Browsing by Author "Talha, Iftakhar Mohammad"

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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 Artificial Intelligence Based Rainfall Prediction Using LSTM and Neural Network
(IEEE, 2020-12) Salehin, Imrus; Talha, Iftakhar Mohammad; Hasan, Md. Mehedi; Dip, Sadia Tamim; Saifuzzaman, Mohd.; Moon, Nazmun Nessa
The most difficult task of meteorology is to predict rainfall. In our study, we proposed an amount of rainfall prediction model that can be easily determined using artificial intelligence and LSTM techniques. This is an advanced method to find out the rainfall. The deep learning approach is most valuable for this type of method implementation and its accuracy finds out. A long short-term memory algorithm is applied to memory sequence data measurement and calculate previous data very fast and create the best prediction. The people of this country are mostly dependent on agriculture so that this prediction system is very necessary. Timely rainfall assessment will increase crop yields and reduce costs in agriculture. Considering all these factors, we have created our model which will help us to determine the amount of rainfall. We have collected data from 6 regions to do this. To predict, we have taken 6 parameters (temperature, dew point, humidity, wind pressure, wind speed, and wind direction). After analyzing all our data, we got 76% accuracy in our work. We also focus on a vast dataset in long time weather for the better result.
Analysis of Student Sentiment During Video Class with Multilayer Deep Learning Approach
(Daffodil International University, 2022-08-08) Salehin, Imrus; Moon, Nazmun Nessa; Talha, Iftakhar Mohammad; Hasan, Md. Mehedi; Nur, Farnaz Narin; Hakim, Md. Azizul; Haque, Farhan Al
The modern education system is an essential part of the rise of technology. The E-learning education system is not just an experimental system; it is a vital learning system for the whole world over the last few months. In our research, we have developed our learning method in a more effective and modern way for students and teachers. For significant implementation, we are implementing convolutions neural networks and advanced data classifiers. The expression and mood analysis of a student during the onlineclass is the main focus of our study. For output measure, we divide the final output result as attentive, inattentive, understand, and neutral. Showing the output in real-time online class and for sensory analysis, we have used support vector machine(SVM)and OpenCV. The level of 5*4 neural network is created for this work. An advanced learning medium is proposed through our study. Teachers can monitor the live class and different feelings of a student during the class period through this system.
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.
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.
Predicting the Depression Level of Excessive Use of Mobile Phone
(Daffodil International University, 2021-05-31) Salehin, Imrus; Talha, Iftakhar Mohammad
In this research titled “Predicting the Depression Level of Excessive Use of Mobile Phone: Using Machine Learning Algorithm” which is applied advanced machine learning and regression analysis to find out the depression level. We have done the whole work in the research area of medical science and information technology and also built up a collaboration. In this study, we are focusing on the strength of the algorithm and also calculate the accuracy with python programming. The result expresses that smart mobile device changing the human brain day by day if spend more time around 8 to 12 hours a day. At last, we observed that a man or woman slowly going through a depression for the impact of the excessive mobile operates. In our study, we have used multiple classification algorithms to find out depression level such as Probability, Decision Tree, Random Forest, Linear Regression and SVM (Support Vector Machine). For the accuracy of our work, we have used four types of algorithms to find the optimal ratio and percentage.