Masters Thesis

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https://dspace.nstu.ac.bd/handle/123456789/95

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DEVELOPMENT OF A WEB BASED ATTENDANCE MANAGEMENT AND ADMIT CARD GENERATOR SYSTEM
(2021-10-20) NUTON CHAKMA
This paper contains features for class teachers to track student attendance, generate class attendance reports. The outcome demonstrates that this system can assist school or university administrators in managing student attendance. Furthermore, a student can pay exam fees based on the average attendance of the entire subject throughout the current semester. In addition, a student can use this system to generate his or her exam admit card for the upcoming exam. As a result, this project is often beneficial for a student to quickly monitor his entire semester's actions rather than the traditional complex method. In this project, our proposed system as named “Web based Attendance management and admit card generator system” will implemented for students and teachers. Basically, we focus on this system for simplify the attendance management procedure for students at a university.
DEVELOPMENT OF ONLINE STUDENT COURSE REGISTRATION SYSTEM
(2022-03-20) Md. Khalilur Rahman
This paper presents the research and findings of a student course registration system at a university. It has been seen that in manual course registration system students must be physically present on their campus to do registration for the semester and they have to wait for verification from their concern officials and also have to wait at the bank for making payment and so on. So, the manual system of course registration is very time consuming. We proposed a computerized system that overcomes all limitations of manual system. In this project, our proposed system as named “Development of Online Student Course Registration System” will implemented for students. Basically, we focus on to simplify the course registration procedure for students at a university.
IMPACT AND SENSITIVITY ANALYSIS OF SCHOOL AND COLLEGE RESULTS ON UNIVERSITY ADMISSION TEST AND HIGHER STUDIES
(2022-03-30) MD. KAMRUL HASAN
This research study will conduct statistical data analysis techniques to observe university student's results impact and sensitivity based on their pre-university academic results. To determine student’s performance there will be used two different analysis. One is ‘impact analysis’ and another one is ‘sensitivity analysis’. In this research, it is observed how pre university academic results keep influence on university results. Besides, it will be determined what is the sensitivity of university results according to pre-university academic results. In sensitivity analysis, we can find how much the university results are dependent on the previous results. Here the results of sensitivity analysis will come into percentage form. This study will be conducted on a huge amount of data. For statistical analysis different software will be used as Microsoft office excel, mintab, matlab etc. The fundamental goal is to find the relationship between honors and previous academic results and determine the effectiveness of previous results on university results to show the relation. Finally, this paper showed some significant relationship between previous results and university results. Even master’s results is also dependent on the pre-university results. After all, this study might be recommendations for implementing different regression models and several considerations for the analysis of academic performance in higher education.
A STUDY ON THE IMPACT OF INTERNET USAGE ON THE ACADEMIC PERFORMANCE OF THE STUDENTS
(2021-10-29) SHAJID HOSSAIN HEMAL
Education systems have greatly changed with the emergence of Internet. It has a significant impact on how students learn things and how educational institutes operate. But its impact can also be contradicting. Internet addiction can slowly poison the minds of our youths and stand in the way of pursuing their goals. Proper analysis of the effects this technology has on today’s youth can help determine the factors that allow a student to succeed academically and those that do not. Also, predicting the academic performance beforehand can help determine the changes that must be incorporated to improve the educational system. So, this research attempts to accurately analyze the effects of internet usage on student’s academic progress and also predict the class performance of the students by using different machine learning algorithms. About 700 student’s data were collected for this study from an offline survey taken from different schools, colleges and universities, all situated in Noakhali district. These data will be analyzed with the help of statistical evaluation methods and machine learning algorithms such as Logistic Regression, Decision Trees, Random Forests and Naive Bayes Classifier. The results from these models will be analyzed using different performance metrics to determine the factors that contribute the most in their academic activities.
Bangla Handwritten Character & Digit Recognition Using Convolutional Neural Network
(2021-10-29) TANVIR HOSSAIN
Bangla handwriting recognition is an important and quite popular for the last few years. This paper proposes a Convolutional Neural Network(CNN) model to recognize the Bangla Handwritten Characters and Digits which contains 50 Character Classes and 10 Digit Classes. The most challenging part is to reduce the noise because of the different shapes of Banlga characters and digits. We propose to use a reliable and large dataset to get the best accuracy output with our proposed Convolutional Neural Network(CNN) model. We also compare the accuracy with the existing model. Finally, we hope we will make a model with less error and the best output result.
Automated Surface Roughness Prediction from Scanning Electron Microscope Images using Convolutional Neural Network
(2024-05-13) Md. Shariar Kabir Asif
In recent years, the advent of Convolutional Neural Networks (CNNs) has opened up new avenues for advancing Surface Roughness (SR) prediction methodologies, particularly through the analysis of Scanning Electron Microscope (SEM) images. However, notable gaps existed in the literature regarding the application of CNNs to SEM images for SR prediction. This research addresses these existing gaps by employing CNN to analyze SEM images for SR prediction, particularly focusing on the comparative analysis of different magnification levels. Three distinct datasets, magnified at 150X, 250X, and 500X, were utilized, comprising 2097, 2103, and 2102 images respectively. These images undergo preprocessing techniques to enhance the CNN model's ability to generalize to new images. Subsequently, a sequential CNN model, comprising 27 layers including convolutional, max pooling, batch normalization, flatten, and fully connected dense layers, is developed and trained on the datasets. The study provides detailed comparative analyses of accuracy, precision, recall, and F1-score across magnification levels. Results indicate that the dataset magnified at 500X consistently outperforms the others, exhibiting superior accuracy (75.7%), precision (0.65), recall (0.72), and F1-score (0.72). This suggests that higher magnification levels provide finer details and clearer images, enabling the model to discern subtle features with increased accuracy. Additionally, the 500X dataset exhibits a better balance between minimizing false positives and false negatives, making it more suitable for real-world applications requiring detailed analysis of microscopic structures. These findings underscore the importance of selecting appropriate magnification levels in SEM imaging for accurate SR prediction. Keywords: Surface Roughness; Surface Roughness Prediction; Scanning Electron Microscope (SEM); Convolutional Neural Network (CNN); Magnified SEM Images
The introduction of the thesis is covered in this part.
(2023-03-20) Nasir Uddin
DEVELOPMENT OF COMPUTER BASED ANSWER SCRIPT EVALUATION SYSTEM
(2023-03-15) Md Nurul Amin
Computer-based evaluation of answer scripts is becoming increasingly important in the field of education. In this thesis, we propose a computer-based answer script evaluation system that evaluates both text and figures in answer scripts. For text evaluation, we consider two cases and use different methods for each case, including writing score, keyword matching score, Cosine similarity score, NLP-based cosine similarity score, and plagiarism matching score. Our system achieves high accuracy scores of 83% and 85% for each case, respectively. For figure evaluation, we use cosine similarity and Euclidean distance techniques, achieving accuracy scores of 85% and 72%, respectively. Our system provides a robust and reliable way to evaluate answer scripts in an automated manner. By using a combination of different evaluation techniques, we are able to effectively evaluate both text and figures, improving the accuracy of the overall evaluation. This system has the potential to greatly reduce the workload of educators, while also providing students with more immediate feedback on their work. Overall, our system represents a significant step forward in the field of automated answer script evaluation.
CRICKET PLAYERS SELECTION FOR NATIONAL TEAM AND FRANCHISE LEAGUE USING MACHINE LEARNIG ALGORITHMS
(2023-03-20) MD.ROBEL
Cricket player selection is a crucial task for both national teams and franchise leagues. Traditionally, selectors rely on their experience and knowledge to evaluate a player's physical fitness, batting, and bowling performance. However, with the advancements in machine learning algorithms, it is possible to automate and improve the selection process. In this study, we propose a machine learning-based approach for cricket player selection. The proposed approach uses a combination of physical fitness data, batting and bowling statistics, and other relevant metrics to create a comprehensive player profile. We then use machine learning algorithms, such as decision trees, random forests, and linear regression, to identify the most promising players. To evaluate the proposed approach, we collect data on a large number of cricket players and their performance in national and franchise leagues. We then train and test several machine learning models on this data, comparing their accuracy and performance. Our results demonstrate that the proposed approach can significantly improve the selection process and identify players with high potential. Overall, this study highlights the potential of machine learning algorithms for cricket player selection. By leveraging the power of data and automation, selectors can make more informed decisions and improve the performance of national teams and franchise leagues. The selection of cricket players for national teams and franchise leagues involves considering various factors such as physical fitness, batting and bowling performance. In this study, we propose a machine learning-based approach to assist in the selection process. We collected data on physical fitness measures, batting and bowling performance of players from past matches and tournaments. We then applied various machine learning algorithms such as logistic regression, decision trees, and random forests to analyze the data and predict player selection. Our results show that physical fitness measures such as speed, agility, and endurance play a crucial role in player selection. Additionally, players with high batting and bowling performance are also more likely to be selected. We also found that the random forest algorithm outperformed other machine learning models in predicting player selection with an accuracy of 87%. These findings suggest that machine learning algorithms can effectively assist in the selection of cricket players for national teams and franchise leagues, based on physical fitness, batting and bowling performance
PREDICTION OF IMPACTS AND OUTBREAK OF COVID-19 USING DISTINCT LEARNING ALGORITHM
(2023-03-13) TASPIA TAZRI CHAITY
The COVID-19 pandemic has caused significant negative impacts on daily wage workers in various professions due to restrictions such as lockdowns and social distancing measures. To analyze and predict these impacts, we collected data on 1665 daily wage workers from eight different professions through an offline survey. After preprocessing the data to remove duplicates, null values, and cleaning, we encoded categorical features and split the dataset into training and testing sets. We used seven machine learning algorithms including logistic regression, decision tree, random forest, support vector machine, AdaBoost, KNN, and XGBoost to develop a predictive model. Our study found that the random forest algorithm had the highest accuracy of 92.1%, followed by XGBoost with 91% accuracy. Our research highlights the potential of machine learning techniques in predicting the impacts of COVID-19 on daily wage workers in various professions. Our thesis paper serves as a guide for policymakers and government officials to understand the effects of COVID-19 on daily wage workers and to implement measures to mitigate these negative effects. Furthermore, the predictive model developed in this study can help predict the impact of future pandemics or crises on these workers and support the development of targeted policies and interventions to help them.
E-commerce Excellence: Building a User-Centric Online Shopping Experience
(2024-05-13) Janefar Akbar
Our E-commerce Web Application project is all about making online shopping easy and enjoyable. Imagine a platform where buyers and sellers seamlessly connect, and shopping is a breeze. That's what we're building. E-commerce is to simplify the online shopping experience. With our user-friendly website, you can browse a wide range of products, make safe payments, and track your orders in real time. We want every transaction to be not just easy but also secure. At its foundation, our intended e commerce platform empowers users with a simple to use feature-rich, and secure online shopping experience, elevating convenience and choice. Shoppers can seamlessly explore a vast product catalog, access detailed product information, read user reviews, and make secure payments, all from the comfort of their smartphone displays. our E-commerce system includes a full Admin Module, streamlining product administration, inventory monitoring, order fulfillment, and customer engagement. Advanced analytics and reporting capabilities enable data-driven decision-making, supporting development and efficiency. The idea is notable for its dedication to responsiveness and inclusion. Because it was created with a mobile-first mindset and complies with accessibility guidelines, the application may be used by everyone, including those with impairments. E-commerce Web Application project is not merely a response to market demands; it's a testament to our commitment to shaping the future of online commerce. By embracing technological innovation, user centric design, and ethical considerations, we aim to create a platform that doesn't just sell products but fosters meaningful connections between businesses and consumers.
RICE DISEASE IDENTIFICATION THROUGH TRANSFER LEARNING
(2024-05-13) Umme Sadia Raisa
In the rural landscape of Bangladesh, where rice reigns supreme as the cornerstone of sustenance, disease poses a formidable threat to the nation's agricultural backbone. With a staggering 78% of the country's arable land dedicated to rice cultivation, the specter of crop diseases looms large, exacting a heavy toll on farmers' livelihoods and the nation's food security. Efficient disease detection is paramount to safeguarding rice yields and ensuring food security. Traditionally, this task has been labor-intensive and time-consuming, demanding extensive manual effort. However, the advent of automated systems offers a beacon of hope in this agricultural conundrum. Hence deep learning is getting popular for its transformative technology rapidly eclipsing conventional approaches with its unparalleled performance. The objectives of this research were twofold: to achieve higher recognition accuracy for rice diseases through transfer learning and to compare the performance of the three CNN architectures. The study found that VGGNet16 and InceptionResNetV2 models, when optimized with RMSprop, achieved high accuracy rates of 98.44% and 98.21%, respectively, demonstrating their robustness and reliability for rice disease detection. In contrast, ResNet50 showed lower performance with an accuracy of 50.99%, indicating its limitations in this specific application. This research contributes by demonstrating the effectiveness of transfer learning in improving disease detection accuracy and providing a comparative analysis of CNN models and optimizers. The findings highlight the potential of these models for broader agricultural applications, offering insights into crop health management beyond rice cultivation. The study underscores the importance of selecting appropriate models and optimization strategies to enhance the efficiency and accuracy of automated disease detection systems, thereby supporting agricultural sustainability and food security. KEYWORDS: Rice; Disease Classification; CNN; Deep Learning; Transfer Learning; VGGNet; ResNet50; InceptionResNetV2; Feature Extraction;
A Comparative Analysis of Machine Learning Models for Liver Disease Prediction
(2024-05-13) Raihan Uddin
Liver disease is a widespread global health concern that demands early detection for effective treatment and improved patient outcomes. Traditional diagnostic methods have their limitations, necessitating further exploration into the use of machine learning to enhance predictive accuracy, accessibility, and cost-effectiveness in liver disease diagnosis. This study provides an in-depth analysis of machine learning classifiers such as Support Vector Machine, K-Nearest Neighbors, Decision Tree, Multilayer Perceptron, and Random Forest for liver disease prediction and compares their performance. The primary objective of this research is to identify the most effective machine-learning classifier for precise and accessible liver disease prediction. This research has the potential to significantly impact patient care, as it can aid in the development of efficient diagnostic tools, reduce the global burden of liver disease, and improve patient outcomes. This study's robust methodology includes feature selection via the Pearson Correlation matrix, leveraging the 10-fold cross-validation technique, and a balanced dataset generated using the Synthetic Minority Over-sampling Technique (SMOTE) on our collected dataset from distinct hospitals across Bangladesh. Our approach, employing a Random Forest classifier, achieved an impressive 80.5 percent accuracy in liver disease prediction. Furthermore, our model demonstrated a precision of 0.88 and a recall of 0.85, indicating its robust performance in identifying positive and negative cases. Keywords: Liver Disease, Pearson Correlation, Support Vector Machine, K-Nearest Neighbors, Decision Tree, Random Forest, Multilayer Perceptron.
An integrated mobile app for rice disease detection and weather prediction
(2024-05-30) Fazla Rabbi
This project seeks to create a cutting-edge mobile application that combines the capacity for disease detection in rice crops and precise weather forecasting for agricultural areas. Rice is a staple crop for billions of people around the world, making agriculture a crucial economic sector. However, if these diseases are not promptly detected and treated, rice crops are vulnerable to a number of illnesses that can result in significant output losses. The success and growth of crops are significantly influenced by the weather as well. In order to provide rice farmers and other agricultural stakeholders with a complete solution, the suggested mobile app makes use of cutting-edge technology, such as machine learning and the integration of real-time weather data. The project's primary goals are as follows: 1. Rice Disease Detection: In order to recognize common illnesses impacting rice crops, such as blast, bacterial leaf blight, and sheath blight, the app will use picture recognition and machine learning algorithms. With the use of an app, users may take pictures of damaged rice plants using their mobile devices, and the program will instantly analyze and diagnose the situation, enabling farmers to take quick corrective action. 2. Weather Prediction: To deliver precise weather forecasts for particular agricultural areas, the app will link existing sources of weather data and apply cutting-edge predictive models. Farmers will be able to make informed decisions about planting, harvesting, and pest control thanks to this feature, which will contain data on temperature, humidity, precipitation, wind speed, and other pertinent elements. 3. Crop Management Insights: Based on disease detection and meteorological information, the app will provide actionable insights. Users will get advice on crop management techniques, such as when to apply pesticides, when to schedule irrigation, and how to rotate their crops. Losses will be reduced and crop yields will be optimized as a result. 4. User-Friendly Interface: Farmers with various degrees of technological ability will be able to utilize the mobile app because of its user-friendly layout. To reach a large user base, it will be accessible on both the Android and iOS platforms. 5. Data Privacy and Security: The app will use strong encryption and abide by data protection laws to guarantee the security and privacy of user data. This project intends to equip rice farmers with the resources and knowledge required to improve crop yield and sustainability by combining disease detection and weather forecasting in a single mobile application. Increased yields, a decrease in the use of pesticides, better resource management, and improved lives for rice farmers are all potential effects of the app. Keywords: Rice disease detection, mobile app, weather prediction, agriculture, machine learning, image recognition, crop management, agricultural technology, data privacy, sustainability.
Enhancing Computer Generated Hologram Quality via Deep Learning Based On U-Net Architecture
(2024-05-30) Md. Maidul Islam
This thesis investigates the advancement of computer-generated hologram (CGH) quality using deep learning methodologies, with a focus on the U-Net architecture. Employing the DIV2k dataset as the foundation, the study proposes a novel approach integrating a specialized 3U configuration within the U-Net framework. This configuration comprises meticulously designed U-shaped modules tailored to extract intricate details and subtle nuances inherent in holographic imagery. Evaluation metrics such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and reconstruction time are employed to assess the model's performance. Comparative analysis against established models, namely Holonet and Holoencoder, underscores the superior efficacy of the proposed method in faithfully reproducing holographic images. The findings reveal substantial enhancements in hologram quality, characterized by elevated PSNR and SSIM values, alongside notable gains in computational efficiency manifested through reduced reconstruction times. This research contributes significantly to the progression of holographic imaging technology, offering promising avenues for future exploration and innovation in the realm of computer-generated holography.
Weather Prediction in Bangladesh Using Distinct Artificial Intelligence Techniques
(2024-05-30) Joysri Rani Das
Weather forecasting holds immense significance in Bangladesh due to its heavy reliance on agriculture and vulnerability to weather-related risks. This study aims to develop reliable weather forecast models for Rainfall, Temperature, and Humidity using artificial intelligence algorithms such as Decision Trees, Random Forests, K-Nearest Neighbors, Support Vector Machines, and Multilayer Perceptron Neural Networks. Historical weather data from the Bangladesh Meteorological Department (BMD) undergo preprocessing to handle missing values and outliers. Various AI techniques are then applied, leveraging their ability to address complex and nonlinear interactions in data. The prediction models are evaluated using performance metrics like Mean Absolute Error and Root Mean Square Error, Root Mean Square Error, R-square accuracy, R-square Value, and Relative Absolute Error. This research contributes to advancing weather forecasting in Bangladesh, benefiting sectors like agriculture, disaster management, and public safety. We divided our Temperature, Humidity, and Rainfall Datasets into two intervals: 1999-2022 which consist of 34 weather stations and 1980- 2022 consist of 26 weather stations. And we used 80% of our data for training and 20% for testing our models. We analyzed our data using both interval and did a detailed analysis of the yearly, monthly, and seasonal trends of Temperature, Rainfall, and Humidity. It also highlights the relationship between These weather parameters. From our results, we have found that Random Forest and Extreme Gradient Boosting Perform better than the Support vector machine, K-Nearest Neighbor, Multilayer neural network, and Decision Tree algorithm. And It is also noticeable that The results of the time interval 1999- 2022 are better than the time interval 1980-2022. KEYWORDS: Artificial Intelligence; Machine Learning; Random Forest; Decision Tree; Support Vector Machine; K-Nearest Neighbor; Extreme Gradient Boosting; MSE; RMSE; MAE; RAE; R- Square Value; R-Square Accuracy.
Development of Web-based Library Management System
(2025-07-30) Rohan Sinha
Library management system is a project which aims in developing a computerized system to maintain all the daily work of the library .This project has many features which are generally not available in normal library management systems like facility of user login and a facility of teachers login. It also has a facility of admin login through which the admin can monitor the whole system .It also has facility of an online notice board where teachers can student can put up information about workshops or seminars being held in our colleges or nearby colleges and librarian after proper verification from the concerned institution organizing the seminar canada it to the notice board . It has also a facility where students after logging in their accounts can see the list of books issued and its issue date and return date and also the students can request the librarian to add new books by filling the book request form. The librarian after logging into his account i.e. admin account can generate various reports such as student report , issue report, teacher report and book reportOverall this project of ours is being developed to help the students as well as staff of library to maintain the library in the best way possible and also reduce the human efforts. The whole system will be developed by Iterative Model. The complete project is developed using HTML (Hyper Text Markup Language), CSS (Cascading Style Sheet), React.js, Java, Spring boot, PostgreSQL, REST API’s. Keywords: Iterative model, Computerized System, Authentication, HTML, CSS, React, Java, Spring boot, PostgresQL, REST API’s.
Deep Learning and Machine Learning Approaches for Non Contact Surface Roughness Prediction from Scanning Electron Microscope (SEM) Images
(2025-08-14) Maria Akter Luthfa
Surface roughness is crucial to the quality, functionality, and durability of manufactured parts. Nonetheless, there is a problem that conventional methods of contact measurements are costly, time-consuming, and ineffective in analyzing complicated surface geometry. To address these constraints, this work proposes deep and traditional machine learning models to specifically predict surface roughness using Scanning Electron Microscope (SEM) images, eliminating the need for physical probing or handcrafted surface analysis. Titanium alloys as a target material were chosen because of the wide range of essential applications, and SEM images were obtained at various magnification levels to capture the broad range of patterns on their surface. A custom design Convolutional Neural Network (CNN) was constructed and trained to perform regression tasks, and two pre-trained deep learning models, ResNet50 and VGG16, were also used. Simultaneously, the images were used to extract the handcrafted features, which were in turn trained by the conventional machine learning models, such as Support Vector Regression (SVR), Random Forest, and Linear Regression. The findings showed that deep learning models mostly performed better, especially VGG16, than the traditional models by providing higher accuracy and R2 values above 0.95 in the majority of cases. The developed approach has excellent prospects for application in the real world to high-precision industries, including aerospace, biomedical, and electronic manufacturing sectors, allowing for improved performance control and process optimization speed. Keywords: Surface Roughness; Surface Roughness Prediction; Scanning Electron Microscope; Convolutional Neural Network; Support Vector Regression; Random Forest; Linear Regression.
Digitalization of Energy Meter Services
(2025-07-30) Mehedi Hasan
A digital meter is a polished metric device which shows greatly exact decimal values based on their entrant [1]. In Bangladesh, digital energy meters have become more common over the recent past, especially in the urban and the industrial areas. But its service system has hardly transformed to a modern one with facilities like remotely balance check, balance transfer, remote recharge etc. Therefore, Digitalization of Energy Meter Service proposes an all-inclusive and web application that will digitalize the digital meter services in a move to resolve this problem. The entire program is developed using separate dashboards of users and administrators to ease the communication of all the involved parties. Internet services are available like enquiry and recharge of services and will also have less time consumption. In this case, we shall use HTTP (Hypertext Markup Language), CSS (Cascading Style Sheet), Python, Flask and SQLAlchemy to come up with a user friendly and slick web application. Keywords: Digital Meter Digitalization, Web-Based Application, User Dashboard, Admin Dashboard, Remote Services, Automation, Time Efficiency, Bangladesh, Utility Management.
A Hybrid Deep Learning Approach for Network Intrusion Detection and Prevention
(2025-07-30) Niloy Kumar Joy
With the ever-changing online threats that are entailing fast development, there are increased concerns on strong and intelligent network security. The static rule-based traditional intrusion detection systems (IDS) based on signature-based approach are not able to detect new or advanced attacks. The present thesis suggests a combination of both deep learning technologies based on Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) networks to improve the detection of intrusion and allow the intrusion prevention proactive measures. By combining the spatial feature extraction capability of CNN and the temporal dependence capture capacity of LSTM, CNN-LSTM model can be said to make best use of the two models when the domain is network traffic. On the NSL-KDD dataset which is a benchmark dataset that comprises of different classes of attacks namely DoS, Probe, R2L, and U2R, the model was trained and tested versus other deep learning models like the MLP, standalone LSTM and autoencoder. The presented model beat others, with the highest accuracy of 95.79 percent, and better precision, recall, and F1-score. Besides detection, this paper discusses rudimentary prevention systems to narrow the response time and constrain attacks. The findings prove that a hybrid deep learning architecture is more likely to offer a more precise, flexible, and integrated answer to intrusion detection and prevention. The study can be used to develop intelligent cyber security solutions and result as the precursor of real-time and self-learning IDS applications in the future. Keywords: Network Intrusion Detection, CNN-LSTM, Deep Learning, NSL-KDD Dataset, Cybersecurity, Intrusion Prevention, Hybrid Neural Network, Anomaly Detection

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