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Classroom Teaching Decision-Making Optimization for Students’ Personalized Learning Needs
Get PDFClassroom teaching is the basic form of teaching in colleges and universities. Teachers need to constantly optimize classroom teaching decision-making to adapt to the ever-changing educational environment. In order to improve the quality of teaching, stimulate students' potential, and promote students' all-round development, it is necessary to fully consider the personalized needs of students in classroom teaching and provide more suitable teaching content and methods for them. To this end, this article takes the accounting major in higher vocational colleges as an example, and conducts a study on the optimization of classroom teaching decision-making for students' personalized learning needs. It introduces the psychological curve function based on expected performance and current performance to measure the satisfaction degree of students' personalized learning needs, and as an evaluation of whether students' personalized learning needs meet expectations, elaborates on the calculation method of expected performance and current performance of personalized learning. A multi-objective decision-making model for classroom teaching is constructed to achieve the three optimization objectives of maximizing the quality of classroom teaching, maximizing the attention of students' personalized learning needs, and maximizing students' dissatisfaction, and the solution method of the model is given. Experimental results verify the effectiveness of the proposed algorithm and the constructed model
A New Method of Teaching ‘Software Operation’ by a Knowledge Sharing Oriented to Practice Teaching Approach
Get PDFThe practice teaching of software operation emphasizes operation practice and practical experience, and it lays special attention on the interactions among students and teachers and teaching in accordance with aptitude. As a typical knowledge sharing platform, question-and-answer (Q&A) community is developing fast and can be taken as an assistant teaching method for the practice teaching of software operation to facilitate the teaching knowledge sharing between teachers and students. Most existing studies consider the attention mechanism of the correlation between questions and answers based on the similarity of word vectors, which has resulted in unsatisfactory accuracy and interpretability of the answers given by the model, in view of this matter, this paper studied a new method of teaching knowledge sharing oriented to the practice teaching of software operation. At first, this paper elaborated on the idea of knowledge sharing, and proposed a knowledge answer selection model for practice teaching of software operation based on the aggregation of features and attention to solve the problem with conventional studies which generally focus on the weighting of attention of a single sentence. Comparing the word granularity of sentence sequences to be matched and aggregating the comparison results have solved the problem with conventional methods in ignoring the interaction between the sentence sequences to be matched. At last, experimental results verified the effectiveness of the proposed method
IOT Based Integrated COVID-19 Self-Monitoring Tool (COV-SMT) for Quarantine
Get PDFCOVID-19 Self-Monitoring Tool (COV-SMT) is the research developed to address multiple issues in monitoring quarantined individuals due to COVID-19 infection. As COVID-19 is still highly infectious despite the availability of vaccines, the implementation of contactless Internet of Things (IoT) technology should be encouraged to minimize the need for medical staff to perform daily health checks and thus prevent them from being directly infected during checking. This research aims to develop an effective method to monitor quarantined individuals regarding their vital signs, such as body temperature, heart rate, and oxygen level. A contactless self-monitoring tool integrated with a stages algorithm is developed to monitor these quarantined individuals with the help of IoT technology. It can provide a consistent platform for patients or users to transfer information or data through networks, including personalized healthcare domains. COV-SMT is an effective tool to streamlet the overall process of taking measurements from quarantined individuals. It integrates multiple sensors into one tool while providing a better overall picture with its graphical presentation to help patients and medical staff better understand their health conditions
Improving Mobile Location Prediction Using the Grey Wolf Optimization Algorithm
Get PDFThe importance of locating a mobile phone has increased significantly during last decade for security and commercial reasons. Locating the mobile phone leads to locating people. This is done by using the most common propagation models in the mobile phone network design to calculate the distance between the mobile phone and the base station, in addition to using positioning algorithms to predict the location of the mobile station. In this work, three telecommunication towers that provide mobile phone service for Zain Iraq were selected, located within the Mahmudiya area in Baghdad as a case study, and a test drive was conducted to measure the signal strength received from these base stations at more than 10 points located within the coverage area of these base stations. The Okumura-HATA model, and the UMTS propagation model were used to calculate the distances. The Gray-Wolf algorithm was used to improve mobile phone position prediction
Deep Learning and Transfer Learning Methods to Effectively Diagnose Cervical Cancer from Liquid-Based Cytology Pap Smear Images
Get PDFAs cervical cancer is considered one of the leading causes of death for women globally, different screening techniques have emerged. As the Papanicolaou technique generates high numbers of false negatives due to only testing 20% of a sample, the liquid-based cytology technique was developed to test 100% of the sample and improve accuracy. However, as the larger sample size has made it difficult to detect the lesion images through a microscope, studies have looked for ways to intelligently analyze sample. The aim of this study is to develop an artificial intelligence image recognition system that detects the lesion level of cervical cancer of liquid-based Pap smears under the Bethesda classification of cancer (NILM/LSIEL/HSIEL/SCC). For this purpose, six activities were carried out: dataset selection, data augmentation, optimization, model development, evaluation and system construction. A dataset built from publicly available Pap smear images and passed through data augmentation algorithms generated a total of 2,676 images. Two models, ResNet50V2 and ResNet101V2, were developed under Deep Learning and Transfer Learning protocols. The evaluation showed that the ResNet50V2 model obtained better performance, where the classification of HSIL and SCC type images obtained a precision of 0.98 and achieved an accuracy of 0.97. Finally, the system based on the ResNet50V2 model was built and its performance was validated
Fiat lux et facta est lux: Leonardo Reveals the Secrets of the Heart and Arteries (in Health and Disease)
Get PDFFive hundred years after his death, the figure of Leonardo da Vinci continues transmitting his tireless desire to know and learn. Leonardo is the symbol of a century in which progress impacted, shattering the thickness of dogmas. In the Quattrocento, the doors were opened, ideas spread and still feed us, clear our path, and enlighten us. Florence, in Leonardo's time, was the Silicon Valley of the Renaissance. Leonardo studied the dynamics of water flow in rivers, using colors to show the flow patterns, thus defining the continuous stress on the side walls of the river. He determined, with different colors, the flow characteristics in the center and near the edges of the rivers and extrapolated those findings to the blood that flows in the arteries. Leonardo studied the coronary artery and veins, heart and bronchia in detail and made several assumptions about the cause of atherosclerosis, based on his previous hydrodynamic studies of water flow. Leonardo theorized that diseases were derived from some imperfection in the structure of the human body and addressed the issue of atherosclerosis and its correlation with aging. He accurately described a case of portal hypertension with liver cirrhosis as well as pulmonary circulation and chronic obstructive pulmonary disease. Leonardo was the great pioneer in revealing the secrets of the heart and arterie
A Novel Method of Invisible Video Watermarking Based on Index Mapping and Hybrid DWT-DCT
Get PDFWatermarking is widely used in multimedia preservation and communication, which comprises, and is therefore not limited to, data security and validation. Security, readability, imperceptibility, and resilience are some of the key advantages of this technology. However, there are still certain issues that need to be addressed, such as the ability to withstand a variety of assaults without substantially affecting the quality and value of embedded data. Based on its operational domain, the watermarking technology may be divided into two groups: spatial and frequency watermarking. An index mapping-based watermarking approach for copyright protection of multi-media color videos is presented in this paper. We offer a hybrid discrete wavelet transform and discrete cosine transform (DCT) watermarking algorithm for digital video watermarking of a color video watermark. Peak signal to noise ratio (PSNR), similarity structure index measure (SSIM), have been used to analyze the distortion produced by watermarking. It is suggested that the proposed video watermarking method provides greater imperceptibility in conjunction with each other with the human visual system and it offers higher robustness against different attacks
Swin Transformer-Based Segmentation and Multi-Scale Feature Pyramid Fusion Module for Alzheimer’s Disease with Machine Learning
Get PDFAlzheimer Disease (AD) is the ordinary type of dementia which does not have any proper and efficient medication. Accurate classification and detection of AD helps to diagnose AD in an earlier stage, for that purpose machine learning and deep learning techniques are used in AD detection which observers both normal and abnormal brain and accurately detect AD in an early. For accurate detection of AD, we proposed a novel approach for detecting AD using MRI images. The proposed work includes three processes such as tri-level pre-processing, swin transfer based segmentation, and multi-scale feature pyramid fusion module-based AD detection.In pre-processing, noises are removed from the MRI images using Hybrid Kuan Filter and Improved Frost Filter (HKIF) algorithm, skull stripping is performed by Geodesic Active Contour (GAC) algorithm which removes the non-brain tissues that increases detection accuracy. Here, bias field correction is performed by Expectation-Maximization (EM) algorithm which removes the intensity non-uniformity. After completed pre-processing, we initiate segmentation process using Swin Transformer based Segmentation using Modified U-Net and Generative Adversarial Network (ST-MUNet) algorithm which segments the gray matter, white matter, and cerebrospinal fluid from the brain images by considering cortical thickness, color, texture, and boundary information which increases segmentation accuracy. After that, multi-scale feature extraction is performed by Multi-Scale Feature Pyramid Fusion Module using VGG16 (MSFP-VGG16) which extract the features in multi-scale which increases the detection and classification accuracy, based on the extracted features the brain image is classified into three classes such as Alzheimer Disease (AD), Mild Cognitive Impairment, and Normal. The simulation of this research is conducted by Matlab R2020a simulation tool, and the performance of this research is evaluated by ADNI dataset in terms of accuracy, specificity, sensitivity, confusion matrix, and positive predictive value.
 
Enhanced Firefly Optimization Based Classifier to Diagnose Multimodality Breast Cancer Images
Get PDFBreast cancer is one of the most affecting carcinoma for women from long time. Early detection is necessary to increase the lifespan of patients. In this study deep learning and machine learning approaches are applied to histopathological, mammogram and ultrasound breast cancer modalities. In- order to increase the efficacy of diagnosis of these modalities. Study has been carried out in majorly four phases. First phase involved collection of the datasets of all the three modalities mentioned earlier. Second phase consists of extracting relevant features using Resnet-18. Third phase involves feeding the extracted information to enhanced firefly or to the existing optimization techniques. Fourth phase consists of considering selected features as input to the classifiers. Then enhanced firefly based classifier compared with the existing ant colony and genetic algorithm based classifier. Enhanced firefly based classifier displays better results compared to the state of art approaches
A Comparative Study of Anemia Classification Algorithms for International and Newly CBC Datasets
Get PDFData generated from modern applications and the internet in healthcare is extensive and rapidly expanding. Therefore, one of the significant success factors for any application is understanding and extracting meaningful information using digital analytics tools. These tools will positively impact the application's performance and handle the challenges that can be faced to create highly consistent, logical, and information-rich summaries. This paper contains three main objectives: First, it provides several analytics methodologies that help to analyze datasets and extract useful information from them as preprocessing steps in any classification model to determine the dataset characteristics. Also, this paper provides a comparative study of several classification algorithms by testing 12 different classifiers using two international datasets to provide an accurate indicator of their efficiency and the future possibility of combining efficient algorithms to achieve better results. Finally, building several CBC datasets for the first time in Iraq helps to detect blood diseases from different hospitals. The outcome of the analysis step is used to help researchers to select the best system structure according to the characteristics of each dataset for more organized and thorough results. Also, according to the test results, four algorithms achieved the best accuracy (Logitboost, Random Forest, XGBoost, Multilayer Perceptron). Then use the Logitboost algorithm that achieved the best accuracy to classify these new datasets. In addition, as future directions, this paper helps to investigate the possibility of combining the algorithms to utilize benefits and overcome their disadvantages