International Journal of Advanced Research in Science, Communication and Technology最新文献

{"title":"Efficient Prediction of Brain Stroke Using Machine Learning","authors":"Abishek Thillai.S, Dr. H. Jayamangala","doi":"10.48175/ijetir-1205","DOIUrl":"https://doi.org/10.48175/ijetir-1205","url":null,"abstract":"In recent years strokes are one of the leading causes of death by affecting the central nervous system. Among different types of strokes, ischemic and hemorrhagic majorly damages the central nervous system. According to the World Health Organization (WHO), globally 3% of the population are affected by subarachnoid hemorrhage, 10% with intracerebral hemorrhage, and the majority of 87% with ischemic stroke. In this research work, Machine Learning techniques are applied in identifying, classifying, and predicting the stroke from medical information. The existing research is limited in predicting risk factors pertained to various types of strokes. To address this limitation a Stroke Prediction (SPN) algorithm is proposed by using the improvised RNN in analyzing the levels of risks obtained within the strokes. This research of the Stroke Predictor (SPR) model using machine learning techniques improved the prediction accuracy is higher when compared with the existing models. In our work we will be using the following algorithms such as Convolution Neural Network (CNN) as existing and Recurrent Neural Network (RNN) as proposed and its accuracy is been calculated and well compared. From the results obtained it is proved that proposed Recurrent Neural Network (RNN) works better than existing Convolution Neural Network (CNN)..","PeriodicalId":341984,"journal":{"name":"International Journal of Advanced Research in Science, Communication and Technology","volume":" 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141678578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning Approach for Medical Image Analysis","authors":"Rahul J K, Dr. H. Jayamangala","doi":"10.48175/ijetir-1208","DOIUrl":"https://doi.org/10.48175/ijetir-1208","url":null,"abstract":"Colorectal cancer, which is frequent, recognized tumors in both genders around the globe. As per the report generated by WHO in 2018, colon cancer placed in the third position, whereas 1.80 million individuals are affected. Precisely, it is the succeeding leading cancer, which is the second most common cause of cancer in females, and the third for males. The loss of control over the integrity of epidermal cells in bowel or malignancy can be the cause of colorectal cancer. An effective way to recognize colon cancer at an early stage and substantial treatment can reduce the ensuing death rates to a great extent. To perform Screening of Morphology of Malignant Tumor Cells in the colon, a Gastroenterologist may refer to cancer diagnosis tests for pathological images. In any Histology method, the process takes a significant duration of time due to infinite numbers of glands in the gastrointestinal system, which may lead to irreconcilable outcomes. By diagnosing through computer algorithms, can give practical and contributory results. Hence, accurate gland segmentation is one crucial prerequisite stage to get reliable and informative morphological image data. In this work, for colorectal cancer prediction various ML and DL algorithms are employed and compared for accuracy","PeriodicalId":341984,"journal":{"name":"International Journal of Advanced Research in Science, Communication and Technology","volume":" 53","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141680623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Smart System to Detect Brain Age Using Machine Learning","authors":"Ahamed Yasir. H, S. Anu Priya","doi":"10.48175/ijetir-1201","DOIUrl":"https://doi.org/10.48175/ijetir-1201","url":null,"abstract":"Detecting brain ageing is of paramount importance in the medical field due to its significant contribution to the rising number of deaths each year. Brain ageing stands out as a prevalent health concern, characterized by a high mortality rate and widespread occurrence. Extensive research endeavors are underway to address this issue, with Magnetic Resonance Imaging (MRI) emerging as a pivotal tool for identifying and tracking the progression of brain ageing. MRI scans offer detailed insights into the ageing process, facilitating superior outcomes compared to alternative methodologies. In our paper, we propose an innovative approach for detecting brain ageing using MRI scanned images. The methodology encompasses several crucial steps, beginning with image preprocessing, where the application of a median filter enhances image quality. Subsequently, segmentation techniques employing mathematical morphological operations isolate regions indicative of brain ageing. Geometric features such as area, perimeter, and eccentricity are then computed for the identified ageing regions. The culmination of our approach involves the utilization of an Iterative Convolutional Neural Network (CNN) classifier. This classifier distinguishes between ageingous (malignant) and normal (benign) brain regions based on the extracted features. To further enhance the accuracy of our classification, we employ both Artificial Neural Network (ANN) as a baseline method and introduce the Optimistic Convolutional Neural Network (OCNN), a novel algorithm proposed in our research. Through rigorous experimentation and evaluation, we compare the performance of ANN and OCNN, analyzing their respective accuracies. Our findings unequivocally demonstrate that the OCNN outperforms the traditional ANN, offering superior accuracy and efficacy in detecting brain ageing from MRI scans. This underscores the potential of advanced neural network architectures in revolutionizing medical image analysis and diagnosis. In conclusion, paper presents a robust methodology for detecting brain ageing using MRI scanned images, leveraging state-of-the-art image processing techniques and innovative neural network algorithms. By enhancing the accuracy and efficiency of brain ageing detection, our research contributes significantly to the ongoing efforts aimed at mitigating the adverse impacts of this pervasive health issue.","PeriodicalId":341984,"journal":{"name":"International Journal of Advanced Research in Science, Communication and Technology","volume":" 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141680539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Legal Ease","authors":"Dr. Mage Usha U, H.R Sunil Kumar","doi":"10.48175/ijarsct-19118","DOIUrl":"https://doi.org/10.48175/ijarsct-19118","url":null,"abstract":"Legal Ease is groundbreaking Android application designed to streamline the interaction between lawyers and clients by providing an array of essential legal services. The application offers a user- friendly interface and robust features to enhance the efficiency and effectiveness of legal processes. Key services provided by LegalEase include appointment scheduling, case suggestion, and document upload and download, an assortment of comprehensive case tracking capabilities. The appointment scheduling feature allows clients to easily book appointments with their respective lawyers at their convenience. This feature simplifies the procedure for arranging meetings and ensures that both lawyers and clients can manage their schedules effectively. LegalEase's case suggestion functionality leverages advanced algorithms to recommend similar cases to lawyers Smart - Intelligent client's case. This feature helps lawyers in their research and preparation by providing relevant references and insights from similar legal matters","PeriodicalId":341984,"journal":{"name":"International Journal of Advanced Research in Science, Communication and Technology","volume":" 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141677636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic Method to Predict and Classify Cyber Hacking Breaches using Machine Learning","authors":"Vishnu Shankara M A, Dr. H. Jayamangala","doi":"10.48175/ijetir-1211","DOIUrl":"https://doi.org/10.48175/ijetir-1211","url":null,"abstract":"The fast propagation of computer networks has changed the viewpoint of network security. Easy accessibility conditions cause computer networks to be susceptible against several threats from hackers. Threats to networks are numerous and potentially devastating. Up to the moment, researchers have developed Malware Detection Systems (MDS) capable of detecting attacks in several available environments. A boundlessness of methods for misuse detection as well as anomaly detection has been applied. Many of the technologies proposed are complementary to each other, since for different kinds of environments some approaches perform better than others. This project presents a new Malware detection system that is then used to survey and classify them. The taxonomy consists of the detection principle, and second of certain operational aspects of the Malware detection system. In our project we have used algorithms like Random Forest (RF) as existing and Support Vector Machine (SVM) as proposed systems. From the results it is proved that the proposed SVM will work better than existing RF. All are measured in terms of accuracy","PeriodicalId":341984,"journal":{"name":"International Journal of Advanced Research in Science, Communication and Technology","volume":" 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141677515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improved Accuracy in Automatic Detection of Pneumonia from Chest CT Images","authors":"Harish R, S. Anu Priya","doi":"10.48175/ijetir-1202","DOIUrl":"https://doi.org/10.48175/ijetir-1202","url":null,"abstract":"Pneumonia is a common and potentially life-threatening respiratory infection that often requires prompt diagnosis and treatment. Chest computed tomography (CT) imaging is a valuable tool for diagnosing pneumonia, but manual interpretation can be time-consuming and subjective. In recent years, machine learning algorithms have shown promise in automating the detection of pneumonia from chest CT images, aiming to improve diagnostic accuracy and efficiency. \u0000Magnetic Resonance Imaging (MRI): This study presents an improved approach for automatically detecting pneumonia from chest CT images using machine learning techniques. We propose a novel framework that combines advanced image processing methods with state-of-the-art deep learning architectures to enhance the accuracy of pneumonia detection. The proposed framework includes several key components: preprocessing steps for noise reduction and image enhancement, feature extraction methods to capture relevant patterns and textures, and a deep learning model trained on a large dataset of annotated chest CT scans.\u0000To evaluate the performance of our approach, we conducted extensive experiments using a diverse dataset of chest CT images collected from multiple medical centers. Our results demonstrate significant improvements in both sensitivity and specificity compared to existing methods, achieving high accuracy in pneumonia detection. Furthermore, we conducted extensive validation experiments and comparative analyses to validate the robustness and generalization capabilities of our approach across different patient populations and imaging protocols.","PeriodicalId":341984,"journal":{"name":"International Journal of Advanced Research in Science, Communication and Technology","volume":" 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141678239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Study on Child Labour in the Workplace at Sivakasi","authors":"Bala Santhan. K, Elakkiya. S","doi":"10.48175/ijarsct-19112","DOIUrl":"https://doi.org/10.48175/ijarsct-19112","url":null,"abstract":"Child labour is a global concern that violates the rights of children and impedes their overall development. The practice of employing children in hazardous occupations deprives them of their right to education, jeopardizes their health and well-being, and hinders their ability to break the cycle of poverty. Sivakasi, a town located in the Virudhunagar district of Tamil Nadu, India, has been a focal point for studying child labour due to its prominence in the firecracker and matchstick industries. It should bring effective measures to eliminate child labour. the objectives are To know about child labour and their problems, To understand the child labour rights with reference to Indian constitution, To create awareness about prevent of child labour and To analysis the causes of child labour in Sivakasi. For the purpose of this research, an empirical method was followed and the data was collected through both online and offline survey analysis. The SPSS software by IBM was used to calculate the empirical statistics. The sample size was 200. Dependent variables are child labour still exist in Sivakasi, child labour are mostly working at Sivakasi, child working in hazardous factories is violation of Indian constitution, child labour is prevalent at Sivakasi, government taking several measures for child labour in India. Independent variables are Age, Gender. Various tools like Bar graph were used","PeriodicalId":341984,"journal":{"name":"International Journal of Advanced Research in Science, Communication and Technology","volume":" 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141677516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning Based Mammogram Classification from Mnist","authors":"Romario Dicruz, Dr. H. Jayamangala","doi":"10.48175/ijetir-1206","DOIUrl":"https://doi.org/10.48175/ijetir-1206","url":null,"abstract":"Breast cancer is one of the most leading causes of death among women. The early detection of abnormalities in breast enables the radiologist in diagnosing the breast cancer easily. Efficient tools in diagnosing the cancerous breast will help the medical experts in accurate diagnosis and timely treatment to the patients. In this work, experiments were carried out using Wisconsin Diagnosis Breast Cancer database to classify the breast cancer as either benign or malignant. Supervised learning algorithm -Support Vector Machine (SVM) with kernels like Linear, and Neural Network (NN) are used for comparison to achieve this tasks. The performances of the models are analysed where Neural Network approach provides more ‘accuracy’ and ‘precision’ as compared to Support Vector Machine in the classification of breast cancer, ANN seems to be fast and efficient method. In our project we have used the following algorithms Support Vector Machine (SVM) as existing and Artificial Neural Network (ANN) as proposed system compared in terms of accuracy","PeriodicalId":341984,"journal":{"name":"International Journal of Advanced Research in Science, Communication and Technology","volume":" 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141678485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diabetes Prediction using Machine Learning Algorithms","authors":"J. D. Jeevaraja, P. Kavitha, S. Kamalakkannan","doi":"10.48175/ijetir-1213","DOIUrl":"https://doi.org/10.48175/ijetir-1213","url":null,"abstract":"Diabetic retinopathy (DR) is a disease that damages retinal blood vessels and leads to blindness. Usually, colored fundus shots are used to diagnose this irreversible disease. The manual analysis (by clinicians) of the mentioned images is monotonous and error-prone. Hence, various computer vision hands-on engineering techniques are applied to predict the occurrences of the DR and its stages automatically. However, these methods are computationally expensive and lack to extract highly nonlinear features and, hence, fail to classify DR’s different stages effectively. This project focuses on classifying the DR’s different stages with the lowest possible learnable parameters to speed up the training and model convergence. The VGG-16, spatial pyramid pooling layer (SPP) is stacked to make a highly nonlinear scale-invariant deep model called the VGG-16 model. The proposed VGG-16 model can process a DR image at any scale due to the SPP layer’s virtue. Moreover, the stacking adds extra nonlinearity to the model and tends to better classification. The experimental results show that the proposed model performs better","PeriodicalId":341984,"journal":{"name":"International Journal of Advanced Research in Science, Communication and Technology","volume":" 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141680051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DNA as a Storage Medium for Efficient and Reliable Cloud Data Archieving","authors":"Sriram.S, Dr. D. R. Krithika","doi":"10.48175/ijetir-1218","DOIUrl":"https://doi.org/10.48175/ijetir-1218","url":null,"abstract":"On Earth right now, there are about 10 trillion gigabytes of digital data, and every day, humans produce emails, photos, tweets, and other digital files that add up to another 2.5 million gigabytes of data.Much of this data is stored in enormous facilities known as exabyte data centers (an exabyte is 1 billion gigabytes), which can be the size of several football fields and cost around $1 billion to build and maintain.Demand for data storage is growing exponentially, but the capacity of existing storage media is not keeping up.This project enables molecular-level data storage into DNA molecules by leveraging biotechnology advances in synthesizing, manipulating and sequencing DNA to develop archival storage. Additionally an effective algorithm is introduced using deoxyribonucleic acid (DNA)-based cryptography to enhance data security while sharing the data over the cloud","PeriodicalId":341984,"journal":{"name":"International Journal of Advanced Research in Science, Communication and Technology","volume":" 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141679988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}