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Study on deep neural networks and big data is merging now by several aspects to enhance the capabilities of intrusion detection system (IDS). Many IDS models has been introduced to provide security over big data. This study focuses on the intrusion detection in computer networks using big datasets. The advent of big data has agitated the comprehensive assistance in cyber security by forwarding a brunch of affluent algorithms to classify and analysis patterns and making a better prediction more efficiently. In this study, to detect intrusion a detection model has been propounded applying deep neural networks. We applied the suggested model on the latest data set available at online, formatted with packet based, flow based data and some additional metadata. The data set is labeled and imbalanced with 79 attributes and some classes having much less training samples compared to other classes. The proposed model is build using Keras and Google Tensorflow deep learning environment. Experimental result shows that intrusions are detected with the accuracy over 99% for both binary and multi-class classification with selected best features. Receiver operating characteristics (ROC) and precision-recall curve average score is also 1. The outcome implies that Deep Neural Networks offers a novel research model with great accuracy for intrusion detection model, better than some models presented in the literature.

PCOS (polycystic ovary syndrome) is a common hormonal disorder that affects many women during their reproductive years. It is marked by hormonal imbalances, leading to ovarian cysts, and can result in health issues such as infertility, diabetes, and even heart problems. Diagnosing PCOS accurately and early can be challenging, as it requires specific medical expertise. However, spotting PCOS promptly allows individuals to follow medical recommendations, which can lead to healthier lifestyles. In this study, we examined a dataset consisting of 541 patient records to enhance the detection of PCOS using advanced machine learning techniques. We established a data preprocessing pipeline that rigorously addressed missing values and identified outliers, while also normalizing the data to ensure it was ready for input. For feature selection, we applied advanced techniques such as SelectKBest, Chi‐Square, and XGBoost. These methods helped us pinpoint the most predictive attributes, which improved the interpretability and efficiency of our models. Hyperparameter tuning was carefully performed through grid search and cross‐validation, ensuring that each model was optimized for the best prediction accuracy. Importantly, our research highlights how effective machine learning can be in predicting PCOS. The logistic regression and support vector machine model stood out with its remarkable accuracy of 99.7753%. Furthermore, we created a user‐friendly web application to facilitate smooth deployment and real‐time analysis. This provides healthcare professionals with a handy tool for identifying early risks related to PCOS. The web application features an intuitive interface where users can easily input clinical information and receive immediate risk assessments. A comparative analysis of traditional and ensemble machine learning models with advanced feature selection to detect polycystic ovary syndrome.

Photovoltaic (PV) fault detection is crucial because undetected PV faults can lead to significant energy losses, with some cases experiencing losses of up to 10%. The efficiency of PV systems depends upon the reliable detection and diagnosis of faults. The integration of Artificial Intelligence (AI) techniques has been a growing trend in addressing these issues. The goal of this systematic review is to offer a comprehensive overview of the recent advancements in AI-based methodologies for PV fault detection, consolidating the key findings from 31 research papers. An initial pool of 142 papers were identified, from which 31 were selected for in-depth review following the PRISMA guidelines. The title, objective, methods, and findings of each paper were analyzed, with a focus on machine learning (ML) and deep learning (DL) approaches. ML and DL are particularly suitable for PV fault detection because of their capacity to process and analyze large amounts of data to identify complex patterns and anomalies. This study identified several AI techniques used for fault detection in PV systems, ranging from classical ML methods like k-nearest neighbor (KNN) and random forest to more advanced deep learning models such as Convolutional Neural Networks (CNNs). Quantum circuits and infrared imagery were also explored as potential solutions. The analysis found that DL models, in general, outperformed traditional ML models in accuracy and efficiency. This study shows that AI methodologies have evolved and been increasingly applied in PV fault detection. The integration of AI in PV fault detection offers high accuracy and effectiveness. After reviewing these studies, we proposed an Artificial Neural Network (ANN)-based method for PV fault detection and classification.

Hybrid PVT systems simultaneously produce electrical energy using photovoltaic technology and thermal energy using a heat extraction method that collects induced heat from the module. The purpose of this work is to establish a PVT system based on characterization, efficiency study, and performance analysis for both an electrical and a thermal system. A mathematical analysis of the electrical, thermal, and optical model is performed to establish the proposed system. Three types of heat exchanger pipes, including stainless steel, aluminum, and copper, are considered for a heat transfer analysis of the system. The results include temperature profiling, a comparison of the PVT system’s different components, and an overall output and efficiency study for all of the mentioned pipes. Results show that the obtained electrical and thermal efficiency for stainless steel is 0.1653 and 0.237, respectively, for aluminum it is 0.16515 and 0.2401, respectively, and for copper it is 0.16564 and 0.24679, respectively. After comparison, it was found that the overall efficiency for stainless steel is 0.40234, for aluminum is 0.40526, and for copper is 0.41244. Thus, this study will enhance the opportunity to provide an effective hybrid PVT energy management system.

Hybrid PVT solar systems offer an innovative approach that allows solar energy to be used to simultaneously generate thermal and electrical energy. It is still a challenge to develop an energy-efficient hybrid PVT system. The aim of this work is to develop a mathematical model, investigate the system’s performance based on parameters, include sensitivity analysis in the upper layer mainly photovoltaic part, and provide an efficient and innovative system. Performance analysis of the hybrid system is obtained by establishing a mathematical model and efficiency analysis. The electrical model and thermal model of the hybrid system is also obtained by appropriate and complete mathematical modeling. It establishes a good connection of the system in the context of electrical analysis and power generation. The parameters variation impact and sensitivity analysis of the most important parameters, namely, irradiance, ambient temperature, panel temperature, wind speed, and humidity in the PV panel section, are also obtained using a MATLAB model. The results show the effective increase or decrease in the electrical power and sensitiveness in the output of the system due to this modification. Related MPP values as a result of these parameters variation and their impact on the overall output of the hybrid PVT system are also analyzed.

A Hybrid Photovoltaic Thermal (PVT) system is one of the most emerging and energy-efficient technologies in the area of solar energy engineering. This review paper provides a comprehensive review of hybrid PVT systems in the context of the history of PVT, general classification, and parameter analysis. Several cell technologies with spectrum analysis are discussed to understand the application’s ability and energy efficiency. Hybrid PVT concept, characteristics, and structure analysis is also discussed in this study. An extensive analysis on the classifications of hybrid PVT systems from the recent literature is also presented here. These literatures are identified based on several criteria. In order to provide a complete and energy-efficient technology, an innovative classification of the hybrid PVT system is proposed in this paper. This proposed classification is a combination and upgrade of various existing classifications mentioned in recent research studies. Parameters have a significant and unavoidable impact on the performance and efficiency of the hybrid PVT system. A brief analysis of different parameters and the optimization of the system is conducted after reviewing recent research articles. This analysis provides insights into the impact of parameter variations on the system. A novel parameter model comprising parametric and optimistic analyses is also presented in this paper. It provides a detailed parametric description that significantly affects the performance and efficiency of the hybrid PVT system. Finally, the assessment focuses on a critical analysis of the main challenges in adopting PVT technology and suggests ways to overcome these barriers.

Coronavirus disease 2019 (COVID-19) has made a huge pandemic situation in many countries of the world including Bangladesh. If the increase rate of this threat can be forecasted, immediate measures can be taken. This study is an effort to forecast the threat of present pandemic situation using machine learning (ML) forecasting models. Forecasting was done in three categories in the next 30 days range. In our study, multiple linear regression performed best among the other algorithms in all categories with R2 score of 99% for first two categories and 94% for the third category. Ridge regression performed great for the first two categories with R2 scores of 99% each but performed poorly for the third category with R2 score of 43%. Lasso regression performed reasonably well with R2 scores of 97%, 99% and 75% for the three categories. We also used Facebook Prophet to predict 30 days beyond our train data which gave us healthy R2 scores of 92% and 83% for the first two categories but performed poorly for the third category with R2 score of 34%. Also, all the models’ performances were evaluated with a 40-day prediction interval in which multiple linear regression outperformed other algorithms.

By 2030, chronic obstructive pulmonary disease (COPD) is expected to become one of the top three causes of death and a leading contributor to illness globally. Chronic Obstructive Pulmonary Disease (COPD) is a debilitating respiratory disease and lung ailment caused by smoking-related airway inflammation, leading to breathing difficulties. Our COPD Healthcare Monitoring System for COPD Early Detection addresses this critical need by leveraging advanced Machine Learning (ML) and Deep Learning (DL) technologies. Unlike previous studies that predominantly rely on image datasets alone, our advanced monitoring system utilizes both image and text datasets, offering a more comprehensive approach. Importantly, we manually curated our dataset, ensuring its uniqueness and reliability, a feature lacking in existing literature. Despite the utilization of popular models like nnUnet, Cx-Net, and V-net by other papers, our model outperformed them, achieving superior accuracy. XGBoost led with an impressive 0.92 score. Additionally, deep learning models such as VGG16, VGG19, and ResNet50 delivered scores ranging from 0.85 to 0.89, showcasing their efficacy in COPD detection. By amalgamating these techniques, our system revolutionizes COPD care, offering real-time patient data analysis for early detection and management. This innovative approach, coupled with our meticulously curated dataset, promises improved patient outcomes and quality of life. Overall, our study represents a significant advancement in COPD research, paving the way for more accurate diagnosis and personalized treatment strategies.

Globalization is certainly a blessing for us. Still, this term also brought such things that are constantly not only creating social insecurities but also diminishing our mental health, and one of them is Cyberbullying. Cyberbullying is not only a misuse of technology but also encourages social harassment among people. Research on Cyberbullying detection has gained increasing attention nowadays in many languages, including Bengali. However, the amount of work on the Bengali language compared to others is insignificant. Here we introduce a Hybrid ensemble method using a voting classifier in Bangla Cyberbullying detection and compare this with traditional Machine Learning and Deep Learning Classifiers. Before implementation, Exploratory Data Analysis was performed on the dataset to gather better insight. There are lots of papers that have already been published in other languages where it is seen that the hybrid approach provides better outcomes compared to traditional methods. Thus, we propose a highly well-driven method for Cyberbullying detection on the Bangla dataset using the hybrid ensemble method by voting classifier. The overall deployment consists of three Machine Learning classifiers, three Deep Learning classifiers, and a Hybrid approach using the voting classifier. Finally, the Hybrid ensemble method yields the best performance with an accuracy of 85%, compared with other Machine and Deep Learning methods.