International Journal of Interpreting Enigma Engineers (IJIEE)

Bidirectional Lstm Based Hybrid Deep Learning Frame Works For Cardiac Arrhythmia Classification

Pinjala N Malleswari — 2024-12-18

In this paper, implemented a hybrid approach to ECG classification by combining a Deep Neural Network andBi-directional Long Short Term Memory (Bi-LSTM) layer. We substantially improved denoising by preprocessing ECG signals with a mix of Empirical Mode Decomposition and Discrete Wavelet Transform, resulting in higher signal quality for classification. The suggested method successfully classified ECG signals into five unique classes, with 95.3% accuracy, 96.9% sensitivity, and 98.7% specificity, respectively, using the MIT-BIH database as an evaluation benchmark. Our results show that the proposed hybrid technique outperforms existing classifiers, highlighting its potential for real-world clinical applications at cardiac arrhythmia diagnosis. Future research can concentrate on enhancing the model for real-time processing and expanding its application to different physiological datasets in order to improve its adaptability and generalizability in cardiovascular disease diagnoses.

Secure Relay Chat System based on Gossip Protocol using Flutter

Saba Fatima — 2024-12-18

The peer-to-peer (P2P) networks are made up of networked devices that share resources without the need for a centralized server. The drawbacks of centralized messaging systems include their dependence on the internet, privacy issues, and censorship. Utilizing Bluetooth and Wi-Fi Direct, the system identifies nearby devices and relays messages across multiple hops in a network, employing an efficient hybrid gossip protocol (First Push, Then Pull) for optimal message propagation. The system is built with Flutter/Dart for cross-platform compatibility and features a user-friendly interface in decentralized network, including real-time updates and device management. It emphasizes scalability, fault tolerance, and privacy, making it a practical solution for communication in challenging scenarios. The implementation uses a hybrid gossip protocol, which combines the advantages of push and pull strategies, to ensure optimal message transmission. RSA (Rivest Shamir Adleman) encryption is used for end-to-end security, and offline storage ensures delivery even when recipients are temporarily unavailable. The innovative solution offers a secure and efficient alternative to conventional messaging platforms, which makes it particularly helpful in remote locations.

Helmet and Number Plate Detection Using YOLOV5 and YOLOV8

Tanusha Gorak — 2024-12-18

The purpose of this project is to create a reliable and effective system for detecting vehicle number plates using two well-known versions of the object detection algorithm YOLO (You Only Look Once), specifically YOLOv5 andYOLOv8. This system aims to automatically identify and extract number plates from images of vehicles, which is essential for applications such as automated tolling, traffic monitoring, and law enforcement. The project involves training both YOLOv5 and YOLOv8 models on a specially created dataset of labelled vehicle images, and then evaluating and comparing the models based on detection accuracy, inference speed, and their robustness in various environmental scenarios. Additionally, the system's performance is improved by incorporating Optical Character Recognition (OCR) to retrieve text from the identified number plates. The results of this comparison indicate that YOLOv8, thanks to its enhanced architecture, offers greater accuracy and faster inference rates compared to YOLOv5, making it a better choice for real-time applications. This work emphasizes the efficacy of deep learning methods in addressing number plate recognition challenges, providing a scalable and practical solution for intelligent transportation systems.

Machine Learning in Melanoma Detection Analyzing The Role of Feature Engineering in Skin Lesion Classification

Bandari Manisha — 2024-12-18

Skin cancer is one of the most common types of cancer in people. Doctors usually find it through visual inspections, starting with a clinical screening, then followed by a biopsy and lab tests. We can improve the prediction of skin cancer and determine if it is melanoma or benign by using automated systems that categorize images of skin lesions. This is made possible by machine learning and artificial intelligence (AI).) approaches. This chapter describes Spotting skin cancer early can make a big difference in treatment and recovery! using Spark and a deep neural network. To find the best algorithm for skin cancer prediction, a comparison study of the several algorithms currently in use has also been conducted. Based on findings gathered from several iterations, skin lesion photographs might be categorized using a CNN approach. Then, many transfer learning models were employed for fine-tuning, This study focuses on several advanced image recognition models, specifically Resnet50, InceptionV3, and Inception Resnet, to improve how we analyze skin lesions. One of the key contributions of this research is the use of a technique called ESRGAN to enhance images before they are processed by these models. By applying this preprocessing step, we aimed to boost the accuracy of the results.We tested multiple models, including our customized model and a standard CNN, to see how well they performed. Interestingly, both the standard pre-trained model and our own produced similar results, indicating that our approach is effective. The effectiveness of our method was shown through simulations using the ISIC 2020 skin lesion dataset, a well-known collection of images used for this kind of research. We found that our CNN model achieved an impressive accuracy of 89.2%, showcasing the potential of our approach in helping to improve skin lesion analysis.

Malaria Diagnosis by Transfer Learning Analysis of Parasitized and Uninfected Red Blood Cell Images Using VGG16, DenseNet201, VGG21, and VGG19

Radhika Talla — 2024-12-18

Malaria is still a major worldwide health concern, and effective treatment depends on early discovery. Using well-known deep learning models like VGG16, DenseNet201, VGG21, and VGG19, this study focuses on applying transfer learning techniques to diagnose malaria by examining photos of red blood cells. To identify malaria-infected cells, the dataset's red blood cell images—both parasitized and uninfected—are processed. With the intention of offering a more dependable and approachable malaria diagnostic technique, the objective is to assess the effectiveness of these predictive models in terms of accuracy, precision, and computational performance. This strategy ensures improved performance. Different methods were tried, and all classifiers' outputs were combined using an ensemble technique. The suggested technique outperformed the individual machine learning classifiers and ensemble methods employed in this study in terms of accuracy, precision, and sensitivity, with rates of 96.94%, 96.94%, and 96.94%. Furthermore, the association between the factors was investigated, showing each factor's contribution to malaria occurrence. The findings show that the suggested approach may efficiently anticipate malaria outbreaks.