Browsing by Author "Nur, Fernaz Narin"

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A Neural Network Based Software Defect Prediction Approach Using SMOTE and Noise Filtering-CLNI
(Research and Development Wing, MIST, 2025-12-30) Ashfaque, Ahmmed Bin; Sattar, Abdus; Jahan, Hosney; Akhtaruzzaman, M.; Nur, Fernaz Narin
Software defects can cause significant loss and system failures in software development life cycle. Software Defect Prediction (SDP) is a vital step for ensuring the quality of software. Till now, a number of machine learning models have been proposed to predict potential defects and make the software more reliable. However, SDP models suffer from the problem of imbalanced dataset, resulting in poor prediction accuracy. To mitigate this, issue several data balancing techniques, i.e., over sampling, under sampling etc. have been proposed to balance the dataset. In some cases, the data balancing methods may further introduce noisy and mislabeled samples in the dataset. To deal with these issues, in this paper, we propose a neural network based approach that combines the oversampling technique Synthetic Minority Oversampling Technique (SMOTE) with the noise filtering technique Class Level Noise Identification (CLNI). Here, we applied three different CLNI methods which are Edited Nearest Neighbor (ENN), Repeated ENN (RENN) and All-KNN. Our aim is to make the dataset clean, balanced and efficient by combining SMOTE with CLNI. In addition, we applied a number of feature selection methods to identify the most important features, further contributing towards achieving better prediction accuracy. To evaluate the effectiveness of the proposed model, we conduct experiments on several benchmark datasets (MC1, PC1, PC2, PC3 and PC4) obtained from NASA MDP and (ML, LC and JDT) AEEEM repository. The experimental results have been evaluated and compared in terms of accuracy, precision, recall and AUC-ROC curve. The experimental results demonstrated that our proposed approach has achieved up to 98% accuracy and outperformed state-of- the-art approaches.
A Novel IoT Based Accident Detection and Rescue System
(IEEE, 2020-08) Karmokar, Pranto; Bairagi, Saikot; Mondal, Anuprova; Nur, Fernaz Narin; Moon, Nazmun Nessa; Karim, Asif; Yeo, Kheng Cher
In South-East Asian cities such as Delhi, Dhaka road accidents are a very common occurrence which brings disaster to human lives as well as infrastructures. Sometimes people cannot reach hospitals prompt after an accident because of the traffic jam, deficit of ambulance, lack of a mechanism to timely propagate information to the appropriate authority. To ensure the safety of lives, this paper proposes an automated IoT based effective accident detection system. Immediately after an incident, the data information is sent to the webserver, instant SMS is forwarded to the victim's acquaintances and also to the relevant authorities such as traffic control room, nearby police station, ambulance service. To evaluate the performance of the system, a simulated road scenario has been designed. The result obtained after a thorough integration and system testing demonstrates that the proposed system not only achieves the stated goal of the research but also can deliver the expected outcome in a rather cost-effective way.
A Priority-Based Process Scheduling Algorithm in Cloud Computing
(Springer Nature Singapore Pte Ltd., 2018-12-12) Haque, Misbahul; Islam, Rakibul; Kabir, Md. Rubayeth; Nur, Fernaz Narin; Moon, Nazmun Nessa
Nowadays, cloud computing is in demand as it provides progressive pliable resource allocation, for unfailing and guaranteed services in the pay-as-you-use scheme, to cloud service users. So, there is a dispensation that all resources are made available to requesting users in an efficient manner to satisfy their needs. Process scheduling has become the key issue in cloud computing. In this paper, we have presented a priority-based process scheduling (PRIPSA) algorithm, which is developed with the block-based queue in cloud computing. It concentrates on the preemptive part as well as it calculates the energy consumption and reducing starvation of process for scheduling the process in the cloud. We provide a priority-based algorithm which considered preempt able task scheduling with block-based queue using burst time and lead time. This job is being performed by the dynamic voltage and frequency scaling (DVFS) controller in our algorithm. The load management, energy consumption, reducing the starvation problem of the processes, and maximizing the revenue are the key motives of our consideration.
A Priority-Based Process Scheduling Algorithm in Cloud Computing
(Scopus, 2020) Haque, Misbahul; Islam, Rakibul; Kabir, Md. Rubayeth; Nur, Fernaz Narin; Moon, Nazmun Nessa
Nowadays, cloud computing is in demand as it provides progressive pliable resource allocation, for unfailing and guaranteed services in the pay-as-you-use scheme, to cloud service users. So, there is a dispensation that all resources are made available to requesting users in an efficient manner to satisfy their needs. Process scheduling has become the key issue in cloud computing. In this paper, we have presented a priority-based process scheduling (PRIPSA) algorithm, which is developed with the block-based queue in cloud computing. It concentrates on the preemptive part as well as it calculates the energy consumption and reducing starvation of process for scheduling the process in the cloud. We provide a priority-based algorithm which considered preempt able task scheduling with block-based queue using burst time and lead time. This job is being performed by the dynamic voltage and frequency scaling (DVFS) controller in our algorithm. The load management, energy consumption, reducing the starvation problem of the processes, and maximizing the revenue are the key motives of our consideration.
An Advanced Method of Treating Agricultural Crops Using Image Processing Algorithms and Image Data Processing Systems
(2020-10) Salehin, Imrus; Talha, Iftakhar Mohammad; Saifuzzaman, Mohd.; Moon, Nazmun Nessa; Nur, Fernaz Narin
Smart agriculture has involved evolution, judgment, and application of new methods of using modern technology. Technological advances in agriculture will enable farmers to enhance their skills in farming. We planned technology for farming by combining an app and a SMS system through the mobile phone. Different types of virus, fungus, and bacterial infection causes a great loss of farming product. Modern technologies in various computer science fields such as image processing, data mining can be applied in this infrastructure. We use the Scale-Invariant Feature Transform (SIFT) algorithm in this paper to identify crop diseases based on various types of datasets. The SFT technique is a well-known method that is applied to find the image data with pixel integrated. Firstly, we find out all key points and store all unique data from image for next steps. After processing every pixel, we match the main key point for major disease detection. In this study, our contribution is that we are trying to identify all diseases. We are trying to provide some solutions with the help of a solution bank using SMS services and live web portals.
An improved algorithm for solving helix generation of RNA secondary structure prediction
(IEEE Xplore, 2015-04-02) Moon, Nazmun Nessa; Nur, Fernaz Narin; Hossain, Syed Akhter
This paper presents an efficient O(n2) time algorithm for solving the helix generation problem of RNA to predict the predict the secondary structure of that RNA. It encodes the RNA secondary structures as an integer permutation of helices. The helices are pre-computed by the helix generation algorithm and each integer corresponds to a candidate helix. In this paper, a helix is formed only when three or more adjacent base pairs are formed and the loop connecting the helix must be at least three nucleotides in length. From this algorithm we find all possible helices that can form in a structure. After that we predict secondary structure of RNA by SARNA-Predict based on Simulated Annealing (SA). SARNA-Predict use a permutation-based representation to the RNA secondary structure and percentage swap translocating mutation operator to find a solution with a lower free energy [1]. Calculating the minimum free energy, we find the stable secondary structure of the RNA. Full Text Link: http://doi.org/10.1109/ICCITechn.2014.7073124
Analysis of Complex Networks for Security Issues Using Attack Graph
(2019 International Conference on Computer Communication and Informatics, IEEE, 2019-09-02) Musa, Tanvirali; Yeo, Kheng Cher; Azam, Sami; Shanmugam, Bharanidharan; Karim, Asif; Boer, Friso De; Nur, Fernaz Narin; Faisal, Fahad
Organizations perform security analysis for assessing network health and safe-guarding their growing networks through Vulnerability Assessments (AKA VA Scans). The output of VA scans is reports on individual hosts and its vulnerabilities, which, are of little use as the origin of the attack can't be located from these. Attack Graphs, generated without an in-depth analysis of the VA reports, are used to fill in these gaps, but only provide cursory information. This study presents an effective model of depicting the devices and the data flow that efficiently identifies the weakest nodes along with the concerned vulnerability's origin.The complexity of the attach graph using MulVal has been greatly reduced using the proposed approach of using the risk and CVSS base score as evaluation criteria. This makes it easier for the user to interpret the attack graphs and thus reduce the time taken needed to identify the attack paths and where the attack originates from.
Breathe Safe
(ICECE 2018 - 10th International Conference on Electrical and Computer Engineering, IEEE, 2018-12-22) Siddique, Md. Junayed; Islam, Mohammad Aynul; Nur, Fernaz Narin; Moon, Nazmun Nessa; Saifuzzaman, Mohd.
Recently, garbage management is the most buzzing word to ensure a healthy environment. The dustbins are placed across an open place which are actually over burden and leads an unhygienic environment as well as spreads different types of unnamed diseases. In the present scenario as the population of Bangladesh is increasing day by day, we should maintain a clean and hygienic environment to avoid this problem. So, we propose a system by which all dustbins are interfaced with microcontroller based system having Ultrasonic sensor for waste level detection and Wi-Fi module to connect to the internet. Realtime status of all dustbins will be shown on an Android application and also shortest direction will be provided on map. Main goal is of this research is to maintain a healthy environment in our city and reduce human sufferings.
BSSID Based Monitoring Class Attendance System Using Wifi
(Proceedings of the 3rd International Conference on I-SMAC IoT in Social, Mobile, Analytics and Cloud, IEEE, 2019-12-14) Hasan, Mahadi; Saha, Dipto; Ferdosh, Jannatul; Nur, Fernaz Narin; Moon, Nazmun Nessa; Saifuzzaman, Mohd.
Recent advancements in wireless technologies have evolved the growth of smart systems in day to day life. Nowadays, people are using WiFi connectivity for accessing the Internet or local hub inside home or office. A smart phone based checking system for monitoring class attendance through WiFi signal is developed in this work. Student, Teacher & Admin need to be installing the application. Most challenging part in this system is to make software design and send notification in smartphone. When Admin set specific time and students Smartphone connected with specific WiFi that time then they get notification for class attendance.
Duty-cycle Medium Access Control for Directional Wireless Sensor Networks
(University of Dhaka, 2018-10-27) Nur, Fernaz Narin
Directional communication in wireless sensor network minimizes interference and thereby increases reliability and throughput performances of the network. Such advantages of Directional Wireless Sensor Networks (DSNs) have attracted the interests of researchers and industry experts around the globe. Furthermore, the sensor nodes with directional antennas provide extended network lifetime and better coverage performances. However, designing a communication protocol for wireless networks with directional antennas is a challenging problem due to lack of synchronization, asymmetry-in-gain, hidden terminal and deafness problems. Our endeavor in this dissertation is to address the aforementioned challenges in neighbor discovery and medium access control in Directional Sensor Networks. One of the key challenges of a directional node is to discover its neighbors due to diﬃculty in achieving synchronization among directed transmissions and receptions. Existing solutions suﬀer from high discovery latency and poor percentage of neighbor discovery either due to lack of proper coordination or centralized management of the discovery operation. In this thesis, we develop a collaborative neighbor discovery (COND) mechanism for DSNs. Using polling mechanism, each COND node directly discovers its neighbors in a distributed way and collaborates with other discovered nodes so as to allow indirect discovery. It helps to increase the neighbor discovery performance signi cantly. A Markov chain-based analysis has been carried out to quantify theoretical performances of the proposed COND system. The performance of the COND system is evaluated in NetworkSimulator Version-3 (NS-3), and the results reveal that it greatly reduces the discovery latency and increases neighbor discovery ratio compared to state-of-the-art approaches. The second contribution of this thesis is to develop a low duty-cycle directional medium access control protocol, termed as DCD-MAC, where, each pair of (parent and child sensor) nodes performs synchronization with each other before data communication. Each parent node in the network schedules data transmissions of its childs in such a way that the number of collisions occurred during transmissions from multiple nodes is minimized. A sensor node remains active only when it needs to communicate with others; otherwise, it goes to sleep state. The DCD-MAC exploits localized information of nodes in a distributed manner and it gives weighted-fair access of transmission slots to the nodes. As a nal point, we have studied the performances of our proposed MAC protocol through extensive simulations in NS-3 and the results show that the DCD-MAC gives better reliability, throughput, end-to-end delay and network lifetime compared to the related directional MAC protocols.
Enjoy and Learn with Educational Game Likhte Likhte Shikhi Apps for Child Education
(Advances in Intelligent Systems and Computing, Springer, 2018-12-12) Dipu, Md. Hasanuzzaman; Moon, Nazmun Nessa; Aunkon, Md. Walid Bin Khalid; Saifuzzaman, Mohd.; Nur, Fernaz Narin
Nowadays, children (under age 2–6 years) become so much affected to smartphone. It is impossible to take back the device when they play a game on that device. So, we realized if this affection is possible to convert to an educational game, it can be better. We decide to build an educational game that can help to teach them how to write and read a letter. A child can easily learn the technique how to write a letter properly by this game. After completing the writing part this app will play the actual pronunciation of that letter. The Graphical User Interface of this game is very attractive as well as user friendly. A child can learn both Bangla and English letter writing and reading by this game. This game is completely offline and dynamic game app. The game size is smaller than other games. And it can run smoothly on any of Android Device such as Smartphone, Tab and Smart TV.
Enjoy and Learn with Educational Game: Likhte Likhte Shikhi Apps for Child Education
(Springer Nature Singapore Pte Ltd., 2018-12-12) Aunkon, Md. Walid Bin Khalid; Dipu, Md. Hasanuzzaman; Moon, Nazmun Nessa; Saifuzzaman, Mohd.; Nur, Fernaz Narin
Nowadays, children (under age 2–6 years) become so much affected to smartphone. It is impossible to take back the device when they play a game on that device. So, we realized if this affection is possible to convert to an educational game, it can be better. We decide to build an educational game that can help to teach them how to write and read a letter. A child can easily learn the technique how to write a letter properly by this game. After completing the writing part this app will play the actual pronunciation of that letter. The Graphical User Interface of this game is very attractive as well as user friendly. A child can learn both Bangla and English letter writing and reading by this game. This game is completely offline and dynamic game app. The game size is smaller than other games. And it can run smoothly on any of Android Device such as Smartphone, Tab and Smart TV.
Human Behaviour Impact to Use of Smartphones with the Python Implementation Using Naive Bayesian
(11th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2020, IEEE, 2020-10-15) Talha, Iftakhar Mohammad; Salehin, Imrus; Debnath, Susanta Chandra; Saifuzzaman, Mohd.; Moon, Nazmun Nessa; Nur, Fernaz Narin
A change of behavior in special groups and many sustainable smart populations increasing day by day for excessive uses of smartphones. In recent years, the use of smartphones and mental imbalances have become a major problem with increasing negative effects. In our study, we find out the major problem of the negative side and its different sources like mental imbalance, stress, depression, loneliness, etc. Bayes' theorem and classifier, support vector machine, special data set of human behavior, and probability are used to calculate accuracy. For collecting data from three major sections, we use the physical methods, virtual methods, and medical reports. So, a vast data set is trained by data to compare method, and also probability is used for predicting the validity of the data model. Naive Bayes' theorem accurate 71% positive which is indicated the negative impact of human behavior. Based on the SVM classifier, we separate the barrier between the impact of positive and negative data. In SVM, we set up a parameter to measure negative and positive values. Python library function is a major component to calculate all instructions and also use for data training. Finally, we compare the results obtained by our proposed specialization with the results obtained from the three baseline landmarks.
Humidity Based Automated Room Temperature Controller Using IOT
(Proceedings of the 3rd International Conference on I-SMAC IoT in Social, Mobile, Analytics and Cloud, IEEE, 2019-12-14) Sharmin, Farah; Moon, Nazmun Nessa; Hasan, Mohd. Saifuzzaman Abir; -Bin-Al-Beruni, Shakib; Hossain, Mohammad Alam; Nur, Fernaz Narin
This research proposed a peerless methodology and implementation for an automatic switching speed electric heater, and control room temperature. Before the use of recent intelligent technologies for achieving smart room heater and automation system, different kinds of relay depending analog circuitries were used. These circuits were mainly dependent on temperature and humidity sensors which provided those circuits the major functionalities. In this research, automation is achieved through using a microcontroller which facilitated auto room temperature controlling and toggle switching. The electric fan adjusts the speed dynamically depending on the variations in the temperature of the environment. This electrical hardware fan system includes a combination of sensor, controller, driver and motor with the incorporation of embedded guided programming. The system takes the temperature sensor data, passes it to the microcontroller and controls the AC heater in the output and displays the output status in the LCD screen. By automating all these processes, it is possible to control room temperature and humidity in according to the user's necessity.
IoT based street lighting and traffic management system
(IEEE, 2018-02-12) Saifuzzaman, Mohd.; Moon, Nazmun Nessa; Nur, Fernaz Narin
In this modern era where energy is the major concern worldwide, it is our prior responsibility & liability to save energy effectively. With the development of technology, where automation system plays a vital role in daily life experience and also it is being preferred over the traditional manual system today. The main purpose of this project is to invent an intelligent system which can make decisions for luminous control (ON/OFF/DIM) considering the light intensity. Here the day and night mode can be identified by fixing a particular intensity value on LDR sensor and street light can be controlled by IR sensor. The interesting part of this paper is the installation of solar cell for the power supply but in course of circumstances, if the solar cell is unable to do so, a secondary backup DC current will maintain the situation immediately. Another remarkable part of this project is to maintain the traffic signal automatically without any help of traffic police and monitor the entire system through internet by installing surveillance camera. All the components of this project are very simple and cost effective but efficient to make a reliable intelligence system.
IOT Based Street Lighting Using Dual Axis Solar Tracker and Effective Traffic Management System Using Deep Learning
(11th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2020. IEEE, 2020-10) Saifuzzaman, Mohd.; Shetu, Syeda Farjana; Moon, Nazmun Nessa; Nur, Fernaz Narin; Hanif Ali, Mohammad
With the rapid increase of population, nonrenewable energy loss has become one of the prior concerns worldwide in recent years. Researchers are working on the proper utilization of renewable energy to save the rest of the non-renewable energy sources for future generations. One of the purposes of this research is to develop an intelligent automation system that can decide luminous control operation based on light intensity. Here, LDR sensor is installed in such a way that it can be able to identify Night and Day mode using a certain intensity value, and as well as IR sensors take over the control of street light. The solar cell will be used for power supply here and DC current will work as a secondary backup. The article also involves automatic monitoring of traffic controls through a video system. The camera incorporates automatic text retrieval from video data utilizing lip reading by decoding facial expression utilizing deep learning methods. The proposed solution includes a test data collection, an interpretation of the picture frame, and a text production of the specified words. The test data collection will be structured in the proposed methodology by integrating all potential facial expressions of different words.
IoT-Based Automatic Gas Leakage Detection and Fire Protection System
(Daffodil International University, 2022-11-20) Islam, Gazi Zahirul; Hossain, Md. Mobarak; Faruk, Md.; Nur, Fernaz Narin; Hasan, Nayeem; Khan, Khalid Mahbub; Tumpa, Zerin Nasrin
Gas Leakage and its fatal effects are a great concern throughout the world, especially in developing countries like Bangladesh. Every year lots of people died and countless damages to assets occur due to the fire caused by the gas leakage. Not only that but gas leakage and explosion are also very harmful to the climate. Thus, a system to detect gas leakage and preventive measures is of utmost importance. In this project, we design and implement an intelligent IoT prototype to detect gas leakage, and the fire caught by gas leakage. Our goal is to minimize the effect of gas leakage by taking some protective measures. When the gas sensor, detects the gas leakage, the solenoid valve shuts off the gas line, and the exhaust fan starts to run. Again, when the flame sensor detects a fire, the sucker throws the fire extinguisher balls at the fire. The GSM SIM module notifies the user by sending a message to his smartphone. The buzzer sounds when a mishap occurs and the LCD monitor always shows the status of the system. In this way, we have efficiently designed and implemented a low-cost and intelligent gas leak detection and fire suppression system.
Machine Learning Approach to Predict SGPA and CGPA
(2021 International Conference on Artificial Intelligence and Computer Science Technology (ICAICST), IEEE, 2021-07-30) Saifuzzaman, Mohd.; Parvin, Masuma; Jahan, Israt; Moon, Nazmun Nessa; Nur, Fernaz Narin; Shetu, Syeda Farjana
The prediction of SGPA and CGPA is beneficial to university students. Students will easily get an estimate of their final outcome from this project. As a result, the students will be able to brace themselves for a successful outcome. Students pass the day by participating in a variety of events. Students use social media sites such as Facebook, Instagram, and Twitter. They engage in various hobbies such as playing mobile games, listening to music, among others. As a result, they were able to move several times with these tasks. As a result, if a student spends so much time doing any of those things, she will not be able to achieve a successful grade because of the experiment; students can develop a research routine or guideline that they can apply to their other tasks. Additionally, students' behaviors will forecast their outcomes. The Authors will now see machine learning in Python being used all over the place. After that, The Authors created a smart SGPA and CGPA prediction project, as well as the results on students. The findings are predicted using the Nave Byes algorithm. The Nave Byes algorithm is a simple but effective prediction algorithm. It is a machine learning algorithm as well. As a result, students will be given an estimate of their final exam scores. They can prepare them to make a good result by following the routine of the SGPA & CGPA prediction project.
Mobile Device and Social Media Forensic Analysis
(2021 1st International Conference on Emerging Smart Technologies and Applications (eSmarTA), IEEE, 2021-08-23) Saha, Debanjana; Karmakar, Sajal; Nur, Fernaz Narin; Mariam, Asma; Moon, Nazmun Nessa; Ahmed, Akash
This research work has focused on a digital forensic analysis of social media through mobile devices to determine the primary criminal. This proposed system considered the mobile device used by the prime suspect as the main evidence of cybercrime and tried to find out the degree of criminal involvement in terms of probability likelihood. At first, the most critical data elements were obtained, e.g., deleted files and keywords, through the forensic analysis of the mobile device. This will also help in the identification of the main culprits in the investigation of cybercrime. Next, the system classified the criminals in one of three zones based on the analysis of the keywords to determine the level of crime. The system also takes into account the most probable timeframe for the crime. Thus, the proposed system helps to identify the main culprits in investigating cybercrime more efficiently than the traditional approaches. The system is also looking into the most probable timeframe for the crime, for example, it has been observed that most cybercrime happens on the weekends. The proposed system investigates using cookies and the logical image of the device that cyber criminals left behind.
Natural Language Processing Based Advanced Method of Unnecessary Video Detection
(International Journal of Electrical and Computer Engineering, 2021) Moon, Nazmun Nessa; Salehin, Imrus; Parvin, Masuma; Hasan, Md. Mehedi; Talha, Iftakhar Mohammad; Debnath, Susanta Chandra; Nur, Fernaz Narin; Saifuzzaman, Mohd.
In this study we have described the process of identifying unnecessary video using an advanced combined method of natural language processing and machine learning. The system also includes a framework that contains analytics databases and which helps to find statistical accuracy and can detect, accept or reject unnecessary and unethical video content. In our video detection system, we extract text data from video content in two steps, first from video to MPEG-1 audio layer 3 (MP3) and then from MP3 to WAV format. We have used the text part of natural language processing to analyze and prepare the data set. We use both Naive Bayes and logistic regression classification algorithms in this detection system to determine the best accuracy for our system. In our research, our video MP4 data has converted to plain text data using the python advance library function. This brief study discusses the identification of unauthorized, unsocial, unnecessary, unfinished, and malicious videos when using oral video record data. By analyzing our data sets through this advanced model, we can decide which videos should be accepted or rejected for the further actions.