Turkish Journal of Computer and Mathematics Education (TURCOMAT)
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BIOINFORMATICS TOOLS: ESSENTIAL FOR THE DEVELOPMENT AND DISCOVERY OF MEDICINES
No full textPharmaceutical research and development is a difficult, high-risk, time-consuming, and potentially lucrative process. Pharmaceutical corporations invest millions of dollars to get a medicine to market. A novel medication demands technical competence, human resources, and a large capital commitment. It also requires stringent adherence to laws on testing and manufacturing standards before a new medicine may be used in the general public; in fact, some drugs fail to enter the market. All of these considerations simply raise the expense of researching and developing a novel chemical entity. Bioinformatics/Tools in the drug design process has a favorable impact on the whole process and may speed up different processes of drug design while lowering costs and total time. The current note focuses on bioinformatics\u27 importance in the drug development and research method
EFFECTS OF ETHNO-MATHEMATICS INSTRUCTIONAL APPROACH AND PROBLEM-BASED LEARNING STRATEGY ON STUDENTS’ INTEREST, ACHIEVEMENT AND RETENTION IN GEOMETRY IN BENUE STATE, NIGERIA
No full textThis study examined the effects of the Ethno-mathematics instructional approach and Problem-based learning strategy on students\u27 interest, achievement, and retention in geometry in Benue State, Nigeria. Quasi-experimental design involving pretest, posttest, and post-posttest with two experimental groups and one control group. Twelve research questions guided the study and twelve hypotheses were tested at 0.05 level of significance. The population comprised 20,213 Junior Secondary Two (JS II) students, with a sample of 1,200 students selected using simple random sampling. Two instruments were used for data collection namely; Geometry Interest Ratings Scale and Geometry Achievement Test. Content validity index for GIRS was 0.78 and construct validity for GAT was 0.88. The reliability of GIRS was tested using Cronbach Alpha formula which yielded an index of 0.81 and K-R21 was used to determine the reliability index of GAT which yielded an index of 0.75. Data were analyzed using mean and standard deviation for research questions and Analysis of Covariance (ANCOVA) for hypotheses The findings indicated a significant difference in the mean interest ratings of students taught geometry using the ethno-mathematics instructional approach, problem-based learning strategy, and conventional teaching approach. However, no significant difference was found in the mean interest scores between male and female students taught geometry using the ethnomathematics instructional approach or problem-based learning strategy. Additionally, there was a significant difference in the mean achievement and retention scores of students taught geometry using these approaches compared to the conventional teaching approach. No significant difference was found in the mean achievement or retention scores between male and female students taught using these approaches. The ANCOVA result on the interaction effect between methods and gender on retention indicates that there is no significant interaction effect between Ethno-mathematics instructional approach, Problem-based learning strategy and gender on retention. Based on the findings of this study, it is recommended that: Students should be subjected to consistent utilisation of ethno-mathematical operations within their culture, adoption of the ethno-mathematics instructional approach in the school system, and training of mathematics teachers in the use of ethno-mathematics instructional approach to improve students\u27 interest, achievement and retention in geometry
THE CAUSE-AND-EFFECT PRINCIPLE: OPACITY OF SLAVERY IN TONI MORRISON’S BELOVED
No full textWhen Toni Morrison embarked on her exploration of slavery, she grappled with two crucial inquiries. The first question delved into the resilience of her nation in enduring the unimaginable cruelties inflicted upon them. The second question probed the conspicuous absence of any mention in European historical records regarding the origins of the slave trade and the subsequent forced migration. The novel is a fearful picture of how bestiality and cruelty could come together to question the crude practices of sexual exploitation, emotional breakdown and physical torture in the name of developing the Western economy. The paper is an attempt to look at the circumstances that gave way to the genesis of one of the most important forerunners of slavery and its repercussions
A Secure Crypto-Biometric System Utilizing GMM Encoder and BCH
No full textNow that cloud computing has reached maturity, a diverse array of providers and services are available in the cloud. On the other hand, security issues continue to receive a lot of focus. Despite the many benefits of cloud computing, users are hesitant to embrace the technology due to concerns about their security and privacy. While biometric technologies are rapidly becoming an integral part of many secure identification and personal verification solutions, they do pose certain challenges when stored in the cloud owing to privacy regulations and the requirement to have faith in cloud providers when handling biometric data. In this work, we offer a crypto biometric system that can be used with cloud computing to solve these issues. This system ensures that no private biometric data is revealed
An approach for two-dimensional convolutional neural networks for hourly passenger boarding demand prediction based on uneven smart-card data
No full textAn invaluable resource for understanding passenger boarding patterns and forecasting future travel demand is the tap-on smart-card data. Positive instances, on the other hand—boarding at a given bus stop at a certain time—are less common than negative instances when looking at the smart-card data (or instances) by boarding stops and by time of day. Machine learning algorithms that are used to estimate hourly boarding numbers at a certain location have been shown to be much less accurate when the data is imbalanced. Before using the smart-card data to forecast bus boarding demand, this research tackles the problem of data imbalance in the data. To create fake traveling instances to add into a synthetic training dataset containing more evenly distributed traveling and non-traveling examples, we suggest using deep generative adversarial networks (Deep-GAN). Next, a deep neural network, or DNN, is trained on the synthetic dataset to predict which instances from a given stop in a certain time frame will travel and which ones won\u27t. According to the findings, resolving the data imbalance problem may greatly enhance the predictive model\u27s functionality and make it more accurate in predicting ridership profiles. The suggested strategy may create a synthetic training set with a better similarity so diversity and, therefore, a stronger prediction capability, according to a comparison of the Deep-GAN\u27s performance with other conventional resampling techniques. The study emphasizes the importance of the issue and offers helpful recommendations for enhancing the quality of the data and model performance for individual travel behavior analysis and travel behavior prediction
Price Negotiating Chatbot on E-commerce website
No full textThe rise of internet purchasing in the last few years is quite remarkable. Despite this growth, not all aspects of internet buying have been perfected. For example, unlike in physical stores, you can\u27t haggle with vendors about prices. A chatbot for product negotiations is now live. Customers are able to acquire a good deal on product(s) with the help of the chatbot. The approach might end up hurting either the goods seller or the customer\u27s budget, as it affects a lot of different parts of online buying. We have devised an algorithm that, in conjunction with the forecast of previously accessible data, can offer a price in order to circumvent such scenarios. Using unrelated data elements or qualities or techniques that aren\u27t a good fit for a certain dataset might reduce the accuracy of price prediction. In light of the fact that erroneous product price predictions may lead to significant financial losses for online retailers, these companies avoid relying only on price prediction algorithms. When data becomes too large or when a characteristic that was relied on the model\u27s prediction becomes unavailable, certain models can fail. Then, in order to keep the model\u27s accuracy and dependability intact, such modifications must be handled. We have made an effort to address some of these concerns in our chatbot system
An Automated News Text Classification Information System
No full textAn information system for the categorization of news texts using machine learning algorithms is being planned and developed in this project. An online platform and an automated categorization system make up the data system in question. We have preprocessed the text data. In order to train classifiers using the grid search method, many experiments were carried out. We have tested four different categorization algorithms: naïve Bayesian, logistic regression, random forest, and artificial neural network. Several measures, including F-score, recall, and precision, have been used to assess the trained classifiers\u27 classification quality. An additional goal in developing the website was to provide easy access to the information system
DATAFITS: A HETEROGENEOUS DATA FUSION FRAMEWORK FOR TRAFFIC AND INCIDENT PREDICTION
No full textIn order to create a complete dataset, this study presents DataFITS (Data Fusion on Intelligent Transportation System), an open-source system that gathers and fuses traffic-related data from several sources. Our hypothesis is that traffic models may benefit from improved information coverage and quality thanks to a heterogeneous data fusion architecture, which would boost the effectiveness and dependability of ITS systems. Two applications that made use of event categorization and traffic estimate models confirmed our hypothesis. For nine months, DataFITS gathered four different kinds of data from seven different sources and combined them into a spatiotemporal domain. While incident categorization utilized the k-nearest neighbors (k-NN) method with Dynamic Time Warping (DTW) and Wasserstein metric as distance measurements, traffic estimation models used polynomial regression and descriptive statistics. The findings show that by fusing data, DataFITS was able to enhance information quality for up to 40% of all roads and dramatically expand road coverage by 137%. While incident classification reached 90% accuracy on binary tasks (incident or non-incident) and about 80% on categorizing three distinct categories of events (accident, congestion, and non-incident), traffic estimate earned an R2 score of 0.91 using a polynomial regression model
Ethical Considerations in Deploying AI Systems in Public Domains: Addressing the ethical challenges of using AI in areas like surveillance and healthcare
No full textThe general use of AI technology, especially in public sectors like security and even in the medical field, has been subject to a number of questions to do with ethics. This paper aims to understand the ethical dilemmas concerning the instantiation of Artificial Intelligence in these fields, specifically privacy, bias, responsibility, and openness concerns. In security, advanced technologies like facial recognition and predictive policing attract concerns pertaining to violation of privacy, importation of race bias, and lack of social control, among others. In health care, the AI systems employed in the diagnosis and treatment of patients call into question issues to do with patient choices, data privacy, and discrimination in medical treatment. Within the scope of the paper, the author considers contemporary ethical standards and legislation regulating AI creation and finds some deficiencies. In response to these issues, some of the potential work for the future highlighted in the paper includes enhancing the legal policies in the area of AI, insisting on the importance of ethical multi-disciplinary research, and creating awareness of the effects of AI in society. It underlines the requirement for responsible and explainable AI, the availability of efficient tools helping in monitoring and controlling AI, and increased people’s involvement in creating AI policies to state that the launched AI technologies will be compliant with the people’s benefit. With these suggestions, the paper sought to add knowledge to the ongoing discussion on AI ethics and ensure that decent utilization of AI systems is enhanced with reverence to human rights and ethical norms
A NOVEL CORONARY HEART STROKE PREDICTION SYSTEM USING MACHINE LEARNING TECHNIQUES
No full textOver the past few decades, cardiovascular diseases have surpassed all other causes of death as the main killers in industrialised, underdeveloped, and developing nations. Early detection of heart conditions and clinical care can lower the death rate. Based on the patient\u27s various cardiac features, we proposed a model for forecasting heart disease and identifying impending heart disease using machine learning techniques In most cases,input is received through numerical data of various parameters, and output findings are generated in real-time, predicting whether or notthe patient has a disease. We\u27ll use a variety of supervised machine learning methods before deciding which one is best for the model. Existing systems rely on classical deep learning models, which are inefficient and imprecise. They aren\u27t as accurate as the proposed model and take a little longer to process