Volume-11 , Issue-12 , Dec - 2023 Go Back

In this work a methodology to estimate the effect of elevated temperature on a Line Start Permanent Magnet Synchronous Motor (LSPMSM) has been proposed. A simulated model of LSPMSM has been developed for Finite Element Analysis (FEA). The losses obtained from FEA were fed to a Lumped Parameter Thermal Network (LPTN) developed for LSPMSM in the LTSpice environment. The extent of demagnetisation was estimated and the FEA was conducted to identify the change in motor performance. The thermal modeling of the motor has been developed to evaluate the increase in temperature at different parts of LSPMSM under three different load conditions and even after switching off the motor. The results so obtained were validated on a physical setup. The results were analysed to ascertain the performance of the permanent magnets in LSPMSM.

Electromagnetic Analysis, LSPMSM, Lumped Parameter Thermal Network, Thermal Analysis, Demagnetisation

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Mousumi Jana Bala, Chandan Jana, Suparna Kar Chowdhury, Arindam Kumar Sil, "Modeling and Simulation of LSPMSM for Estimation of Thermal Effect during Different Load Condition," International Journal of Computer Sciences and Engineering, Vol.11, Issue.12, pp.1-8, 2023.

Revolutionizing supply chain dynamics, medicine-carrying drones facilitate a stable vertical flight for seamless supply transfers to remote areas. Empowered by Ardupilot, an open-source Unmanned Vehicle Autopilot software suite, these drones showcase advanced flight control capabilities. GPS ensures precise navigation, while Mission Planner, intricately connected with MAVLink, optimizes quadcopter operations. The RFID-Arduino Nano interface secures the container housing medical resources. Post-compilation, the code dynamically runs, extracting RFID card serial numbers for heightened security. Exclusive access is granted solely to cards with designated UIDs, fortifying overall mission security and reliability. This abstract encapsulates a technological nexus, converging advanced flight systems and robust security measures, propelling medicine delivery to new frontiers with efficiency and precision.

Unmanned aerial vehicle, ArduPilot, MAV, Mission Planner, RFID, UID, GPS.

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Gokula G., Minavathi, "A Quadcopter at Your Service-108 with Secure Delivery of Medicine," International Journal of Computer Sciences and Engineering, Vol.11, Issue.12, pp.9-15, 2023.

As businesses strive to maintain a competitive edge in today`s dynamic market, understanding and mitigating customer churn has become a critical imperative. This study explores the application of machine learning algorithms in Python for predicting customer churn, providing valuable insights to empower businesses in customer retention strategies. Leveraging a comprehensive dataset encompassing customer behavior, transaction history, and demographic information. Our methodology incorporates a diverse set of machine learning techniques, encompassing K-Nearest Neighbors (KNN), Support Vector Classifier (SVC), Random Forest, Logistic Regression, Decision Tree Classifier, AdaBoost Classifier, Gradient Boosting Classifier, and Voting Classifier. The outcomes reveal that the machine learning models demonstrate auspicious predictive capabilities, presenting businesses with a proactive means of identifying and mitigating potential churn risks. The discoveries from this investigation contribute valuable insights to the expanding realm of knowledge in customer relationship management, offering actionable guidance for businesses seeking to enhance customer retention strategies through the implementation of machine learning techniques in Python.

Machine Learning Algorithm, Analysis, Best Algorithms, Customer Churn Prediction.

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Megha Gupta, Anisha Patil, Ansh Tyagi, Deepanshi Singhal, "Broadening the Scope: Exploring Best Machine Learning Algorithms for Customer Churn Prediction," International Journal of Computer Sciences and Engineering, Vol.11, Issue.12, pp.16-20, 2023.

In this world of digitalized communication, effective communication crosses regional boundaries and linguistic obstacles making the world more connected. The demand for seamless multilingual communication has never been more important as corporations, institutions, and individuals engage on a worldwide scale. This article explores a trailblazing initiative that uses real-time translation and transcription services offered by Whisper-AI to transform the world of video conferences. The goal of the research is to create an AI model that easily interfaces a translation and transcription-based model to work in a real-time video conferencing system. Participants may converse in real-time without any language barriers by utilizing cutting-edge voice recognition and translation technologies.

Sound transcription, Sound translation, AI, Deep learning, Real-time, Language barrier, NLP.

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Kunal Kashyap, Prashant Singh, Anjali Verma, Satya Mishra, Prachi Goel, "VAARTALAP: Embedding Whisper-AI-like Model into a Video-Conferencing System to Aid Real-Time Translation and Transcription," International Journal of Computer Sciences and Engineering, Vol.11, Issue.12, pp.21-25, 2023.

Potholes present a substantial hazard to both road safety and the structural integrity of vehicles. This paper introduces a novel approach to pothole detection leveraging YOLOv8, an object detection algorithm, and TensorFlow.js. The proposed system aims to detect potholes accurately and swiftly by analysing live video feeds. The trained model exhibits promising performance metrics in pothole detection, with the bounding box precision at 0.822 and the mean Average Precision (mAP) value of 0.847, highlighting the model`s robustness in accurately localizing potholes. The proposed pothole detection system presents a promising solution for proactive road maintenance and safety enhancement. Its efficiency in real-time detection, combined with the adaptability of TensorFlow.js, holds the potential for widespread implementation, contributing to mitigating road hazards and infrastructure maintenance. The use of Tensorflow.js allows JavaScript developers to work with YOLOv8 reducing the dependency on Python for this purpose. The Pothole Detection and Reporting System with YOLOv8 and Tensorflow.js provides quite promising results.

Object detection, YOLOv8, TensorFlow.js, Road safety, Pothole detection.

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[3] S. R. Kuthyar, R. S., V. Rasika, S. Manjesh, R. Girimaji, R. Davis Arjun, P. S., "An Intelligent Pothole Detection and Alerting System using Mobile Sensors and Deep Learning," 2021.
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Nidhi Ruhil, Devansh Sahni, Anushaka, Anurag Wadhwa, Anjali Sharma, "Pothole Detection and Reporting System Implementation Using Yolov8 and TensorFlow.js," International Journal of Computer Sciences and Engineering, Vol.11, Issue.12, pp.26-31, 2023.

Data privacy and security have become paramount concerns in the realm of artificial intelligence (AI) due to the increasing reliance on vast datasets for training AI models. This review paper explores the potential of blockchain technology to enhance data privacy and security in AI applications. Blockchain, known for its core features of decentralization, immutability, transparency, and security, offers a promising framework to address data privacy challenges. Keywords like decentralized data storage, access control mechanisms, data provenance, and privacy-preserving machine learning are discussed in the context of blockchain integration with AI. Several use cases, including healthcare, finance, supply chain, and identity verification, demonstrate the practical applicability of blockchain in safeguarding sensitive data. However, challenges related to scalability, regulation, and adoption must be addressed. The paper concludes by highlighting emerging trends, research directions, and the importance of ongoing efforts to harness blockchain`s potential for preserving data privacy in AI.

Blockchain, Data Privacy, Artificial Intelligence, Decentralized Data Storage, Access Control, Data Provenance, Privacy-Preserving Machine Learning, Use Cases, Challenges, Emerging Trends.

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Shashank Saroop, Radha, "Blockchain Based Data Privacy through Artificial Intelligence: Review," International Journal of Computer Sciences and Engineering, Vol.11, Issue.12, pp.32-37, 2023.

This research explores the implementation of a state-of-the-art attendance system utilizing face recognition technology with a focus on Haar Cascade and Local Binary Pattern Histogram (LBPH) algorithms. The primary objective of this system is to revolutionize traditional attendance management methodologies by incorporating computer vision techniques. The Haar Cascade algorithm is employed for precise face detection, ensuring accurate identification of facial features, while the LBPH algorithm enhances facial recognition robustness. The paper elucidates the architectural framework of the proposed system, delineates the algorithmic workflow, and presents empirical results demonstrating the efficacy of the implemented solution. By integrating these advanced algorithms, the developed attendance system not only automates the attendance tracking process but also provides a secure and efficient mechanism for organizations to manage attendance records in a contemporary and technologically sophisticated manner.

Attendance system, Analysis, LBPH Algorithms, Haar cascade, LBP, HOG, cascade classifier

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[13] Ranganatha S, Y P Gowramma2, “Image Training and LBPH Based Algorithm for Face Tracking in Different Background Video Sequence”, Research Paper Vol.6, Issue.9, pp.43-49, 2018.

Shuchi Sharma, Anirudh Sharma, Priyam Jain, Ishant Popli, Harsh Chahar, "Implementation of an Automated Attendance System with Integrated Face Recognition using Haar Cascade and LBPH Algorithm," International Journal of Computer Sciences and Engineering, Vol.11, Issue.12, pp.38-45, 2023.

The paper involves the development of a rock- paper-scissors game using computer vision techniques, specifically gesture recognition. The game aims to provide an engaging and interactive user experience by allowing players to use hand gestures to play the game against a computer opponent. The user interface was designed for optimal user experience, incorporating visual feedback to effectively engage the player. The accuracy of gesture recognition was evaluated using quantitative metrics, measuring the precision of identifying rock, paper, and scissor gestures. The paper serves as an educational tool to demonstrate the practical applications of computer vision in gaming scenarios, potentially inspiring interest in STEM fields. It sets the foundation for future advancements in computer vision applications, with potential enhancements including multi- player functionality, improved gesture recognition using deep learning models, and integration into augmented or virtual reality environments. While the rock-paper-scissors game using OpenCV and CV Zone has several merits, there are also a few demerits to consider, indicating areas for further improvement. Overall, the paper contributes to the practical understanding of gesture recognition and image processing within the context of an interactive gaming application.

Rock-Paper-Scissors (RPS) Game, Computer Vision, OpenCV, CVZone, Python Programming, Gesture Recognition, Image Processing, Object Detection

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[4]Bradski, G., & Kaehler, “Learning OpenCV”, O`Reilly Media, Inc. Publisher, USA, pp.1-30, 2008.
[5]Python 3.12.1 documentation, 2023.
[6]Li Liu, Wanli Ouyang, Xiaogang Wang, Paul Fieguth, Jie Chen, Xinwang Liu, Matti Pietikäinen, “Deep Learning for Generic Object Detection: A Survey”, International Journal of Computer Vision, Vol.4, Issue.9, pp.1-19, 2019. https://doi.org/10.1007/s11263-019-01247-4
[7]Hasrshdeep Singh Sabharwal, Tushar Kanti Maujumdar, Shashank Saroop, Lokesh Meena, "To Enhance the RPS Game using Open CV & CV Zone by using Python Platform", International Journal of Computer Sciences and Engineering, Vol.11, Issue.12, pp.1-8, 2023. https://doi.org/10.26438/ijcse/v11i12.14

Harshdeep Singh Sabharwal, Tushar Kanti Majumdar, Shashank Saroop, Lokesh Meena, "To Enhance the RPS Game using Open CV & CV Zone by using Python Platform," International Journal of Computer Sciences and Engineering, Vol.11, Issue.12, pp.46-52, 2023.

In today’s digital era, face recognition systems have emerged as a vital component across various sectors. As one of the most commonly used biometric technologies, face recognition offers a multitude of advantages including security, authentication, and identification. Despite its lower accuracy compared to iris recognition and fingerprint recognition, face recognition continues to gain traction due to its contactless and non-invasive nature. Moreover, it can be effectively utilized for attendance marking in educational institutions and workplaces, addressing the shortcomings of traditional manual methods such as consumption and the risk of proxy attendance. This paper proposes a class attendance system based on face recognition, aiming to streamline the attendance process and eliminate its associated challenges. The system encompasses four key phases: database creation, face detection, face recognition, and attendance updation. The database is created by capturing images of students in the classroom. Face detection and recognition are performed using the Haar-Cascade classifier and Local Binary Pattern Histogram algorithm, respectively. Faces are detected and recognized in real time using live streaming videos. At the end of each session, the attendance is automatically sent via email to the respective faculty.

Face Recognition, Face Detection, Haar-Cascade classifier, Local Binary Pattern Histogram, attendance system

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Prachi Goel, Abdul Aleem Ansari, Apurva Jain, Anshul Nagar, "Face Recognition Attendance System," International Journal of Computer Sciences and Engineering, Vol.11, Issue.12, pp.53-55, 2023.

The exponential growth of cloud computing services has led to increased concerns regarding fault tolerance, energy efficiency, and resource optimization. This paper introduces a novel approach combining container-based serverless architecture with artificial intelligence for dynamic resource management and fault prediction. Our system employs deep learning algorithms for workload prediction, reinforcement learning for resource allocation, and ensemble methods for failure detection. To forecast workloads, we utilized historical and real-time data with sequence modeling techniques, achieving accurate demand predictions. Failure detection leveraged ensemble methods, combining diverse predictive algorithms to enhance robustness. Experimental results from a three-month deployment demonstrate significant improvements: an 85% reduction in energy consumption, a 40% decrease in response latency, and a 60% lower operational cost while maintaining 99.99% service availability. These improvements stem from the system`s AI-driven predictive workload management, efficient resource allocation strategies, and robust failure detection mechanisms. These results surpass current industry standards and existing academic solutions by leveraging the synergy between containerization, serverless computing, and AI-driven optimization.

AI-Driven Resource Optimization, Serverless Computing, Fault Prediction, Deep Reinforcement Learning and Ensemble Methods.

[1] Kumari, Priti, and Parmeet Kaur. "A survey of fault tolerance in cloud computing”, Journal of King Saud University-Computer and Information Sciences, Vol.33, Issue.10, pp.1159-1176, 2021.
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Vikas Mongia, "Container-Based Serverless Computing with AI-Driven Resource Optimization for Cloud Fault Tolerance," International Journal of Computer Sciences and Engineering, Vol.11, Issue.12, pp.56-60, 2023.