Generative AI Applications In Consumer Engagement And Brand Communication
Authors: Chandana M, Vivek R Venkatesh
Abstract: GenAI is revolutionizing the way consumers interact with the brands and engage in conversation with them. This paper examines the ways in which brands use GenAI to develop personalized, co-creative, and immersive consumer experiences. Based on the results of a thorough review of the existing literature and current application practices, the paper highlights three main mecha-nisms through which GenAI can be used to increase engagement: personalization of content; collaboration on the content creation process between the brand and the consumer; and use of conversational AI assistants. It is found that despite the fact that GenAI is very effective at in-creasing personalization and engagement rates, its success heavily depends on the presence of human control and the ability to maintain balance between novelty of technology and emotional engagement. Moreover, consumer acceptance is shown to be age-dependent, with younger gener-ations being more comfortable with AI interactions. The paper provides recommendations on using GenAI in the communication strategy. Generative AI, Consumer Engagement, Brand Communication, Personalization, Human-AI Collaboration, Digital Marketing
Federated Deep Learning-Based Privacy-Preserving Cyber Defense Architecture For Distributed IoT Ecosystems
Authors: Dr. Vimala Roselin J, Jyothi V Kulkarni
Abstract: The explosive growth in IoT devices has created new cybersecurity challenges with an estimated 40 billion devices by 2030, with about 50% being vulnerable to cyber-attacks. The traditional centralized Intrusion Detection Systems (IDS) are faced with significant challenges such as com-munication bottlenecks, point-of-failure, and issues with privacy protection of data. In this paper, a novel cyber defense architecture is presented utilizing federated deep learning approach that incorporates hybrid deep neural networks along with privacy protections for distributed IoT networked devices. The proposed architecture makes use of a three-layer system architecture comprising IoT devices, edge gateways, and cloud cooperation using CNN, BiLSTM, and auto-encoders using a federated learning framework. Evaluation results have demonstrated the perfor-mance superiority of the proposed architecture with an accuracy of more than 99% in detecting anomalies, a 67% reduction in communication overhead, and effective resilience to adversarial attacks due to use of differential privacy.
AI-Driven Talent Acquisition Framework For Predictive Employee Performance Assessment
Authors: Surjya Narayan Sahoo
Abstract: The use of AI in talent acquisition is a significant paradigm shift that enables organizations to identify, evaluate, and select candidates based on their capability to excel in the required job roles. This paper describes an innovative talent acquisition framework built around AI technology and involving machine learning, natural language processing, and predictive analytics to improve the employee performance evaluation process. Specifically, the pro-posed talent acquisition framework encompasses resume scanning, soft skill assessment via conversational AI, and employee performance prediction using One-Class SVM classifiers. Based on quantitative analyses of different organizational databases, the framework pro-vides 95.28% accuracy in evaluating the performance of potential candidates, 40% decrease in recruitment process time, and 35% increase in performance prediction accuracy com-pared to conventional approaches.
Automated Detection Of Lung Diseases From Chest X-Ray Images Using Vision Transformers
Authors: Sakshi Shukla, Surjya Narayan Sahoo
Abstract: Lung diseases continue to be one of the top causes of deaths worldwide, which means that there is a need for efficient and fast diagnosis procedures. In this paper, we provide an overview of recent research in automated detection of lung diseases from chest X-rays with Vision Transformer (ViT). Traditional deep learning solutions such as Convolutional Neural Networks (CNNs) have proven themselves to be very effective. However, they suffer from the inability to model the crucial dependencies between distant spatial elements. Vision Transformers represent a major breakthrough in deep learning through the use of self-attention mechanism to model global relationships between the different parts of the image. This paper provides an overview of recent developments in the area of ViTs, covering such topics as hybrid CNN+ViT architectures, dual-stream attention fusion, and gaze guided architectures. Comparative analysis of the results shows that hybrid approaches, where global relationships are combined with traditional local feature extraction methods, produce the best results with accuracy rates above 98%.
CAD-Based Design Optimization Techniques For Sustainable Manufacturing
Authors: Ms. Amisha Malviya, Mr. Rahul Khobragade, Mr. Rahul Ghotkar
Abstract: The increasing demand for environmentally responsible industrial production has accelerated the adoption of sustainable manufacturing practices across various engineering sectors. Modern manufacturing industries are continuously seeking innovative approaches to minimize material consumption, reduce energy usage, lower production costs, and improve product quality without compromising functional performance. Among the emerging technological solutions, Computer-Aided Design (CAD) integrated with design optimization techniques has become a fundamental component in achieving sustainable manufacturing objectives. CAD enables engineers to create accurate three-dimensional digital models, perform virtual simulations, evaluate multiple design alternatives, and optimize product structures before physical manufacturing begins. This digital-first approach significantly reduces material waste, shortens product development cycles, mini-mizes production errors, and supports environmentally friendly manufacturing processes. Conse-quently, CAD-based design optimization has become an essential methodology for developing high-performance products while promoting economic, environmental, and social sustainability. Sustainable manufacturing aims to balance industrial productivity with environmental conserva-tion by utilizing resources efficiently throughout the product life cycle. Traditional product devel-opment methods often relied on repeated physical prototyping, trial-and-error experimentation, and manual design modifications, resulting in increased material waste, excessive energy con-sumption, longer production times, and higher manufacturing costs. In contrast, modern CAD software integrated with optimization algorithms enables designers to analyse product perfor-mance virtually, identify inefficient design elements, and implement improvements during the early stages of product development. This proactive engineering approach reduces unnecessary manufacturing operations and supports sustainable resource utilization across diverse industrial applications including automotive, aerospace, biomedical engineering, consumer electronics, renewable energy systems, and advanced mechanical manufacturing. Recent technological ad-vancements have transformed CAD from a simple drafting tool into an intelligent engineering platform capable of integrating simulation, optimization, artificial intelligence, cloud computing, digital twins, and additive manufacturing technologies. Advanced CAD environments incorporate Finite Element Analysis (FEA), Computational Fluid Dynamics (CFD), topology optimization, parametric modelling, generative design, multi-objective optimization, and life cycle assessment tools to evaluate product performance under various operating conditions. These integrated capa-bilities enable engineers to optimize product geometry, structural integrity, thermal performance, manufacturability, and environmental impact simultaneously. As a result, sustainable product development has become more efficient, accurate, and cost-effective than traditional engineering approaches.
