Daily Archives: April 20, 2026

Authors: Vansh Nigam, Mr. Pankaj Lalwani

Abstract: Artificial Intelligence (AI) is no longer just a futuristic concept; it has quietly become a part of our daily lives, influencing the way people search, shop, and interact with brands. From personalized recommendations on e-commerce platforms to virtual assistants answering queries in real time, AI has started to reshape how consumers make decisions. This research paper focuses on understanding the impact of AI on consumer behaviour, looking beyond the technology itself to explore how it changes trust, buying patterns, loyalty, and expectations. The study examines how AI creates value by offering convenience and personalization—consumers now expect brands to “know them” and provide solutions tailored to their needs. At the same time, it highlights challenges such as privacy concerns, over-reliance on algorithms, and the risk of losing the human touch in brand–consumer relationships. By analysing existing studies, market practices, and consumer perceptions, this paper aims to bridge the gap between technological advancement and human psychology. Ultimately, the research argues that AI is not just influencing consumer choices but also shaping a new kind of consumer—more informed, more connected, and more demanding. Businesses that can balance AI-driven efficiency with ethical responsibility and genuine human engagement will be the ones to build lasting trust in the age of intelligent technology.

DOI: https://doi.org/10.5281/zenodo.19658824

Published by: vikaspatanker

Authors: Dr. Ch. Venkata Krishna Reddy, B. Varun Tej, T. Prabhas, G. Vishnu Charan

Abstract: Accidents caused by fire result in severe damage to life and property, especially in hazardous and hard-to-reach areas. In order to minimize human risk and increase the efficiency of firefighting, a Fire Fighting Robot with ESP32 Camera is proposed and implemented. In this system, the Arduino Uno board is used as a primary controller. The ESP32-CAM is used for live video streaming through a web page for the user. The robot is designed to operate in two modes: manual mode and automatic mode. The modes are selected through a web page. In manual mode, the user controls the robot’s movement and views the live video feed. The ultrasonic sensor is used in manual mode for obstacle detection. Four flame sensors are used to detect fire. Once the fire is detected, the robot moves towards the fire source. A DC water pump is used to spray water on the fire and extinguish it. The robot’s movement is controlled using DC motors driven by an L298 motor driver. A servo motor is used for direction control of the water pump. A buzzer is used for alarm generation when the fire is detected. The robot is powered using a battery supply regulated using an LM2596 voltage regulator module. This project is a simple and cost-effective way of remote fire detection and firefighting using robotics and wireless monitoring techniques. It is useful for industrial areas, warehouses, and places where human access is hazardous.

DOI: https://doi.org/10.5281/zenodo.19658551

Published by: vikaspatanker

Authors: Anto Maurin Lisha L, Muthu M, Sadeesh P, Tamilarasan S

Abstract: Liver diseases such as fatty liver, cysts, and tumors require early and accurate diagnosis to improve patient outcomes. Ultrasound imaging is widely used due to its non-invasive and cost-effective nature; however, its heterogeneous characteristics, including speckle noise, low contrast, and variability across devices, make diagnosis challenging. This paper proposes a deep learning-based approach for the classification of liver diseases using heterogeneous ultrasound images. The system employs pre-processing techniques such as noise reduction, normalization, and contrast enhancement to improve image quality. A YOLO-based architecture integrated with convolutional neural networks is used for feature extraction and simultaneous detection and classification of liver abnormalities. Experimental results show that the proposed model achieves improved accuracy and robustness compared to conventional methods. The system supports real-time analysis and can assist clinicians in reliable and efficient liver disease diagnosis.

Published by: vikaspatanker

Authors: Pooja Sharma

Abstract: Network virtualization has emerged as a transformative technology in modern networking by enabling the abstraction of physical network resources into flexible, scalable, and programmable virtual networks. It allows multiple virtual networks to coexist on a shared physical infrastructure, improving resource utilization, isolation, and management efficiency. This review explores key network virtualization technologies, including Software-Defined Networking (SDN), Network Function Virtualization (NFV), and virtual overlay networks. It examines how these technologies decouple network control from hardware, enabling dynamic configuration, automated provisioning, and improved scalability in cloud and data center environments. The study also discusses the role of network virtualization in supporting cloud computing, IoT, and 5G networks. Furthermore, it highlights critical challenges such as performance overhead, security concerns, interoperability issues, and orchestration complexity. Emerging trends such as intent-based networking, edge virtualization, and AI-driven network management are also analyzed. The findings emphasize that network virtualization significantly enhances flexibility, efficiency, and scalability in modern network infrastructures.

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Published by: vikaspatanker

Authors: Vikram Singh

Abstract: Cloud-based solutions for big data processing have become essential in managing the massive volume, velocity, and variety of data generated in modern digital environments. Traditional data processing systems are often insufficient to handle large-scale datasets efficiently due to limitations in storage, computing power, and scalability. Cloud computing addresses these challenges by providing on-demand resources, distributed computing frameworks, and scalable storage systems for efficient big data processing. This study explores the role of cloud platforms in enabling real-time analytics, batch processing, and distributed data management. It examines key technologies such as Hadoop, Spark, and cloud-native data processing services that support parallel processing and fault tolerance. The study also highlights the integration of big data analytics with artificial intelligence and machine learning to derive meaningful insights from complex datasets. Furthermore, it discusses major challenges including data security, latency, data governance, and cost management. Emerging trends such as serverless computing, edge-cloud integration, and hybrid cloud architectures are also analyzed. The findings indicate that cloud-based big data solutions significantly enhance scalability, efficiency, and flexibility in data-driven applications.

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Published by: vikaspatanker

Authors: Neha Gupta

Abstract: Intelligent automation in IT systems represents the integration of advanced technologies such as artificial intelligence, machine learning, robotic process automation (RPA), and cognitive computing to enhance operational efficiency and decision-making. It enables organizations to automate repetitive tasks, optimize workflows, and improve service delivery with minimal human intervention. This study explores the role of intelligent automation in modern IT environments, focusing on its ability to streamline IT operations, reduce costs, and improve system reliability. It examines key components such as automated incident management, predictive maintenance, intelligent monitoring, and self-healing systems. The study also highlights the integration of AI-driven analytics to enhance automation capabilities and enable real-time decision-making. Furthermore, it discusses major challenges such as implementation complexity, integration with legacy systems, security concerns, and workforce adaptation. Emerging trends such as autonomous IT operations (AIOps), hyperautomation, and AI-driven orchestration are also analyzed. The findings indicate that intelligent automation significantly enhances efficiency, scalability, and resilience in modern IT systems.

DOI: https://doi.org/10.5281/zenodo.19657761

 

Published by: vikaspatanker