Fusion: Practice and Applications最新文献

{"title":"Ultrasound Medical Images Classification Based on Deep Learning Algorithms: A Review","authors":"Fairoz Q. Kareem, A. Abdulazeez","doi":"10.5281/ZENODO.4621289","DOIUrl":"https://doi.org/10.5281/ZENODO.4621289","url":null,"abstract":"With the development of technology and smart devices in the medical field, the computer system has become an essential part of this development to learn devices in the medical field. One of the learning methods is deep learning (DL), which is a branch of machine learning (ML). The deep learning approach has been used in this field because it is one of the modern methods of obtaining accurate results through its algorithms, and among these algorithms that are used in this field are convolutional neural networks (CNN) and recurrent neural networks (RNN). In this paper we reviewed what have researchers have done in their researches to solve fetal problems, then summarize and carefully discuss the applications in different tasks identified for segmentation and classification of ultrasound images. Finally, this study discussed the potential challenges and directions for applying deep learning in ultrasound image analysis.","PeriodicalId":269527,"journal":{"name":"Fusion: Practice and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130222863","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":"Micro-Expression Recognition using 3D - CNN","authors":"V. Dubey, Bhavya Takkar, Puneet Singh Lamba","doi":"10.5281/ZENODO.3825862","DOIUrl":"https://doi.org/10.5281/ZENODO.3825862","url":null,"abstract":"Micro-expression comes under nonverbal communication, and for a matter of fact, it appears for minute fractions of a second. One cannot control micro-expression as it tells about our actual state emotionally, even if we try to hide or conceal our genuine emotions. As we know that micro-expressions are very rapid due to which it becomes challenging for any human being to detect it with bare eyes. This subtle-expression is spontaneous, and involuntary gives the emotional response. It happens when a person wants to conceal the specific emotion, but the brain is reacting appropriately to what that person is feeling then. Due to which the person displays their true feelings very briefly and later tries to make a false emotional response. Human emotions tend to last about 0.5 - 4.0 seconds, whereas micro-expression can last less than 1/2 of a second. On comparing micro-expression with regular facial expressions, it is found that for micro-expression, it is complicated to hide responses of a particular situation. Micro-expressions cannot be controlled because of the short time interval, but with a high-speed camera, we can capture one's expressions and replay them at a slow speed. Over the last ten years, researchers from all over the globe are researching automatic micro-expression recognition in the fields of computer science, security, psychology, and many more. The objective of this paper is to provide insight regarding micro-expression analysis using 3D CNN. A lot of datasets of micro-expression have been released in the last decade, we have performed this experiment on SMIC micro-expression dataset and compared the results after applying two different activation functions.","PeriodicalId":269527,"journal":{"name":"Fusion: Practice and Applications","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121077780","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":"Facial Expression Recognition with Gender Identification","authors":"Aarushi Dhawan, Arpita Gupta, A. Dubey","doi":"10.54216/fpa.020203","DOIUrl":"https://doi.org/10.54216/fpa.020203","url":null,"abstract":"The human facial emotions recognition has attracted interest in the field of Artificial Intelligence. The emotions on a human face depicts what’s going on inside the mind. Facial expression recognition is the part of Facial recognition which is gaining more importance and need for it increases tremendously. Though there are methods to identify expressions using machine learning and Artificial Intelligence techniques, this work attempts to use convolution neural networks to recognize expressions and classify the expressions into 6 emotions categories. Various datasets are investigated and explored for training expression recognition models are explained in this paper and the models which are used in this paper are VGG 19 and RESSNET 18. We included facial emotional recognition with gender identification also. In this project we have used fer2013 and ck+ dataset and ultimately achieved 73% and 94% around accuracies respectively.","PeriodicalId":269527,"journal":{"name":"Fusion: Practice and Applications","volume":"71 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120842564","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":"Fusion of Water Evaporation Optimization and Great Deluge: A Dynamic Approach for Benchmark Function Solving","authors":"Saman M. Almufti","doi":"10.54216/fpa.130102","DOIUrl":"https://doi.org/10.54216/fpa.130102","url":null,"abstract":"The Water Evaporation Optimization - Great Deluge explores the synergy between the Water Evaporation Optimization Algorithm (WEOA) and the Great Deluge Algorithm (GDA) to create a novel fusion model. This research investigates the efficacy of combining these two powerful optimization techniques in addressing benchmark problems. The fusion model incorporates WEOA's dynamic exploration-exploitation dynamics and GDA's global search capabilities. By merging their strengths, the fusion model seeks to enhance convergence efficiency and solution quality. The study presents an experimental analysis of the fusion model's performance across a range of benchmark functions, evaluating its ability to escape local optima and converge towards global optima. The results provide insights into the effectiveness of the fusion model and its potential for addressing complex optimization challenges., a comprehensive performance analysis of the application of the proposed fusion model to a curated set of widely acknowledged benchmark functions, renowned for their role in evaluating the capabilities of optimization algorithms, is undertaken. By rigorously evaluating the convergence characteristics, solution quality, and computational efficiency of the algorithm, a thorough understanding of the strengths and limitations of WEOA is aimed to be provided. Through meticulous comparisons with established optimization techniques, illumination of the aptitude of WEOA in addressing diverse optimization challenges across a spectrum of problem landscapes is intended. The analytical insights, not only advancing the understanding of WEOA's applicability, but also furnishing valuable guidance for both researchers and practitioners in search of robust optimization methodologies, are proffered.","PeriodicalId":269527,"journal":{"name":"Fusion: Practice and Applications","volume":"319 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":"116633395","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":"Crime Anomaly Detection using CNN and Ensemble Model","authors":"Gautam Gupta, Prachi Aggarwal, Achin Jain, Puneet Singh Lamba, A. Dubey, Gopal Chaudhary","doi":"10.54216/fpa.110107","DOIUrl":"https://doi.org/10.54216/fpa.110107","url":null,"abstract":"Every single day, thousands of crimes are perpetrated, and hundreds may be probably taking place right now throughout the world. Without a doubt, crime is viewed as a social blight. Nothing can truly stop it, no matter what is done. Surveillance cameras, on the other hand, can dramatically minimize it. Using public surveillance camera systems to prevent, document, and minimize crime can be a cost-effective solution. Installing enough cameras to detect crimes in progress and integrating technology to automate the monitoring of the live stream from these cameras will result in the most effective systems. Because of its self-learning characteristics, the advanced Artificial Intelligence surveillance system is constantly learning and improving. The Deep Learning Algorithms applied in this work processes videos using electronic devices like cameras in real-time termed as image processing, saving both human resources and a great deal of time. The highest accuracy of 86.6% was attained by Ensemble Model, followed by Inception Model with SGD Optimizer, Leaky Relu Activation Function giving an accuracy of 83.43%. Hence, anomalies were detected efficiently using decision making in real-time surveillance scenarios.","PeriodicalId":269527,"journal":{"name":"Fusion: Practice and Applications","volume":"35 11 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":"116652078","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":"Electrocardiogram Classification Based on Deep Convolutional Neural Networks: A Review","authors":"A. Admin, A. Abdulazeez","doi":"10.54216/fpa.030103","DOIUrl":"https://doi.org/10.54216/fpa.030103","url":null,"abstract":"Due to many new medical uses, the value of ECG classification is very demanding. There are some Machine Learning (ML) algorithms currently available that can be used for ECG data processing and classification. The key limitations of these ML studies, however, are the use of heuristic hand-crafted or engineered characteristics of shallow learning architectures. The difficulty lies in the probability of not having the most suitable functionality that will provide this ECG problem with good classification accuracy. One choice suggested is to use deep learning algorithms in which the first layer of CNN acts as a feature. This paper summarizes some of the key approaches of ECG classification in machine learning, assessing them in terms of the characteristics they use, the precision of classification important physiological keys ECG biomarkers derived from machine learning techniques, and statistical modeling and supported simulation.","PeriodicalId":269527,"journal":{"name":"Fusion: Practice and Applications","volume":"34 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":"114426544","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":"Pixel Level Image Fusion in Moving objection Detection and Tracking with Machine Learning","authors":"P. Pareek","doi":"10.54216/fpa.020105","DOIUrl":"https://doi.org/10.54216/fpa.020105","url":null,"abstract":"It is not feasible for a single image sensor to convey all of the information essential to comprehend a circumstance thoroughly. The output of many image sensors combined in one place would supply more accurate or comprehensive information on the topic at hand. In recent years, multi-sensor fusion has emerged in the academic world as an emerging topic that has the potential to produce beneficial results. This is because it can aggregate the data from several different sensors. One of the primary objectives is to devise various methods for combining kinematic and visual data to track a moving object. These methods should allow us to achieve this aim. This article looks into the intricacies of various techniques to evaluate the current condition of a target and explores the outcomes of those approaches. These sorts of methods include, for instance, the Kalman filter and its expanded version, the extended Kalman filter. The study of the proposed work is to demonstrate the specifics of the development of an interacting multiple-model Kalman filter to monitor the performance of the moving target in response to a wide variety of tuning parameters. The proposed technique includes the Principal Component Analysis and spatial frequency to integrate the hazy images that were all shot with the same sensor modalities. This action was taken to achieve the aimed-for outcome. The effectiveness of the fusion is evaluated based on the results of several distinct metrics.","PeriodicalId":269527,"journal":{"name":"Fusion: Practice and Applications","volume":"19 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":"116999510","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 Review of the Common DDoS Attack: Types and Protection Approaches Based on Artificial Intelligence","authors":"Nafea ali majeed .., K. H. Zaboon, Ammar Abdullah","doi":"10.54216/fpa.070101","DOIUrl":"https://doi.org/10.54216/fpa.070101","url":null,"abstract":"Recently, the technology become an important part of our live, and it is employed to work together with the Medicine, Space Science, Agriculture, and industry and more else. Stored the information in the servers and cloud become required. It is a global force that has transformed people's lives with the availability of various web applications that serve billions of websites every day. However, there are many types of attack could be targeting the internet, and there is a need to recognize, classify and protect thesis types of attack. Due to its important global role, it has become important to ensure that web applications are secure, accurate, and of high quality. One of the basic problems found on the Web is DDoS attacks. In this work, the review classifies and delineates attack types, test characteristics, evaluation techniques; evaluation methods and test data sets used in the proposed Strategic Strategy methodology. Finally, this work affords guidance and possible targets in the fight against creating better events to overcome the most dangers Cyber-attack types which is DDoS attacks.","PeriodicalId":269527,"journal":{"name":"Fusion: Practice and Applications","volume":"16 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":"130496669","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":"Potential Energy Efficient Data Fusion Model for Wireless Sensor Networks","authors":"Rajeev Pandey, M. Kumar, Jaswant Samar","doi":"10.54216/fpa.080204","DOIUrl":"https://doi.org/10.54216/fpa.080204","url":null,"abstract":"Wireless sensor networks have made a significant contribution to wireless sensor communication system based on resource constraints and limited computational sensors. Over the last decade, several focused research efforts have been made to investigate and provide solutions to problems relating to the energy efficiency data fusion aggregation in Wireless sensor networks. However, the problem of designing routes that are energy efficient has not been resolved. It is rather a tough effort to guarantee that the lifespan of a sensor is prolonged for a longer period because of the restricted computational capabilities of sensors, which are often coupled with energy constraints. The findings of this work present an enhanced energy-efficient technique for communication in sensor networks which consists of three distinct innovative frameworks. The suggested framework known as Data Fusion with Potential Energy Efficiency (DFWPEE) is responsible for the optimization of energy. The proposed work reduces energy consumption by using probabilistic methods and clustering. During the data fusion process, the Multiple Zone Data Fusion (MZDF) architecture uses a globular topology that helps with load balancing. The strategy presents an innovative routing approach that is used to aid in the performance of energy efficient routing in large-scale wireless sensor networks. By introducing the idea of routing agents, the framework for the Tree-Based Fusion Technique (TBFT), as suggested, comes up with an innovative method for dynamic reconfiguration. The plan enables the system to determine which sensor has a higher rate of energy dissipation and then immediately transfers the job of data fusion to a node that is more energy efficient. This threshold-based technique enables a sensor to perform both the role of a cluster head and the function of a member node. The node behaves as a cluster head until it achieves its threshold remnant energy and functions as a member node after it passes the threshold residual energy. Both of these roles may be played simultaneously. The mathematical modeling was done using the conventional radio energy model which improved the dependability of attained results. The proposed system delivers enhanced energy efficient communication performance when measured against existing implemented standards for energy efficient schemes. The enhanced technique uses nearly half as much energy as LEACH while focusing on reducing the overall time taken for the process to complete leading to enhanced performance.","PeriodicalId":269527,"journal":{"name":"Fusion: Practice and Applications","volume":"27 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":"130606817","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":"Leveraging Social Media Data Fusion for Enhanced Student Evolution in Media Studies using Machine Learning","authors":"Najla M. .., Walaa .., M. Balbaa","doi":"10.54216/fpa.120215","DOIUrl":"https://doi.org/10.54216/fpa.120215","url":null,"abstract":"In the realm of media studies, understanding student evolution is a crucial aspect for educators and researchers. However, traditional research methods often struggle to capture the dynamic nature of media consumption and the intricate interactions between individuals and media content. To address this challenge, this paper focuses on leveraging social media data fusion and machine learning techniques to enhance the comprehension of student evolution. By integrating data from diverse social media sources and employing the CATBoost algorithm with the Greedy Target-based Statistics (Greedy TBS) technique, we aim to predict student outcomes based on a comprehensive set of attributes. The results showcase the superior performance of CATBoost in accurately capturing the complexities of student evolution, surpassing other machine learning algorithms. The findings hold immense significance for educators, empowering them with valuable insights into students' behaviors, preferences, and performance.","PeriodicalId":269527,"journal":{"name":"Fusion: Practice and Applications","volume":"63 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":"123649435","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}