Mohd. Saifuzzaman

Bio: Mohd. Saifuzzaman is an academic researcher from Daffodil International University. The author has contributed to research in topics: Support vector machine & Naive Bayes classifier. The author has an hindex of 7, co-authored 32 publications receiving 177 citations. Previous affiliations of Mohd. Saifuzzaman include Jahangirnagar University.

IoT based street lighting and traffic management system

Abstract: 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

A Survey of Botnet in Cyber Security

Abstract: Botnets is one of the most critical cyber security threats that confronted by organizations day by day. Botnet has used distinctive strategies, topologies and communication protocol in specific levels in their life cycle. Identifying of botnets has emerged as a very challenging topic, particularly for the reason that they can improve their method at any time. Nowadays, most of the recent botnet detection techniques cannot locate modern botnets in an early stage. Mainly, botnets follow command and control infrastructure. Nowadays, botnet is an interesting and very vital research topic for researchers in our cyber security. This Paper represents a completely comprehensive evaluation that extensively discusses the botnet problem; this survey classifies all of the possible botnet detection methods, also summarizes previously published studies and recent works.

Smart Security for an Organization based on IoT

Abstract: This research describes a low cost and flexible security system which is basically based on Arduino with necessary interface to enable Internet and the control of power through Global System for Mobile Communication (GSM) & Bluetooth module (HC-05). This paper consumed more real life interactions along with embedded software solutions. In this project a password is set for the access of all sensors, for this we use LCD Display and Keypad. Motion sensor, gas module, reed sensor, laser sensor, all the sensors are used to detect theft and unwanted occurrences. Control Panel Interface of Web Server and android voice control both are created to control of all lights of the organization and for the purpose of power savings. The proposed system requires minimum human intervention to control the system. This research ensures the safety of organization from unwanted occurrence and theft. The main contribution of this paper is that it not only helps to ensure the security of an organization but also energy efficient and time saving.

An Artificial Intelligence Based Rainfall Prediction Using LSTM and Neural Network

Abstract: 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.

Impactful E-Learning Framework: A New Hybrid Form of Education

Abstract: An e-learning model is helpful to enhance online connectivity and learning. This study aims to propose and develop an efficient e-learning framework to strengthen the online learning environment. The single consumer has to play a part in an e-learning system. There is a user-friendly interface for the proposed model. Architecture classifies into various categories. The work shows different parts of a successive model of e-learning and even each section's working process. This model and the structure can take all types of students into account. Since it is possible, there is a solution to this if the system grows using this method. Therefore, for teachers, it would be helpful too. The e-learning model facilitates self-learning for learners. Via this method, teachers can also do any operation. Using this form of system, they can also learn computer abilities and upgrade interactive skills.

Multimedia Internet of Things: A Comprehensive Survey

Abstract: The immense increase in multimedia-on-demand traffic that refers to audio, video, and images, has drastically shifted the vision of the Internet of Things (IoT) from scalar to Multimedia Internet of Things (M-IoT). IoT devices are constrained in terms of energy, computing, size, and storage memory. Delay-sensitive and bandwidth-hungry multimedia applications over constrained IoT networks require revision of IoT architecture for M-IoT. This paper provides a comprehensive survey of M-IoT with an emphasis on architecture, protocols, and applications. This article starts by providing a horizontal overview of the IoT. Then, we discuss the issues considering the characteristics of multimedia and provide a summary of related M-IoT architectures. Various multimedia applications supported by IoT are surveyed, and numerous use cases related to road traffic management, security, industry, and health are illustrated to show how different M-IoT applications are revolutionizing human life. We explore the importance of Quality-of-Experience (QoE) and Quality-of-Service (QoS) for multimedia transmission over IoT. Moreover, we explore the limitations of IoT for multimedia computing and present the relationship between the M-IoT and emerging technologies including event processing, feature extraction, cloud computing, Fog/Edge computing and Software-Defined-Networks (SDNs). We also present the need for better routing and Physical-Medium Access Control (PHY-MAC) protocols for M-IoT. Finally, we present a detailed discussion on the open research issues and several potential research areas related to emerging multimedia communication in IoT.

Product Market Competition and Firm Performance: Business Survival Through Innovation and Entrepreneurial Orientation Amid COVID-19 Financial Crisis

Abstract: The product market competition has become a global challenge for business organizations in the challenging and competitive market environment in the influx of the COVID-19 outbreak. The influence of products competition on organizational performance in developed economies has gained scholars’ attention, and numerous studies explored its impacts on business profitability. The existing studies designate mixed findings between the linkage of CSR practices and Chinese business firms’ healthier performance in emerging economies; however, the current global crisis due to the coronavirus has made product market completion fierce, which ultimately affects business firms’ performance. This study focuses on this logical global challenge, investigates the rationale, and examines product-market completion impact on firms’ performance operating in the Chinese markets. The study collected data from the annual reports of Chinese business organizations with A-share listing and registered with the database of China Stock Markets and Accounting Research (CSMAR). The study employed a Generalized Method of Moment technique and investigated the connection between product market competition and Chinese firm performance. The empirical analysis of this study highlights the conclusion that market competition positively and significantly affected business firms’ performance. This study specified that product market competition play a dynamic and indispensable role in achieving healthier firm performance in the Chinese markets. This study provides valuable insights on practical implications and future research directions for the scholars to draw interesting results with new study models.

Multi-View Deep Network: A Deep Model Based on Learning Features From Heterogeneous Neural Networks for Sentiment Analysis

Abstract: By the development of social media, sentiment analysis has changed to one of the most remarkable research topics in the field of natural language processing which tries to dig information from textual data containing users’ opinions or attitudes toward a particular topic. In this regard, deep neural networks have emerged as promising techniques that have been extensively used for this aim in recent years and obtained significant results. Considering the fact that deep neural networks can automatically extract features from data, it can be claimed that intermediate representations extracted from these networks can be also used as appropriate features. While different deep neural networks are able to extract various types of features due to their distinct structures, we decided to combine features extracted from heterogeneous neural networks using multi-view classifiers to enhance the overall performance of document-level sentiment analysis by considering the correlation between them. The proposed multi-view deep network makes use of intermediate features extracted from convolutional and recursive neural networks to perform classification. Based on the results of the experiments, the proposed multi-view deep network not only outperforms single-view deep neural networks but also has superior efficiency and generalization performance.

IoT based street lighting and traffic management system

Abstract: 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

Comparison of Naive Bayes and SVM Algorithm based on Sentiment Analysis Using Review Dataset

Abstract: Now a day's sentiment analysis is the most used research topic The sentiment analysis result is based on different investigation for example politics, terrorism, economy, international affairs, movies, fashion, justice, humanity Social media are the main resource for collecting people's opinion and their sentiment about a different trending topic People use many abusing words in social media to express their emotion Using sentiment analysis, we will build a platform where one can easily identify the opinions are either positive or negative or neutral This research paper will contain supervised learning which is under the machine learning approach We run an experiment on different queries from humanity to terrorism and find out an interesting result First of all, we have preprocessed the dataset to convert unstructured airline review into structured review form After that, we convert structured review into a numerical value We have to preprocess the data before using it Stop word removal, @ removal, Hashtag removal, POS tagging, calculating sentiment score have done in preprocessing part Then an algorithm has been applied to classify the opinion as either it is positive or negative In this research paper, we will briefly discuss supervised machine learning Support vector machine as well as Naive Bayes algorithm and compares their overall accuracy, precession, recall value The result shows that in the case of airline reviews Support vector machine gave way better result than Naive Bayes algorithm