It is becoming increasingly important for society to identify hate speech on social media. Differentiating hate speech from other instances involving offensive language is a significant difficulty for automatic hate s...
It is becoming increasingly important for society to identify hate speech on social media. Differentiating hate speech from other instances involving offensive language is a significant difficulty for automatic hate speech tracking on social media. To distinguish between these categories, we train various classical machine learning models such as logistic regression, decision trees, random forest, naive Bayes, k-nearest neighbors, and support vector machines (SVM) - support vector classifier (SVC) on a dataset divided into three groups: those containing hate speech, those containing only offensive language, and those containing neither. From our practical trials, we found that the Logistic Regression algorithm and the SVM-SVC algorithm perform well in detecting hate speech and offensive language.
Delivery Tracker"is a comprehensive delivery management system designed to streamline package delivery services within an educational institution. The primary goal of this initiative is to simplify the process fo...
详细信息
Terahertz (THz) communication is expected to be a key technology for future sixth-generation (6G) wireless networks. Furthermore, reconfigurable intelligent surfaces (RIS) have been proposed to modify the wireless pro...
详细信息
Federated Learning (FL) is a decentralized machine learning strategy that ensures training data stays on personal devices while also facilitating collaborative machine learning of complicated models among dispersed de...
Federated Learning (FL) is a decentralized machine learning strategy that ensures training data stays on personal devices while also facilitating collaborative machine learning of complicated models among dispersed devices. FL is used to circumvent Mobile Edge Computing’s constraints (MEC). In FL, mobile devices utilize their local data to train an ML model that is controlled by the Federated Learning server. The model changes, i.e. the model’s weights, are subsequently sent to the FL server for collection. This stage is performed several times until the desired precision is obtained. Federated Learning has several issues, including communication costs, resource allocation, privacy, and security. The Federated Learning method is outlined in this work, and existing solutions to overcome its constraints are categorized and reviewed. Furthermore, there are several research directions and unanswered questions that must be addressed while considering a better method to Maximize Federated Learning Models in The Mobile Edge Computing Environment.
The realization of capacitive fractional-order circuit elements based on distributed layer R-C-NR (resistive, capacitive, resistive) structures is analyzed for implementation in thick-film technology. The metal contac...
The realization of capacitive fractional-order circuit elements based on distributed layer R-C-NR (resistive, capacitive, resistive) structures is analyzed for implementation in thick-film technology. The metal contacts for layer connections are found to introduce parasitic capacitance which is modeled in this work treating the contacts as lumped-element capacitors. A design method for the algorithmic suppression of the parasitic capacitance is presented. The design method uses a genetic algorithm to optimize the interconnections and parameters of the R-C-NR structures that comprise the circuit to counteract the effects of the parasitic capacitance. Using modified nodal analysis, the impact of the parasitic capacitance on the admittance characteristics is simulated and suppressed by the algorithm. Simulations validate this method, with best performance for fractional orders between 0 and 0.5, where it is possible to design circuits with a frequency range of constant admittance phase of 2.5 to 4 decades with a maximum admittance phase deviation of 2 degrees.
Human-Robot Cooperation (HRC) is a field which focuses on employing the best skills of both the robot and the human working together to achieve a common or shared task more efficiently. In most cases, both the human a...
详细信息
Accurately estimating population density is a crucial component of policy-making for the development of any country. Traditionally, population density has been estimated through labor-intensive surveys that can be tim...
详细信息
This demonstration presents CryptojackingTrap, an advanced cryptojacking malware detection solution that stands out among similar works due to its superior evasion resilience. The demonstration covers the architecture...
详细信息
ISBN:
(数字)9798350364422
ISBN:
(纸本)9798350364439
This demonstration presents CryptojackingTrap, an advanced cryptojacking malware detection solution that stands out among similar works due to its superior evasion resilience. The demonstration covers the architecture, design, and implementation of CryptojackingTrap, highlighting its effectiveness in detecting cryptojacking attempts, even in scenarios where attackers employ evasion techniques to reduce the hash rate by up to tenfold. The evaluation includes rigorous experimental testing against both miner and non-miner executables. This demonstration provides cybersecurity specialists with a deep understanding of the algorithm, showcasing its robustness and potential for extension, and offering insights into benchmarking detection solutions using publicly available datasets and opensource codebases.
Coronary Heart Disease (CHD) is one of the major causes of death worldwide. Bangladesh and other developing nations face similar challeng.s. Most people wait until it is too late to recognise that their cardiac proble...
Coronary Heart Disease (CHD) is one of the major causes of death worldwide. Bangladesh and other developing nations face similar challeng.s. Most people wait until it is too late to recognise that their cardiac problems are getting worse. For this reason, early detection is essential to reduce the death toll or major health effects from CHD. This paper’s main goal is to use supervised machine learning (ML) techniques to improve the accuracy of CHD prediction for a Bangladeshi population. ML methods including KNN, Random Forest, Decision Tree, Naive Bayes, and Binary Logistic Regression Model are used in our research methodology to predict CHD on two distinct datasets: one from Bangladesh and the other from Canada. Synthetic data for Bangladeshi dataset were generated by using ADASYN which produces accuracy of 88.12% . On the other hand, using SMOTE, the obtained accuracy was around 93.79%. Both accuracies were achieved by applying Random Forest Algorithm. Binary Logistic Regression obtained highest accuracy for the Canadian dataset which is 72.33%.
Objective: This research explores the link between playing digital games and stress reduction among students across diverse educational levels. The study investigates optimal gaming duration for stress reduction. Meth...
Objective: This research explores the link between playing digital games and stress reduction among students across diverse educational levels. The study investigates optimal gaming duration for stress reduction. Method: The study employs two approaches: a broad survey capturing student opinions and a focused method involving 14 volunteers playing games while monitoring stress levels using a questionnaire pre-gaming and post-gaming, over a 21-day span. Key Results: The general survey indicates that over 80% of students find digital games helpful in stress reduction. However, an in-dept. analysis reveals around 55% to 60% experience actual relaxation post-gaming, typically lasting less than 2 hours. Few exhibit consistent long-term stress reduction. The focus group shows daily stress reduction for some, with occasional spikes attributed to academic stress. Conclusion: Digital games have a positive impact on student stress levels, providing effective daily stress reduction in short gaming sessions of 15 to 30 minutes. The impact of longer gaming duration remains beyond this study’s scope.
暂无评论