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Classification and analysis of Alzheimer's Disease using Deep Learning methods on MRI and PET
Alzheimer's disease (AD) falls in the category of neurodegenerative illness in which an individual loses his or her power to remember things and behaviors. It affects memory in younger patients and as it progresses causes diffuse cortical functions. However, a major issue with the diagnosis and treatment of AD symptoms is that it has complex pathogenesis because of which there is no clinical intervention for its treatment. There is no disease-modifying treatment to cure AD symptoms that increases co-morbidities among the patients. The present research identified this gap and focuses on using Deep Learning methods on MRI and PET data so that there is early diagnosis of AD by healthcare experts and they could propose a better treatment process for reducing AD symptoms. The present research identified that by using deep learning-based approaches particularly ResNet50 architecture, there is the execution of quantitative assessment of brain MRI and PET to acquire insights about the internal abnormalities through self-learning features. It will help in initiating proper treatment and avoiding damage to the brain further. 2022 IEEE. -
Classification Framework for Fraud Detection Using Hidden Markov Model
Machine learning is described as a computer program that learns from experience E with regard to some task T and some performance measure P, if its performance on T improves with E as measured by P. Suppose we have a credit card fraud detection which watches which transactions we mark as fraud or not, and on the basis, it knows how to filter better fraudulent transactions then, E is watching your transactions is fraud or not, T is classifying your transactions as fraud or not, P is number of transactions correctly differentiated as spam or not spam. Machine learning has two types: supervised learning and unsupervised learning. Supervised learning is the type of machine learning where machine is provided with input mapped with its output, and these inputs and outputs are used to make a machine learn a particular function from the trained dataset. There are two branches of supervised learning, i.e., classification and regression. In unsupervised learning, we do not supervise model instead we allow machine to work on its own to discover information. Clustering is type of unsupervised learning. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Classification of Breast Invasive Ductal Carcinomas Using Histopathological Images Based on Deep Learning Techniques
Women suffer from cancer, which is the main reason for death for females around the world. With the use of artificial intelligence, it is possible to predict and detect all types of cancers in the near future. It is not just women who can heal, and most breast cancers are caused by the most vulnerable type of breast. Eighty percent of all diagnoses of carcinoma are invasive ductal carcinomas (IDCs). In this paper, deep learning techniques are extended to support visible semantic evaluation of tumor areas, using convolutional neural networks (CNNs).A CNN is skilled ended a large number of photo covers (tissue areas) after Whole Slide Images (WSI) to study ranked part-based total image. About 600 normal image patches and 200 breast invasive ductal carcinomas are selected for the experiment. It was intended to amount classifier correctness in the detection of IDC tissue areas in Whole Slide Images. We achieved excellent measurable outcomes for an automated finding of IDC areas with our technique. The results are evaluated based on performance measures and compared with a different number of neurons, and the results are highlighted. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Classification of Disaster Tweets using Machine Learning and Deep Learning Techniques
Social networks provide a plethora of information for gathering extra data on people's behavior, trends, opinions, and feelings during human-affecting occurrences, such as natural catastrophes. Twitter is an inevitable communication medium during calamities. People mainly depend on Twitter to announce real-time emergencies. However, it is rarely straightforward if someone is declaring a tragedy. Sentiment analysis of disaster tweets aid in situational awareness and realizing the disaster dynamics. In our paper, we perform a sentimental analysis of disaster tweets using techniques based on machine learning and deep learning. The tweets are pre-processed before being converted into a structured form using Natural Language Processing (NLP) methods. Supervised learning techniques such as the Support Vector Machine and the Naive Bayes Classifier algorithm are used to develop the Classifier, which categorizes tweets into distinct catastrophes and selects the most appropriate algorithm. The chosen algorithm is further enriched with an emoticon detection algorithm for explicit elucidation. Our research would help disaster relief organizations and news agencies to conclude about the state of affairs and do the needful. 2022 IEEE. -
Classification of Diseased Leaves in Plants Using Convolutional Neural Networks
The article focuses on the classification of diseased leaves using a machine learning algorithm. The main focus in agriculture is controlling pests and weeds, for which farmers spray chemical pesticides to get a good yield. The issue here is over-usage and under-usage of pesticides, which might harm the end consumer. To achieve the goal of reducing pesticide use and detecting pests in the crop early, the machine learning algorithm is deployed on the leaf image. The image data of the leaf of the cauliflower plant is collected for 40days. The data was collected from the day the plant was seeded in a pot until the day it was ready to be planted in the soil. From this data, the pest attack on the plants is tracked without the application of pesticides. To achieve this, the CNN algorithm is used on the collected image data. The outcome of the study would be to classify the diseased leaves based on the pest attack and know the right time to spray the pesticides to reduce the damage to the plant. This also reduces the use of pesticides and costs to the farmer. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Classification of fibroid using novel fully connected CNN with back propagation classifier (NFCCNNBP)
In this phase, we utilize features extracted from a prior stage to classify uterine fibroids. We employ a predefined dataset with feature values as our training set for a novel classifier called the "Novel Fully Connected CNN with Back Propagation Classifier."This classifier learns from the training set. We then put this method to the test with new images not included in the training dataset. Its primary objective is to assess the extent of infection across the entire uterine surface. Through the adoption of a Convolutional Neural Network (CNN) combined with Back Propagation (BP), we have achieved an impressive accuracy rate of 98.3% for predictions. When we compare this accuracy to existing classifiers like Fuzzy Logic, Naive Bayes, and SVM, our proposed model, NFCCNNBP, outperforms them significantly. 2024 Author(s). -
Classification of Hypothyroid Disorder using Optimized SVM Method
Hypothyroidism is an endocrine disorder where the thyroid organ doesn't provide the enough amount of thyroid hormones. It is one of the common diseases found in women. Detection of hypothyroidism needs suitable diagnostic tests to encourage prompt analysis and medication. Accurate and early detection of a disease is more important and compulsory in healthcare domain to facilitate correct and prompt diagnosis and timely treatment. The information generated in healthcare domain is on large scale, crucial and complex with multiple parameters. To interpret and understand such a huge data and retrieve the accurate and relevant information from it is a tedious as well as challenging task. However, there is a need and importance to facilitate the patients with better medical solutions. This will help to reduce the cost, time and give more relief to users by applying advanced and upgraded knowledge. It will also assist to prevent the further complications. The proposed study gains the knowledge from the hypothyroid dataset to predict the level of disease. To identify the level of hypothyroid disorder, we used four classification machine learning techniques, namely KNN (K-Nearest Neighbour), SVM (Support Vector Machines), LR (Logistic Regression) and NN (Artificial Neural Network). The Experimental results compared the classification accuracy of four methods. Logistic Regression method achieved 96.08% accuracy among other three classifiers. But, SVM is found the best classifier after standardizing the data and parameter tuning with accuracy of 99.08%. 2019 IEEE. -
Classification of Skin Diseases Using Convolutional Neural Networks (VGG) with Histogram Equalization Preprocessing
Skin diseases are a major global health concern for which prompt and precise diagnosis is necessary for effective treatment. Convolutional neural networks (CNN), one of the deep learning techniques, have shown potential in automating the diagnostic procedure. The goal of this research is to enhance the effectiveness of skin disease categorization by fusing the capabilities of CNNs - particularly the VGG architecture - with the histogram equalization preprocessing method. In image processing, histogram equalization is a commonly used approach to enhance the contrast and general quality of medical photographs, which include photos of skin conditions. In order to improve the characteristics and details of dermatological pictures for this study, we employed histogram equalization as a preprocessing step. This allowed CNN to extract pertinent features more quickly. 2024 IEEE. -
Classification of Soil Images using Convolution Neural Networks
Classification of soil is crucial for the agricultural domain as it is an essential task in geology and engineering domains. Various procedures are proposed to classify soil types in the literature, but many of them consumed much time or required specially designed equipments/applications. Classification of soil involves the accounting of various factors due to its diversified nature. It can be observed that several critical domain-oriented decisions often depend on the type of soil like farmers might be benefitted from knowing the kind of soil to choose crops accordingly for cultivation. We have employed different Convolution Neural Network (CNN) architectures to identify the soil type accurately in real-time. This paper describes the comparative evaluation in terms of performances of various CNN architectures, namely, ResNet50, VGG19, MobileNetV2, VGG16, NASNetMobile, and InceptionV3. These CNN models are used to classify four types of soils: Clay, Black, Alluvial, and Red. The performance of the ResNet50 model is the best with a training accuracy and training loss of 99.47% and 0.0252, respectively compared to other competing models considered in this paper. 2021 IEEE. -
Classification of Vehicle Make Based on Geometric Features and Appearance-Based Attributes Under Complex Background
Vehicle detection and recognition is an important task in the area of advanced infrastructure and movement administration. Many researchers are working on this area with different approaches to solve the problem since it has a many challenge. Every vehicle has its on own unique features for recognition. This paper focus on identifying the vehicle brand based on its geometrical features and diverse appearance-based attributes like colour, occlusion, shadow and illumination. These attributes will make the problem very challenging. In the proposed work, system will be trained with different samples of vehicles belongs to the different make. Classify those samples into different classes of models belongs to same make using Neural Network Classifier. Exploratory outcomes display promising possibilities efficiently. 2019, Springer Nature Singapore Pte Ltd. -
Classification of Vehicle Type on Indian Road Scene Based on Deep Learning
In Recent days an intelligent traffic system [ITS] is implemented on indian traffic sytem. Different applications are widely used to improvies the performance of the system. To improve the intelligence of the system deep learning can used to classify the vehicles into three different classes. The combination of Faster RCNN classifier and RPN can used to detect the objects and classify those objects into different classes. Analysis of the experimental results shows the improved accuracy and efficiency in classifying the vehicles on indian roads into different categories. 2021, Springer Nature Singapore Pte Ltd. -
Classification of Vitiligo using CNN Autoencoder
Precise recognition of skin ailment is a time-consuming procedure even for Professionals. With the invention of deep learning and medical image processing, the identification of skin disease is possible in a time-efficient manner and accurately. Autoencoder is the generative algorithm but in the proposed work it is used as a generator and as well as a classifier. In this work, a Convolutional (CNN) autoencoder was used to classify the skin disease Vitiligo. In this work encoding and decoding layers were used but in the last layer in place of reproducing the original image, the classification layer was used to classify the image. The proposed work gave training accuracy of 87.71 % whereas validation accuracy was 90.16%. 2022 IEEE. -
Classification on Alzheimers Disease MRI Images with VGG-16 and VGG-19
Balancing thoughts and memories of our life is indeed the most critical part of the human brain.Thus, its stability and sustenance are also important for smooth functioning.The changes in the structure can lead to disorders such as dementia and one such type of condition is known as Alzheimers disease.Multi modal neuroimaging like magnetic resonance imaging (MRI) and positron emission tomography (PET) is used for the early diagnosis of Alzheimers disease (AD) by providing complementary information.Different modalities like PET and MRI data were acquired from the same subject, there exists markable materiality between MRI and PET data.Mild cognitive impairment (MCI) is the initial stage with few symptoms of AD.To recognise the subjects which are capable of converting from MCI to AD is to be analysed for further treatments.In this research, specific convolutional neural networks (CNN) which are designed for classifications like VGG-16 and VGG-19 deep learning architectures were used to check the accuracy of cognitively normal (CN) versus MCI, CN versus AD and MCI to AD conversion using MRI data.The proposed research is analysed and tested using MRI data from Alzheimers disease neuroimaging initiative (ADNI). 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Classifying AI-generated summaries And Human Summaries Based on Statistical Features
In an age where artificial intelligence knows no bounds, it's crucial to know if the textual content is reliable. But, the task of identifying AI-generated content within vast volumes of textual data is a big challenge. The existing studies in feature-based classification only explored prompt-based text responses. This paper explores methods to identify AI-generated summaries using feature-based machine-learning techniques. This study uses the BBC News Summary dataset. The summaries for the dataset are then generated using three of the top-performing summarisation models. Different statistical features like Zipf's Law Score, Flesch Reading Ease Score, and the Gunning Fog Index are used for extracting features for the classification model. The aim is to differentiate AI-generated summaries from human-written summaries. The main part of the study involves extracting the statistical features from the summarized texts, which are then classified using different classification models. Different models like Support Vector Machine (SVM), Random Forest, Decision Tree, and Logistic Regression models are used in the paper. Grid Search is also used to fine-tune SVM for the best results. The right model depends on what the need is. Whether it's accuracy, F1 score, or a mix of both, there are different options to lead you to the truth. The feature-based approach in this paper helps in more explainable classification and can compare how statistical text features are different for human-written summaries and generated summaries. 2024 IEEE. -
Clinical Text Classification of Medical Transcriptions Based on Different Diseases
Clinical text classification is the process of extracting the information from clinical narratives. Clinical narratives are the voice files, notes taken during a lecture, or other spoken material given by physicians. Because of the rapid rise in data in the healthcare sector, text mining and information extraction (IE) have acquired a few applications in the previous few years. This research attempts to use machine learning algorithms to diagnose diseases from the given medical transcriptions. Proposed clinical text classification models could decrease human efforts of labeled training data creation and feature engineering and for designing for applying machine learning models to clinical text classification by leveraging weak supervision. The main aim of this paper is to compare the multiclass logistic regression model and support vector classifier model which is implemented for performing clinical text classification on medical transcriptions. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Cloud based ERP Model using Optimized Load Balancer
Enterprise Resource Planning (ERP) and Cloud computing are turning out to be increasingly more significant in the field of Information Technology (IT) furthermore, Communication. These are two distinct segments of current data frameworks, and there are a few inside and out examinations about Enterprise Resource Planning on cloud computing framework. ERP frameworks are related with a few issues, for example, shared synchronization of multi-composed assets, constrained customization, massive overhauling cost, arrangement mix, industry usefulness, reinforcement support and innovation refreshes. These issues render ERP frameworks execution excruciating, complex and time-devouring and create the need for a huge change in ERP structure to upgrade ERP frameworks foundation and usefulness. Cloud Computing (CC) stages can defeat ERP frameworks inconsistencies with financially savvy, redid and profoundly accessible figuring assets. The objective of this examination is to blend ERP and CC benefits to lessen the factor of consumption cost and execution delays through a proposed system. For this reason, investigate the unmistakable issues in current ERP frameworks through a complete correlation between ERP when moving to CC condition. Also, a conventional structure is proposed for 'Cloud-based ERP frameworks'. 2020 IEEE. -
Cloud Computing Application: Research Challenges and Opportunity
In a world with intensive computational services and require optimal solutions, cloud security is a critical concern. As a known fact, the cloud is a diverse field in which data is crucial, and as a result, it invites the dark world to enter and create a virtual menace to businesses, governments, and technology that is facilitated by the cloud. This article addresses the fundamentals of cloud computing, as well as security and threats in various applications. This research study will explore how security is remaining as a potential risk for cloud users across the globe by listing some of the cloud applications. Some viable solutions and security measures that could help us in analyzing cloud security threats are reviewed. The analyzed solutions include profound analytical thinking on how to render the solutions more impactful in each scenario. Several cloud security solutions are available to assist businesses in reducing costs and enhancing security. This study discover that if the risks are taken into consideration without any delay then the matter of solutions gets divided into four pillars, which will assist us in obtaining a more comprehensive knowledge. Visibility, compute-based security, network protection, and lastly identity security are referred as four pillars. 2022 IEEE. -
Cloud Computing with Machine Learning Could Help Us in the Early Diagnosis of Breast Cancer
The purpose of this study is to develop tools which could help the clinicians in the primary care hospitals with the early diagnosis of breast cancer diagnosis. Breast cancer is one of the leading forms of cancer in developing countries and often gets detected at the lateral stages. The detection of cancer at later stages results not only in pain and in agony to the patients but also puts lot of financial burden on the caregivers. In this work, we are presenting the preliminary results of the project code named BCDM (Breast Cancer Diagnosis using Machine Learning) developed using Mat lab. The algorithm developed in this research cancer work based on adaptive resonance theory. In this research work, we concluded how Art 1 network will help in classification of breast. The aim of the project is to eventually run the algorithm on a cloud computer and a clinician at a primary healthcare can use the system for the early diagnosis of the patients using web based interface from anywhere in the world. 2015 IEEE. -
Cloud Computing, Machine Learning, and Secure Data Sharing enabled through Blockchain
Blockchain technologies are sweeping the globe. Cloud computing & secure data sharing have emerged as new technologies, owing to current advances in machine learning. Conventional machine learning algorithms need the collection & processing of training information on centralized systems. With the introduction of new decentralized machine learning algorithms & cloud computing, ML on-device information learning is now a reality. IoT gadgets may outsource training duties to cloud computing services to enable AI at the network's perimeter. Furthermore, these dispersed edges intelligence architectures bring additional issues, also including consumer confidentiality & information safety. Blockchain has been proposed as a viable alternative to these issues. Blockchain, as a dispersed intelligent database, has evolved as a revolutionary innovation for the future phase of multiple industries' uses due to its decentralized, accessible, & safe structure. This system also includes trustworthy automatic scripting running & unchangeable information recordings. As quantum technologies have proven more viable in the latest days, blockchain has faced prospective challenges from quantum computations. In this paper, we summarize the existing material in the study fields of blockchain-based cloud computing, machine learning, and secure data sharing, as well as a basic orientation to post-quantum blockchain to offer a summary of the existing state-of-the-art in these cutting-edge innovations. 2022 IEEE. -
Cloud Enabled Smart Firefighting Drone Using Internet of Things
Internet of Things is fasted booming sector. This technology is evolved in various fields. The frequent updates in concerning the progress of Skyscraper fire or high-rise fire it is essential for us to ensure effective and safe firefighting. Since high-rise fire is typically inaccessible by ground vehicles due to some constraints or parameters. Due to less advancement in technology most skyscrapers are not furnished with proper fire monitoring and prevention system. To solve this issue this article is propose Unmanned Air Vehicles (UAVs) are making an appearance and making promises to prevent such kind of incidents. In this system, UAV can be launched from the Fire Control Unit (FCU). The proposed methodology is implemented with the help of Internet of Things (IoT). Sensors which are installed at the skyscraper detects the presence of fire and immediately send stress signals to the command and control unit from where further possible action can be taken. The pilot at the fire control unit continuously monitors the flight path and receives the video and fire scan information from the UAV. Upon detection of a stress signal or fire signal the Skyscraper position is determined with the help of Global Positioning System (GPS) and permission is requested from the applicable security agency to launch the extinguisher vehicle. The permission is granted, the coordinates of the location are filled in the system and the nearest station sends the UAV to the location. The fire suppressant are deployed it comes back to the nearest landing location and re-loaded with another fire suppressant to be carried to the fire location. The proposed methodology should improve the Quality of Service. 2019 IEEE.