Fusion: Practice and Applications最新文献

{"title":"Machine Learning Data Fusion for Plant Disease Detection and Classification","authors":"El Mehdi .., A. Saddik","doi":"10.54216/fpa.080104","DOIUrl":"https://doi.org/10.54216/fpa.080104","url":null,"abstract":"It is crucial to quickly identify plant diseases since they impede the development of affected plants. Despite the widespread use of Machine Learning (ML) models for this purpose, the recent advances in a subset of ML known as Deep Learning (DL) suggest that this field of study has much room for improvement in terms of detection and classification accuracy. To identify and categorize plant diseases, a wide variety of established and customized DL architectures are deployed with several visual analysis methods. In this study, we use deep learning techniques to create a model for a convolutional neural network that can identify and diagnose plant diseases using very basic photos of healthy and sick plant leaves. The models were trained using an open library of 20639 photos that included both healthy and diseased plants across 15 different classifications. Some model architectures were trained, with the highest performance obtaining a success rate of 97.70% in detecting the correct [plant, illness] pair (or healthy plant). Due to its impressive success rate, the model is a valuable advising or early warning tool, and its technique might be developed to help an integrated plant disease diagnosis system function in actual production settings.","PeriodicalId":269527,"journal":{"name":"Fusion: Practice and Applications","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121174671","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":"A Learning Model for Acute Myeloid Leukemia Prediction Using Dense Polynomial Dimensionality-Based Predictor","authors":"K. Venkatesh, S. Pasupathy, S. Raja","doi":"10.54216/fpa.120212","DOIUrl":"https://doi.org/10.54216/fpa.120212","url":null,"abstract":"Analysis of microarray data is extremely complex and considered as a hot topic in recent research. Acute Myeloid Leukemia (AML) prediction based on machine learning shows huge impact on prediction which automatically diagnoses the disease severity and any malfunctions. It is important to design the relevant classifier that processes the large data volume with large data size. Deep learning is an updated machine learning approach for mitigating these issues. It is easy to handle the huge volume of data because of the large number of hidden layers. The proposed classification methodology is used for understanding the training of the proposed Dense Polynomial Dimensionality based Predictor Model (). The hidden neuron numbers are large in a sufficient way where the proposed is elaborated to predict AML. AML and ALL samples are classified using five layers in the deep network model. The data is partitioned as 20% data and 80% data testing and training in the network. Compared with other classifiers, the satisfying outcome from the proposed is higher and fulfilling. The validation is done in three datasets: Kaggle, Gene expression and Bio GPS and it gives 96% accuracy, 94% precision, 96% recall, 96% F1-score, and 98% AUROC while executing with Kaggle; then, 95.50% accuracy, 94% precision, 95% recall, 96% F1-score, and 96% AUROC is achieved while executing with Gene expression and finally 98% accuracy, 94.5% precision, 98.5% recall, 96% F1-score, and 94% AUROC is achieved while executing with Bio GPS. Based on this analysis, it is proven that the model works well with the proposed and establishes a better trade-off.","PeriodicalId":269527,"journal":{"name":"Fusion: Practice and Applications","volume":"261 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122692746","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":"Crop Recommendation Using Machine Learning","authors":"Akshita Waldia, Pragati Garg, Priyanka Garg, Rachna Tewani, A. Dubey, Anurag Agrawal, Great Learning India Data Scientist","doi":"10.54216/fpa.060203","DOIUrl":"https://doi.org/10.54216/fpa.060203","url":null,"abstract":"The population of India is over one billion. Nearly 65 percent of the population of India lives in villages with the main occupation being agriculture. The diverse climatic conditions in the country result in the production of a large number of agricultural items. Many surveys have proved that the suicide rate of farmers is proliferating over years due to the selection of the wrong crop resulting in less yield. In some areas, farmers lack information about the composition of soil and weather conditions and may choose the wrong crop to sow which results in lesser yield. Production of crops depends on geographical parameters like humidity, rainfall and properties of soil such as pH, and NPK content. Integration of technology with agriculture helps the farmer to improve his production. The main goal of agricultural planning is to achieve the maximum yield rate of crops by using a limited number of land resources. This paper mainly focuses on recommending the appropriate crop using ML Algorithms ( Decision Tree, Naive Bayes, Random Forest ) based on soil composition and weather conditions to maximize the yield of the farm and increase the economic condition of India’s farmers.","PeriodicalId":269527,"journal":{"name":"Fusion: Practice and Applications","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124649453","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 for False Information Detection in Social Internet of Things","authors":"Esmeralda Kazia","doi":"10.54216/fpa.100103","DOIUrl":"https://doi.org/10.54216/fpa.100103","url":null,"abstract":"By capitalizing on object relationships and local navigability, the Social Internet of Things (SIoT) is one of the burgeoning paradigms that could solve the technical challenges of conventional IoT. Because of this paradigm's capacity to combine conventional IoT with social media, it is possible to create smart objects and services with greater utility than those created using conventional IoT infrastructures. In recent years, scholars have become interested in SIoT, leading to a plethora of works examining various mechanisms for providing services and technologies within this context. In this vein, we present a comprehensive review of recent research covering important aspects of SIoT. In this research, we give a detailed justification for the function of several machine learning paradigms and provide a practical application of hitherto unexamined concerns relating to erroneous data and other social IoT. First, we give a classification of false news detection approaches and an analysis of these techniques. Second, the potential uses for detecting fake news are examined at length, including how it might be applied to the areas of fake profile detection, traffic management, bullying detection, etc. We also suggested a detailed review of the possibilities of machine learning algorithms for detecting bogus news and intervening in social networks. In our paper, we introduce categories of fake news detection methods providing a comparison between these methods. After that, the promising applications for false news detection is extensively discussed in terms of fake account detection, bot detection, bullying detection, and security and privacy of SIoT. Also, the paper contains a discussion of the potential of machine learning approaches for fake news detection and interventions in SIoT networks along with the state-of-the-art challenges, opportunities, and future search prospects. This article seeks for aiding the readers and researchers in explaining the motive and role of the different machine learning paradigms to offer them a comprehensive realization for so far unexplored issues related to false information and other scenarios of SIoT networks.","PeriodicalId":269527,"journal":{"name":"Fusion: Practice and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116855407","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":"Skin Cancer Detection Using Deep Learning and Artificial Intelligence: Incorporated model of deep features fusion","authors":"Ahmed Abdelaziz, A. N. Mahmoud","doi":"10.54216/fpa.080201","DOIUrl":"https://doi.org/10.54216/fpa.080201","url":null,"abstract":"Among the most frequent forms of cancer, skin cancer accounts for hundreds of thousands of fatalities annually throughout the globe. It shows up as excessive cell proliferation on the skin. The likelihood of a successful recovery is greatly enhanced by an early diagnosis. More than that, it might reduce the need for or the frequency of chemical, radiological, or surgical treatments. As a result, savings on healthcare expenses will be possible. Dermoscopy, which examines the size, form, and color features of skin lesions, is the first step in the process of detecting skin cancer and is followed by sample and lab testing to confirm any suspicious lesions. Deep learning AI has allowed for significant progress in image-based diagnostics in recent years. Deep neural networks known as convolutional neural networks (CNNs or ConvNets) are essentially an extended form of multi-layer perceptrons. In visual imaging challenges, CNNs have shown the best accuracy. The purpose of this research is to create a CNN model for the early identification of skin cancer. The backend of the CNN classification model will be built using Keras and Tensorflow in Python. Different network topologies, such as Convolutional layers, Dropout layers, Pooling layers, and Dense layers, are explored and tried out throughout the model's development and validation phases. Transfer Learning methods will also be included in the model to facilitate early convergence. The dataset gathered from the ISIC challenge archives will be used to both tests and train the model.","PeriodicalId":269527,"journal":{"name":"Fusion: Practice and Applications","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121751920","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":"Relevance Mapping based CNN model with OSR-FCA Technique for Multi-label DR Classification","authors":"S. .., V. –, Ragav Venkatesan, S. Malathi","doi":"10.54216/fpa.110207","DOIUrl":"https://doi.org/10.54216/fpa.110207","url":null,"abstract":"In computer vision, multi-label classification (MLC) is especially important for medical picture analysis. We use MLC to classify diverse stages of diabetic retinopathy (DR) using colour fundus pictures of varying brightness and contrast. As a result, ophthalmologists can now identify the early warning symptoms of DR and the varying stages of DR, allowing them to begin therapy sooner and prevent further difficulties. Using the outlier-based shallow regularization fuzzy clustering approach (OSR-FCA), for classification we present a deep learning method in this paper's picture segmentation task. The fundamental feature of the proposed system is the ability to identify and analyse different degenerative changes in the retina that occur alongside the progression of DR without requiring the patient to undergo costly diagnostic procedures like dye injections. Photographs are first resized, converted to grayscale, cleaned of noise, and the contrast increased by the use of histogram equalization adopting the CLAHE method. The clipping limit of CLAHE is optimized by the help of the rat optimization algorithm, which is applied throughout the histogram process. In addition, a Gaussian metric regularization to the objective function in OSR-FCA is a great way to enhance clustering approaches that use fuzzy membership with sparseness which is based on neutrosophic set. This research proposes a new approach called Relevance Mapping on Multi-Class Label (RMMCL) for locating and viewing regions of interest (ROI) inside a segmented picture. These representations give better explanations for the predictions of the DL model founded on a convolutional neural network-(CNN). The validation of two ML datasets showed the projected model outperformed the existing models by achieving an average correctness of 97.27 percent over five stages of the IDRID dataset.","PeriodicalId":269527,"journal":{"name":"Fusion: Practice and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129084257","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 Steering Actuator System to Improve Driving of Autonomous Vehicles based on Multi-Sensor Data Fusion","authors":"Nasseer K. Bachache, Ali Muhssen Abdul-Sadah, Bashar Ahmed Khalaf","doi":"10.54216/fpa.110106","DOIUrl":"https://doi.org/10.54216/fpa.110106","url":null,"abstract":"In autonomous vehicles, the control unit must be based on two main goals, first maintains the stability of the car second follows the desired path. All things considered, the controller's effectiveness is heavily dependent on the details of the steering system actuators. The necessary steering is set by a higher-order controller. The time delay of the steering actuator is one of the main features affecting the performance of the controller. While the artificial intelligence and artificial ethic are new apparatuses in autonomous vehicles but their ICs and electrical component are exposed to fusion. This paper primarily presents a more reliable system work during the fusion of multi-sensor information. We design the requirements of the steering system and the sureness of stability control in autonomous vehicles, also finding the suitable parameters for high-level control algorithms to compensate for time delay and ensure vehicle stability. The vehicle's steering angle response was obtained by testing the actuator of electric power steering (EPS) undergoing different speeds. In fact, using the identification of the system has been beneficial because obtaining the transfer function is easier than the actual methods which need the implementation of a mathematical model of the system. The system response of the Input-output has been defined via MATLAB. Full vehicle model simulation results indicate that the found adjustment parameter improves lane-tracking performance in a basic architecture by reducing oscillation and lateral error relative to other instances. The simplified steering system is the primary improvement brought by this effort.","PeriodicalId":269527,"journal":{"name":"Fusion: Practice and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129272684","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":"Physical Activity Monitoring for Older Adults through IoT and Wearable Devices: Leveraging Data Fusion Techniques","authors":"H. M. Salman, Hasan Faleh Hamdan, Raed Khalid, S. Al-Kikani","doi":"10.54216/fpa.110204","DOIUrl":"https://doi.org/10.54216/fpa.110204","url":null,"abstract":"The emergence of low-cost individual sensing devices has facilitated the application of data fusion methods to yield insights useful for score-level, rank-level, or hybrid-level fusion. Intelligent tools for fusion processing, such as fuzzy methods and optimization algorithms, may be used to the deluge of raw data generated by these devices. The use of numerous sensors allows for multi-levelhybrid-level fusion, and the combination of several models for intelligent systems allows for fusion system design optimized for score improvement. Multimedia data fusion applications and machine learning methods can be used to accomplish data fusion in cloud settings. For older people in independent living conditions, a physical activity assessment framework (PAAF) that uses deep learning models for fusion to identify activity and evaluate progress based on the spectral domain of each window is needed. This study highlights the significance of data fusion in outlining the needs for IoT devices in networked computers for distant patient monitoring. In order to provide for the health of the elderly without compromising their comfort or freedom of choice, we need a seniors network based on the Internet of Things and wearable health technology. The sensors' functionality was investigated by analyzing data gathered from the environment and the organisms within it. The proposed PAAF-IoT architecture has many layers, each one connected to a different device, with the most important part being the integration of data from all of them to classify types of physical activity. Cloud services geographically close to the customer are used to process the resulting mountain of data, reducing end-to-end delay and facilitating prompt responses from healthcare professionals. Data fusion in healthcare and remote patient monitoring are demonstrated through the deployment of an app that allows doctors to remotely administer prescriptions and maintain track of patients' medical histories.","PeriodicalId":269527,"journal":{"name":"Fusion: Practice and Applications","volume":"135 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124542260","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":"Multi-objective Decision Making Model for Stock Price Prediction Using Multi-source Heterogeneous Data Fusion","authors":"N. Metawa, Maha Mutawea","doi":"10.54216/fpa.090105","DOIUrl":"https://doi.org/10.54216/fpa.090105","url":null,"abstract":"Stock exchanges are developed as an essential component of economies, as they can promote financial and capital gain. The stock market is network of economic connections where share is bought and sold. Stock Market Prediction (SMP) is quite useful to investors. An effective forecast of stock prices is offer shareholders with suitable help in making appropriate decisions regarding if sell or purchase shares. The employ of Machine Learning (ML) and Sentiment Analysis (SA) on data in microblogging sites are developed as a famous approach to SMP. However, the heterogenous data fusion in stock market field is a big challenge. This paper introduces an effective Cat Swarm Optimization with Machine Learning Enabled Microblogging Sentiment Analysis for Stock Price Prediction technique. The presented model investigates the social media sentiments to foresee SPP. Firstly, the proposed model executes data pre-processing and Glove word embedding approach. Next, the weighted extreme learning machine approach was utilized for the classification of sentiments for SPP. Lastly, the CSO system was exploited for optimal adjustment of the parameters related to the WELM model. The experimental validation of the proposed approach was executed using microblogging data. The results show that the proposed method outperforms the previous studies.","PeriodicalId":269527,"journal":{"name":"Fusion: Practice and Applications","volume":"9 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130261063","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":"Regression Analysis and Artificial Neural Network Approach to Predict of Surface Roughness in Milling Process","authors":"Zaineb Hameed Neamah, Ahmad Al Al-Talabi, Asma A. Mohammed Ali","doi":"10.54216/fpa.130103","DOIUrl":"https://doi.org/10.54216/fpa.130103","url":null,"abstract":"Surface roughness (Ra) has a significant influence on the fatigue strength, corrosion resistance, and aesthetic appeal of machine components. Ra is hence a crucial manufacturing process parameter. This study predicts Ra of aluminum alloy Al-7024 after milling. Regression analysis and artificial neural network (ANN) modeling approaches are suggested for predicting Ra values. For better surface roughness, the cutting parameter must be set properly. Spindle speed, feed rate, and depth of cut have been chosen as predictors. Through 31 study cases, regression and ANN were used to examine how these parameters affected Ra. The measurement of surface roughness, together with comprehensive Ra analysis and regression analysis. The findings of this investigation indicate that Ra was predicted by both the regression and ANN models. convergent results from model predictions are obtained. This convergence highlights the promising methodology used in this work to forecast Ra in the milling of Al-7024. The findings demonstrated that, in comparison to the regression model, which had an average variation from the actual values of roughly 1%, The surface roughness was accurately predicted by the ANN model.","PeriodicalId":269527,"journal":{"name":"Fusion: Practice and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130511182","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}