platform leverages large action models to automate repetitive tasks, analyze vast datasets, and generate actionable insights, all while empowering human employees to focus on high-value, judgment-driven activities. This collaboration is not about replacing human labor but augmenting it, fostering a workforce where machines and people work in tandem to achieve superior outcomes. The article delves into the technical underpinnings of agentic architectures, the role of frameworks and controls in ensuring ethical and effective AI deployment, and the practical benefits of hybrid collaboration for businesses. By examining real-world use cases, metrics for success, and the evolving landscape of AI governance, this article provides a comprehensive overview of how Salesforce is redefining the future of work through agentic AI and hybrid collaboration.

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Authors: Mr. Adnan Shafiq Mangaonkar, Ritesh Kumar Indrajit Sharma

Abstract: India's rapid adoption of biometric technology has positioned it as a global leader in AI-driven identity recognition systems, with over 1.3 billion citizens enrolled in the Aadhaar database. This research examines the evolving trends and emerging threats in biometric data usage across India's digital ecosystem through secondary data analysis. The study analyzes five comprehensive case studies spanning government identification systems, law enforcement surveillance, banking sector authentication, consumer mobile applications, and healthcare implementations. Key findings reveal a biometric market valued at INR 24,303.6 crores in 2024, growing at 12.18% CAGR, alongside concerning security vulnerabilities including 815 million healthcare records breached in 2023 and a 300% increase in biometric data breaches between 2020-2023. The research identifies critical gaps in privacy frameworks, discriminatory policing practices using 80% accuracy thresholds, and inadequate regulatory oversight of consumer applications. While biometric authentication has enhanced financial inclusion and service delivery efficiency, significant threats include deepfake attacks, algorithmic bias, and mass surveillance capabilities. The study recommends strengthened data protection laws, transparent AI governance frameworks, and enhanced user consent mechanisms to balance technological innovation with fundamental privacy rights.

DOI: http://doi.org/10.5281/zenodo.15813266

 

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Authors: Assistant Professor Mr. Sabir Nasir Mujawar

Abstract: Micro, Small, and Medium Enterprises (MSMEs) have emerged as a powerful force driving credit growth in the Indian banking sector, overtaking traditional retail lending segments. This research investigates the shifting dynamics of credit disbursement between MSMEs and retail loans using secondary data from RBI reports and case insights from Economic Times articles (2025). With improved asset quality and government-backed credit guarantee schemes such as CGFMU and ECLGS, banks are increasingly investing in MSME lending. This paper explores how digital infrastructure (e.g., TReDS platforms), fintech collaborations, and monetary policies have catalyzed MSME credit expansion. Through a case study and trend analysis of credit disbursal data from FY 2022–25, we examine sectoral credit performance, NPA levels, and structural challenges. The study concludes with strategic recommendations to sustain credit quality and unlock further growth in India’s MSME sector.

DOI: https://doi.org/10.5281/zenodo.15813033

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Authors: Ms. Ifrah Kampoo, Ms. Tejashree Khandekar, Dr. Jasbir Kaur, Assistant Professor

Abstract: – In healthcare demand of giving a rapid solutions led in digital health services. The chatbot employs natural language processing to interpret user inputs and leverages supervised machine algorithms. We use Natural Language Processing(NLP)extracting the structure symptoms from the text. ML algorithm use like Naive Bayes use by Probabilistic mode good for text classification fast works with small datasets.Another algorithm Decision Tree use to Rule-based predicate from symptom combinations easy to interpret and fast. Random Forest also use by Ensemble of decision trees used in more accurate, handles noisy data well. Support Vector Machine(SVM) that algorithm used as Bnary/multi-class classification of symptoms high accuracy, effective with high-dimensional data. An associated symptom-disease dataset is used to train and validate the model. Technology is accelerating innovations in healthcare domain has increasing people living years.

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Authors: Ravi Prakash Jaiswal, Manish Saraf, Vijendra Pratap Singh, Ambuj Kumar Misra

Abstract: Nitrogen (N) deficiency remains a major constraint on cereal productivity because it reduces chlorophyll formation, canopy photosynthesis, and grain filling, while blanket fertilizer practices often fail to match within-field variability and reduce nitrogen use efficiency (NUE) (Govindasamy et al., 2023). Although destructive sampling and laboratory diagnostics are accurate, they are slow and difficult to scale for timely, spatially targeted decisions in real farms (Fu et al., 2021). This study frames N deficiency detection as a cross-domain transfer learning problem and develops a cross-crop machine learning framework for wheat, maize, and rice using RGB imagery under field conditions. We harmonized and profiled three public datasets (wheat: 1,381 leaf images; maize: 1,200 canopy/plot images; rice: 1,500 leaf images with Leaf Color Chart-based labeling), applied standardized preprocessing, and trained baseline CNN and fine-tuned ResNet models with fixed random seeds and identical train/validation/test splits for reproducibility. Performance was evaluated under three scenarios: within-crop testing, direct cross-crop transfer without retraining, and domain adaptation using unlabeled target data. Four adaptation methods were benchmarked: CORAL, MMD, AdaBN, and Domain-Adversarial Neural Networks (DANN) (Ganin et al., 2016; Gretton et al., 2012; Li et al., 2016; Sun & Saenko, 2016). Baseline cross-crop transfer showed substantial generalization gaps (≈25–35 percentage points), with accuracy ranging from 47.6% to 56.2% across crop pairs, confirming severe domain shift (Fu et al., 2021; Pan & Yang, 2010). Domain adaptation improved average cross-crop accuracy from 51.7% (baseline) to 58.3% (AdaBN), 60.1% (CORAL), 64.6% (MMD), and 73.2% (DANN), with DANN delivering up to ~19% absolute improvement and the most consistent gains under challenging transfers (Ganin et al., 2016). Overall, results indicate that adversarial domain adaptation can substantially reduce cross-crop failure modes and supports more scalable nitrogen monitoring with reduced dependence on crop-specific labels, while practical deployment should include agronomic guardrails and uncertainty-aware decision rules for safe in-season recommendations (Fu et al., 2021).

DOI: https://doi.org/10.5281/zenodo.18514672

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Authors: Ndongkeh Nelson Maineh, Edith Bate Etakah, Edna Buhnyuy Visiy, Mbeck Prosper Wanlo

Abstract: Plastic and diaper waste are major pollution problems worldwide. Approximately 72% of global plastic and diaper wastes end up in landfills, exacerbating environmental degradation, highlighting the urgent need for valorization strategies. This study investigated the potential use of Polyethylene Terephthalate (PET) plastics, diaper wastes and sand for the production of pavement blocks, with the goal of developing an environmentally sustainable method for repurposing these waste materials into valuable construction products. Four formulations of the paving blocks were produced and their mechanical and physical properties evaluated through various testing methods. For the four formulations, the plastic (binder) content was maintained at a constant 45% while the diaper (aggregate) content was being varied across formulations, replacing sand at percentages of 0%, 2.5%, 5% and 10% respectively. The results showed that the compressive strength of the blocks remained relatively constant across the first three formulations, with values of 10.23 MPa, 10.25 MPa, and 10.25 MPa, respectively, but dropped significantly in the fourth formulation (5.57 MPa). This indicated that a 10% replacement of sand by diapers in the fourth formulation is not advisable, as their compressive strength falls below the minimum of 8.5 MPa recommended by the SNI 03-0691-1996 standard. Moreover, the results also indicated that both flexural strength and abrasion resistance of the blocks declined as the diaper concentration increased, suggesting an optimal threshold concentration for incorporating waste diapers into the waste blocks. Also, the water absorption rate of the paving blocks increased with increasing diaper concentration with values of 0.34%, 0.34%, 0.92%, and 2.18%, respectively. However, all values were within the <20% limit for high quality blocks (ISS 1077-1970 Standard) suggesting that the blocks can withstand extreme environmental conditions, such as floods. The research demonstrates the potential to co-valorize waste plastics, diapers, and sand for the production of sustainable pavement blocks.

DOI: https://doi.org/10.5281/zenodo.15811965

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Authors: MCA,M.Phil.,, Ms.S.Prathi

Abstract: – Multimodal sentiment analysis (MSA) integrates data from multiple sources, such as text, audio, and visual cues, to enhance the accuracy and interpretability of sentiment classification models. Traditional sentiment analysis predominantly relies on textual data, which can be limited in capturing non-verbal nuances like tone of voice or facial expressions. This paper explores the synergy between text, speech, and visual data in sentiment analysis tasks, addressing key challenges such as data alignment, feature extraction, and fusion techniques. We compare various fusion strategies, including early, late, and hybrid fusion, using state-of-the-art deep learning architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers. Experimental results demonstrate that multimodal approaches significantly outperform unimodal systems, providing higher accuracy and robustness in sentiment detection. We discuss the potential applications of multimodal sentiment analysis in fields such as social media monitoring, customer sentiment analysis, and healthcare. Finally, the paper outlines future research directions, emphasizing the need for more efficient fusion techniques and the incorporation of emerging models to advance multimodal sentiment analysis further.

DOI: https://doi.org/10.5281/zenodo.15811942

 

Published by: vikaspatanker