Category Archives: Uncategorized

Authors: Drbrindhas, Ms. P.Abirami In, Mr. Ajay.R, Mr. Anbarasan.R, Mr. Rishihesh .M.M, Mr.Safwan.S, Mr.Sriram.V

Abstract: This paper presents the design and implementation of a Smart Playlist Generator using Affective Computing — a real-time, AI-driven music recommendation system that personalizes playlists based on the user's emotional state. The system integrates three core components: (1) a Facial Emotion Recognition (FER) module built on OpenCV and Convolutional Neural Networks (CNNs) that classifies emotions in real time from webcam input, (2) a Natural Language Processing (NLP) module that supports Thanglish (Tamil- English transliterated) text commands for conversational interaction, and (3) a Spotify Web API integration that maps detected emotions to audio features such as valence, energy, and tempo to generate context-aware playlists. The system achieves an emotion recognition accuracy of 87– 90%, Thanglish command interpretation accuracy exceeding 90%, and a playlist-mood alignment rate of 85–90%, with an end-to-end latency of approximately 3 seconds. The architecture leverages HTML/CSS/JavaScript for the frontend, Node.js with Express for the backend, Firebase for data persistence, and Python-based AI modules for emotion and language processing. Experimental results confirm the viability of affective computing for dynamic, personalized music delivery, and the system demonstrates significant potential for next- generation human-computer interaction in multimedia platforms.

DOI: https://doi.org/10.5281/zenodo.19659822

 

Published by: vikaspatanker

Authors: Aarthi R, Ranjith S, Subash P, Surya T

Abstract: The Hospital-Based Smart Hematology Analyzer with Cancer Risk Alert is an advanced system designed to automate blood analysis while providing early cancer risk detection for organs such as the brain, lung, and skin. The system integrates a deep learning algorithm, InceptionV3, to analyse blood smear images and identify abnormal cell patterns indicative of potential malignancies. High-resolution images captured through an optical sensor are pre-processed and fed into the algorithm for feature extraction and classification. The hardware architecture includes a microcontroller interfaced with sensors and a display unit, interconnected through UDP communication to ensure fast, reliable, and real-time data transfer within the hospital network. The analyser automatically computes hematology parameters such as RBC, WBC, haemoglobin levels, and platelet count, while the AI module evaluates potential cancer risk based on morphological anomalies. Alerts and reports are generated for medical staff if any abnormal patterns are detected, facilitating prompt medical intervention. The working flow begins with blood sample collection, followed by automated slide preparation, image acquisition, and pre-processing. The processed images are analysed by the InceptionV3 model, which classifies the results and calculates risk levels. Data is transmitted via UDP to a central monitoring system for visualization, record keeping, and further evaluation by doctors. This system emphasizes automation, real-time analysis, and predictive diagnostics, aiming to reduce manual errors, accelerate clinical decision-making, and improve early cancer detection. It provides a cost-effective, intelligent, and scalable solution for hospital-based patient care.

Published by: vikaspatanker

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

Authors: Agus Prasetyo

Abstract: Cloud-based application deployment has become a fundamental practice in modern software engineering, enabling organizations to deliver scalable, reliable, and flexible applications. With the rapid adoption of cloud computing, various deployment strategies have emerged to address the challenges of performance, availability, and cost optimization. This study provides a comprehensive analysis of cloud-based application deployment strategies, including traditional virtual machine-based deployment, containerization, and serverless computing. It examines key approaches such as continuous integration and continuous deployment, blue-green deployment, canary releases, and rolling updates, highlighting their advantages and limitations. The role of orchestration tools like Kubernetes and infrastructure-as-code frameworks in automating deployment processes is also discussed. Additionally, the study explores critical factors such as scalability, fault tolerance, security, and resource management in cloud environments. Challenges related to deployment complexity, vendor lock-in, and monitoring are analyzed along with potential solutions. The findings suggest that adopting appropriate deployment strategies significantly enhances application performance, reduces downtime, and improves operational efficiency in cloud-based systems.

DOI: https://doi.org/10.5281/zenodo.19654560

 

Published by: vikaspatanker

Authors: Rina Kartika

Abstract: Microservices architecture has become a widely adopted approach for developing scalable and flexible applications by decomposing systems into independently deployable services. However, this distributed and loosely coupled nature introduces significant security challenges, including increased attack surfaces, inter-service communication vulnerabilities, and complex identity management. This paper presents a comprehensive review of security mechanisms in microservices architecture, focusing on strategies to ensure confidentiality, integrity, and availability of services and data. It examines key security practices such as API gateway protection, service-to-service authentication, encryption of data in transit and at rest, and the implementation of zero-trust security models. The role of container security, orchestration platforms like Kubernetes, and service mesh technologies in enforcing security policies is also discussed. Additionally, the paper highlights the importance of DevSecOps practices, continuous monitoring, and automated threat detection in maintaining secure microservices environments. Challenges such as scalability, policy management, and integration with legacy systems are analyzed, along with emerging solutions. The review concludes that a layered and integrated security approach is essential to effectively mitigate risks in microservices-based systems.

DOI: https://doi.org/10.5281/zenodo.19654386

 

Published by: vikaspatanker