Neural Network Research Articles

Summarization and Translation Using NLP

Abstract: Text summarization and translation are two critical tasks in natural language processing with significant applications in various domains such as news aggregation, document summarization, machine translation, and information retrieval. In recent years, there has been remarkable progress in the development of techniques and models for both tasks, leveraging advancements in deep learning and neural network architectures. This paper presents a comprehensive review and comparative analysis of state-of-the-art methods in text summarization and translation. First, we provide an overview of the different approaches to text summarization, including extractive, abstractive, and hybrid methods, highlighting their strengths and weaknesses. We discuss various evaluation metrics and datasets commonly used for benchmarking summarization systems, shedding light on the challenges and opportunities in this field.

Deep Learning based Credit Card Fraudulency Detection System

Abstract: Huge increase in the internet usage has been observed since last decade. It led to the emergence of services like ecommerce, tap and pay systems, online bill payment systems, etc. have proliferated and become more widely used. Due to various online payment options introduced by e- commerce and other numerous websites, the possibility of online fraud has risen drastically. Thus, due to an increase in fraud rates, research on analyzing and detecting fraud in online transactions has begun utilizing various machine learning techniques. The Deep Learning techniques viz., Convolutional Neural Network Architecture is used to detect credit card frauds in the proposed model. The Principal Component Analysis transformation gives a set of numerical input variables as output which are taken as the features to be considered. Due to confidentiality concerns, some of the original characteristics and background information about the data are not entirely disclosed. Features of credit card frauds must be chosen carefully as they play important role when deep learning techniques are used for credit card fraud detection. The TensorFlow and Keras are used for the development of the current proposed model. The proposed model aims to predict credit card frauds with 91.9% accuracy

Leaf Disease Detection Using Convolutional Neural Network

Leaf Disease Detection Using Convolutional Neural Network

Sign Language Recognition for Real-time Communication

Abstract: Sign language is an essential communication tool for India's Deaf and Hard of Hearing people. This study introduces a novel approach for recognising and synthesising Indian Sign Language (ISL) using Long Short-Term Memory (LSTM) networks. LSTM, a kind of recurrent neural network (RNN), has demonstrated promising performance in sequential data processing. In this study, we leverage LSTM to develop a robust ISL recognition system, which can accurately interpret sign gestures in real-time. Additionally, we employ LSTM-based models for ISL synthesis, enabling the conversion of spoken language into sign language for improved inclusivity and accessibility. We evaluate the proposed approach on a diverse dataset of ISL signs, achieving high recognition accuracy and natural sign synthesis. The integration of LSTM in ISL technology holds significant potential for breaking down communication barriers and improving the quality of life for India's deaf and hard of hearing people

An Effective Classification of Brain Tumor using Deep Learning Techniques

Abstract: Brain tumor classification is a critical task in medical imaging, aiding in timely diagnosis and treatment planning. In this paper, we propose a comprehensive approach utilizing deep learning models for the classification of brain tumors into four categories: glioma, meningioma, pituitary tumors, and no tumors. We employ state-of-the-art convolutional neural network (CNN) architectures including ResNet50, DenseNet201, EfficientNetB3, and InceptionV3 for the classification task. To enhance the performance of the model, we employ resizing and augmentation techniques such as flips and rotation, thereby increasing the diversity of the training dataset. This is particularly crucial due to the limitations posed by small-sized datasets in previous methodologies. Our findings underscore the efficacy of deep learning approaches in brain tumor classification, with EfficientNetB3 emerging as a promising model for accurate diagnosis. Furthermore, our utilization of resizing and augmentation techniques demonstrates their significance in mitigating the challenges associated with limited training data.

Brain Tumor Segmentation Using U-Net Model

Abstract: Cancer of the brain presents a significant challenge due to its lethality, necessitating precise surgical segmentation. This study explores the application of U-Net, a Convolutional Neural Network (CNN) architecture, to segment brain tumors. Specifically, the segmentation involves distinguishing necrotic, edematous, growing, and healthy tissues within the brain. Analyzing overlaps between these diverse tissue types can be intricate, making the extraction of relevant information from images challenging. The study enhances the 2D U-Net network, leveraging the BraTS datasets for training. U-Net's flexibility in setting up various encoder and decoder routes proves invaluable for extracting pertinent information from images, adaptable for diverse applications

Language Translator Using Deep Learning

Abstract: Digital translation (MT) is a process of translating text from one language to another using software by including both computer and language information. Initially, the MT system acquires text translation into the source language by simply associating the meaning of the words in the source language with the target language with the help of grammar. However, such methods did not produce good results due to their failure to capture the various sentence structures in the language. This process of translation is time-consuming and requires skilled craftsmen in both languages. Subsequently, integrated translation methods such as mathematical translation (SMT) and neural translation (NMT) technology have been introduced to address the challenges of legal-based approaches. MT has already shown promising results in bilingual translation. In contrast to SMT, which requires sub-components trained separately in translation, NMT uses one large neural network for training. This paper outlines how to train a repetitive neural network for rearrangement for a source to identify. The default encoder helps to reconstruct the vectors of the target language. Therefore, powerful hardware (GPU) support is required. The GPU improves system performance by reducing training time.

Traffic Sign Recognition using HIICNN Stack Ensemble Method

Abstract: This project introduces a novel deep convolutional neural network (CNN) model aimed at enhancing traffic sign recognition for autonomous vehicle technology and road safety. Employing a stacking ensemble approach, we combine multiple CNNs with diverse improvement techniques, including preprocessing for adverse weather conditions and shooting errors, and data augmentation to handle class imbalance. Through adjustments in learning rates during model training, we mitigate overfitting. Our ensemble model achieves an exceptional 99.4 percent test accuracy on the German Traffic Sign Recognition Benchmark (GTSRB) dataset, surpassing prior studies. We utilize gradient-weighted Class Activation Mapping (Grad-CAM) for model explain- ability and an evidential deep learning approach to quantify classification uncertainty. Our framework underscores the efficacy of combining preprocessing, ensemble learning, and transfer learning for superior perfor- mance and reliability in traffic sign recognition systems, contributing significantly to autonomous driving and road safety.

AI Assisted Deceptive Content Analysis System Using Bi-LSTM

Abstract: In an age of widespread misinformation, creating accurate machine learning algorithms is vital for ensuring the integrity of information sharing. This project aims to develop a machine learning model capable of distinguishing between fake and true news articles. [1]The model utilizes a Bidirectional Long Short-Term Memory (Bi-LSTM) neural network architecture. Exploratory data analysis techniques are employed to gain in-sights into the characteristics and distributions within the dataset. Data visualization techniques aid in understanding patterns and relationships within the dataset. Additionally, unigram analysis is conducted to extract meaningful features from the text data. The datasets are then prepared for model training, involving preprocessing steps such as tokenization and vectorization. Fi-nally, the Bi-LSTM model is constructed, leveraging its ability to capture long-range dependencies in sequential data. The model is trained on the prepared datasets, optimized using appropri-ate techniques, and evaluated using metrics such as accuracy, precision, recall, and F1-score. The Bi-LSTM architecture offers the advantage of capturing long-range dependencies in sequential data, thereby enhancing the model’s ability to discern nuanced patterns in news articles. The primary objective of this project is to develop a robust and accurate system for automatically detecting fake news, thus playing a pivotal role in enhancing the dissemination of reliable information in the digital age.

Deep Learning Approach for Early Detection of Alzheimers Disease

Abstract: Alzheimer’s disease (AD) is a chronic, irreversible brain disorder, no effective cure for it till now. However, available medicines can delay its progress. Therefore, the early detection of AD plays a crucial role in preventing and controlling its progression. The main objective is to design an end-to-end framework for early detection of Alzheimer’s disease and medical image classification for various AD stages. A deep learning approach, specifically convolutional neural networks (CNN), is used in this work. Four stages of the AD spectrum are multi-classified. Furthermore, separate binary medical image classifications are implemented between each two-pair class of AD stages.

Crop Disease Prediction Using Web Application

Abstract: Agriculture plays a very vital role in our life. Without agriculture, the existence of human beings is not possible as it is the main source of our food supply to sustain on the earth and it also helps to grow our economy across the world. Plant disease detection is one of the most important aspects of maintaining an agriculturally developed nation. The timely and efficient detection of plant diseases is essential for a healthy and productive agricultural sector. Various diseases like Common Rust, Bacterial Spot, Leaf Mold, Mosaic Virus, Powdery Mildew and others that could affect a plant and cause farmer to lose a substantial sum yearly. Deep Learning (DL) can play a crucial role in helping farmers to prevent crop failure by early disease detection in plant leaves. In the experiment, examination conducted by Convolution Neural Network (CNN) model on dataset (which consists of images of healthy and infected leaves) to detect plant diseases and Web Application is used for real-life crop disease prediction. The proposed Web Application aims to assist farmers in detection of plant diseases by analyzing images of the plant leaves. The proposed application uses the CNN model to distinguish healthy and infected leaves. The goal is to help farmers and prevent economic loss by detecting plant diseases early

Intelligent Document Processing for Disease Detection

Abstract: This project endeavors to develop an intelligent document processing pipeline tailored specifically for medical reports, with a primary focus on samples from Dr Lal Path lab and targeting prevalent diseases leading to kidney failures such as glomerulonephritis, chronic kidney disease, polycystic kidney disease, hypertensive nephropathy, and lupus nephritis. The proposed pipeline integrates cutting-edge technologies including the YOLOv8 object detection model for precise cropping of tabular data, Paddle OCR for accurate extraction of information from tabular images, and Fuzzy Wuzzy NLP library for filtering pertinent data from the extracted information necessary for the subsequent machine learning model. The goal is to employ a neural network model to predict potential kidney failure based on the processed medical data. This holistic approach amalgamates advanced computer vision, natural language processing, and machine learning techniques to streamline the analysis of medical reports, potentially enhancing diagnostic accuracy and clinical decision-making in nephrology.

Human Action Recognition System Using LRCN

Abstract: The importance of human action recognition is significant across various domains, driving advancements in safety, healthcare, sports analytics, and interactive technologies. Leveraging machine learning techniques like Long Recurrent Convolutional Neural Networks (LRCN) trained on datasets such as UCF50, our project focuses on action prediction from YouTube videos. This technology plays a pivotal role in enhancingsafety and security by enabling the detection of anomalies and suspicious behaviours in surveillance systems. In healthcare, it supports remote patient monitoring, rehabilitation assessment, and personalized care plans based on observed actions. Addi- tionally, in sports analytics and entertainment, human action recognition informs performance evaluation, content creation, and immersive experiences. Our integration of a pre-trained LRCN model into a Flask web application signifies the tangible impact of machine learning on human action recognition, with ongoing efforts to optimize functionality through input validation, error handling, user feedback, security measures, and performance enhancements, illustrating the practical application and societal benefits of this innovative technology

Global Weight Optimization on Artificial Neural Network for Optimizing Crop Yield Prediction Using Genetic Algorithm

Abstract: Accurate crop yield prediction is a complex and intricate task that encompasses a multitude of factors and variables, rendering it arduous to establish a dependable mathematical model. Conventional machine learning (ML) models for crop yield prediction have surpassed their efficacy. This research endeavour sought to enhance crop yield prediction accuracy by hybridizing a neuro-genetic model utilizing statistical data amassed over a 35-year period from various pertinent agricultural departments in Tamil Nadu, India, including the Statistical, Agricultural, and Meteorological Departments. This research delved into analyzing and identifying the optimal weight configuration for the artificial neural network (ANN) to bolster accuracy with the assistance of genetic algorithms (GA). Seventy-five percent of the data was employed to train the model, while the remaining 25% was utilized for model testing. To gauge the performance of this research work, 5-fold cross-validation was implemented. RMSE, MAE, and Adj R2 were employed to evaluate the performance and contrast the performance of the neuro-genetic model with the conventional ANN. The neuro-genetic model exhibited superior accuracy compared to the conventional ANN.

URL and Malicious Link Prediction

Abstract: In response to the pressing cybersecurity challenges posed by the proliferation of phishing URLs and malicious links, this research introduces a ground breaking approach centered on transfer learning within deep neural networks. By leveraging transfer learning, intricate patterns within URLs and their content are unveiled, culminating in the development of a model seamlessly integrating Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Unit (BiGRU) networks. These architectures effectively capture sequential dependencies, enhanced by their bidirectional variants accessing both past and future states to comprehend temporal dynamics and improve performance. Through meticulous evaluation and fine-tuning processes, the proposed cybersecurity solution demonstrates robustness and efficacy in defending against evolving threats. This research contributes significantly to advancing the cybersecurity domain, introducing an adaptive strategy that harnesses the strengths of BiLSTM and BiGRU networks within the framework of transfer learning, thus paving the way for more resilient and effective cybersecurity solutions.

Automating Helmet Usage Detection: A YOLOv8 Based Framework

Abstract: The biggest threat to Powered two-wheeler(PTW) riders is a head injury. With the humongous amount of daily commuters on such PTWs, the risk only increases substantially. In such cases, helmets reduce the risk of head injuries in accidents but not every citizen is keen on their use. In India, where the use of motorcycles is widespread, ensuring helmetuse remains a challenge. This study explores the effectiveness of YOLOv8(You Only Look Once) a state-of-the-art single-stage object detection algorithm for helmet detection on Indian roads [14]. The CNN(Convolutional Neural Networks) based technologyimplements many other efficient, reliable and quick algorithmsto train, validate and predict the object’s detection, segmentationand classification task. We leverage the use of a publicly available Indian helmet dataset from Kaggle, containing 942 images with annotations. This an image dataset of real-life powered two-wheeler riders. The dataset has images and video frames for training on 5 different objects/classes along with annotations that specify thoseobjects. This multi-class approach offers valuable insight intohelmet usage patterns paves shows us the way for real-world applications for automated traffic monitoring. Real-time helmet and number plate detection can become a game changer in trafficmonitoring and can strengthen basic safety laws

Svara Rachana - Audio Driven Facial Expression Synthesis

Abstract: Svara Rachana is a fusion of artificial intelligence and facial animation which aims to revolutionize the field of digital communication. Harnessing the ever-evolving power of neural networks in the form of Long Short-Term Memory (LSTM) model, Svara Rachana offers a cutting edge, interactive web application designed to synchronize human speech with realistic 3D facial animation. Users can upload or record an audio file and upload it to the web interface containing human speech, with the core functionality being the generation of synchronized lip movements on a 3D avatar. The system gives special emphasis on the accuracy of the system to generate reliable facial animation movements. By providing an interactive, human like 3D model, Svara Rachana aims to make machine to human interaction a more impactful experience by blurring the lines between humans and machines.

Pioneering Dermatological Disease Detection with CNNs

Abstract: Skin cancer poses a significant public health concern globally, with its incidence steadily rising. Early and accurate detection plays a pivotal role in improving patient outcomes and survival rates. With leveraging advancements in deep learning, this project explores the efficacy of Convolutional Neural Networks (CNNs) in automating the classification and detection of skin cancer from dermatoscopic images. The methodology employed in this project involves the development and optimization of a CNN architecture tailored for skin cancer analysis. The proposed model harnesses the hierarchical feature learning capabilities of CNN to automatically extract intricate patterns and discriminative features from dermatoscopic images, enabling accurate differentiation among various skin lesion types, including melanoma, basal cell carcinoma, and benign nevi. In addition to classification, the developed CNN architecture integrates a lesion detection mechanism. This auxiliary functionality enables the identification and delineation of potentially cancerous regions within skin lesions. The proposed model’s detection capabilities are evaluated through bounding box prediction and segmentation masks, exhibit notable performance in localizing suspicious areas, aiding dermatologists in targeted examinations. With the help of this project, we can prevent the cause of damage to skin at an early stage.

Sleeping Stage Classification Using CNN

Abstract: Maintaining proper health and mental stability is critical for overall health and well-being. Despite a good deal of research investment, sleep quality continues to be a crucial public challenge. Nowadays, people of all age groups are affected by improper sleep quality. Poor sleep can lead to a variety of neurological disorders. Sleep disorders are common in all subsets of the population, independently of gender. This public health challenge greatly affects quality of life in terms of both physical and mental health. Insomnia, parasomnias, sleep- related breathing difficulties, hypersomnia, bruxism, narcolepsy, and circadian rhythm disorders are some common examples of sleep-related disorders. Some of these disorders can be treated with proper analysis of early symptoms; in such cases, adequate sleep quality is essential for the patient’s recovery. Artificial intelligence has several applications in sleep medicine including sleep and respiratory event scoring in the sleep laboratory, diagnosing and managing sleepdisorders, and population health. While still in its nascent stage, there are several challenges which preclude AI’s generalizability and wide-reaching clinical applications. Artificial intelligence is a powerful tool in healthcare that may improve patient care, enhance diagnostic abilities, and augment the management of sleep disorders. However, there is a need to regulate and standardize existing machine learning algorithms and deep learning algorithm prior to its inclusion in the sleep clinic. In this project, we can develop the framework for sleeping stage classification for both subjective data and ECG data and classify the data using Convolutional neural network algorithm to analyse the multiple sleeping stages.

Moving Vehicle Number Plate Detection

Abstract: The ability to detect and recognize vehicle license plates is a crucial task in various applications such as traffic management, law enforcement, and parking systems. In this project, we propose a system for moving vehicle license plate detection using image and video processing techniques. The system employs a combination of computer vision algorithms and machine learning models to accurately locate and recognize license plates from moving vehicles in real-time. The input images or video frames are preprocesses to enhance the regions containing license plates and reduce noise. This preprocessing step may include operations such as resizing, grayscale conversion, noise reduction, and contrast enhancement. Next, object detection algorithms such as Haar cascades or deep learning-based techniques like YOLO (You Only Look Once) are utilized to detect candidate regions in the image/frame that potentially contain license plates. After candidate regions are identified, a series of image processing techniques such as morphological operations, contour detection, and character segmentation are employed to isolate and extract the license plate characters. Machine learning models like Support Vector Machines (SVM), Convolutional Neural Networks (CNNs) are then applied to recognize the characters on the license plate.