Browsing by Author "Bhuiyan, Touhid"

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A Case Study of SQL Injection Vulnerabilities Assessment of .bd Domain Web Applications
(IEEE Xplore, 2016-06-16) Alam, Delwar; Kabir, Md. Alamgir; Bhuiyan, Touhid; Farah, Tanjila
Web applications or services play an important role in present day to day life. They have impact on the development of both individual and a country. Easy access to services such as online education, banking, reservation, shopping, resources, and information sharing have been proven most efficient for every day life. Various government and private organizations of Bangladesh have started to use web services to support clients. Most of the web applications of Bangladesh is registered with .bd domain and developed using content management system(CMS), various scripting language and SQL or MySQL database.Web applications are popular target for web attackers. However the security issues of the .bd domain web applications are not looked appropriately upon as of yet. One of the most attacked vulnerability of the database driven web applications is SQL injection or SQLi. SQLi through URL and user-input field is extremely high risk in current web based applications. Restricting user access to URL and user input field defies the purpose of web applications. However, the un-restricted user access exposes the vulnerable fields to web attacks. To prevent these exploitation'sit is essential to have knowledge of the vulnerabilities adversaries uses to exploit the web applications. This paper presents an evaluation and analysis of SQLi vulnerabilities present in the existing web applications of .bd domain using black box penetration testing approach. User input based SQLi has been used for evaluation. Full Text Link: http://doi.org/10.1109/CyberSec.2015.23
A Comparative Study on GA-based Scheduling on Cloud Computing
(Scopus, 2020) Rawshan, Lamisha; Rahman, Tasnim; Begum, Afsana; Hossain, Syeda Sumbul; Bhuiyan, Touhid
Cloud computing provides data storage and computing power based on user demand by assigning tasks to virtual resources. To deliver overall improved performance and meet challenges such as availability, resource utilization and reliability in the cloud, appropriate resource scheduling methods are needed. A number of metaheuristic optimization algorithms are used to solve the problem of resource scheduling. This work lists challenges and analyzes previous scheduling methods based on Genetic Algorithm (GA). It classifies the GA-based scheduling methods with respect to many parameters. At last, it presents the scopes of enhancement for future researchers.
A Comprehensive Cotton Leaf Disease Dataset for Enhanced Detection and Classification
(Elsevier, 2024-09-10) Bishshash, Prayma; Nirob, Asraful Sharker; Sarower, Afjal Hossan; Bhuiyan, Touhid; Noor, Sheak Rashed Haider
The creation and use of a comprehensive cotton leaf disease dataset offer significant benefits in agricultural research, precision farming, and disease management. This dataset enables the development of accurate machine learning models for early disease detection, reducing manual inspections and facilitating timely interventions. It serves as a benchmark for testing algorithms and training deep learning models, aiding in automated monitoring and decision support tools in precision agriculture. This leads to targeted interventions, reduced chemical use, and improved crop management. Global collaboration is fostered, contributing to the development of disease-resistant cotton varieties and effective management strategies, ultimately reducing economic losses and promoting sustainable farming. Field surveys conducted from October 2023 to January 2024 ensured meticulous image capture under diverse conditions. The images are categorized into eight classes, representing specific disease manifestations, pests, or environmental stress in cotton plants. The dataset comprises 2137 original images and 7000 augmented images, enhancing deep learning model training. The Inception V3 model demonstrated high performance, with an overall accuracy of 96.03 %. This underscores the dataset's potential in advancing automated disease detection in cotton agriculture.
A Comprehensive Review of Green Computing
(IEEE, 2023-08-01) Paul, Showmick Guha; Saha, Arpa; Arefin, Mohammad Shamsul; Bhuiyan, Touhid; Biswas, Al Amin; Reza, Ahmed Wasif; Alotaibi, Naif M.; Alyami, Salem A.; Moni, Mohammad Ali
Green computing, also called sustainable computing, is the process of developing and optimizing computer chips, systems, networks, and software in such a manner that can maximize efficiency by utilizing energy more efficiently and minimizing the negative environmental influence on the surrounding. The term “green computing” refers to practices that lessen the negative effects of technology on the environment. Due to the improvements in modern technology, various devices, mechanisms, and software have been developed, and lots of studies have been conducted to optimize and increase those technologies’ green computing abilities. Thus, review and summarization of green computing-based studies are required to identify the current advancements, challenges, and future research opportunities. This study reviewed and summarized green computing in each area studies, by exploring green computing’s twelve areas. Current research trends, datasets or testing mechanisms, and the construction or implementation of various technologies to accomplish green computing and sustainable development have been discussed. This study, after conducting a thorough comparison and analysis, provides responses to the proposed state-of-the-art research questions. Furthermore, this study presents the current challenges and future research opportunities with respect to each green computing area. This study will provide organizations, researchers, and institutions conducting research on green computing with insights and ideas. Furthermore, environmental organizations, companies, and government agencies concerned with reducing carbon emissions and energy consumption will also benefit from this review study.
A Comprehensive Review on Big Data for Industries
(IEEE, 2022-12-26) Sarker, Supriya; Arefin, Mohammad Shamsul; Kowsher, Md.; Bhuiyan, Touhid; Kwon, Oh-Jin; Dhar, Pranab Kumar
Technological advancements in large industries like power, minerals, and manufacturing are generating massive data every second. Big data techniques have opened up numerous opportunities to utilize massive datasets in several effective ways to improve the efficacy of related industries. This paper presents a review of big data technologies used in the power, mineral, and manufacturing industries for various purposes. We analyze the meta-data of the collected papers before reviewing and selecting papers by applying selection criteria and paper quality assessment strategy. Then we propose a taxonomy of big data application areas in the power, mineral, and manufacturing industries. We have studied current big data architectures and techniques implemented in industry sectors and have uncovered the big data research gaps in industry sectors. To address the gaps, we point out some relevant research questions and, to answer the questions, we make some future research recommendations that might explore interesting research ideas for building a big data-driven industry. As the careful use of big data benefits every other industry sector; hence, supportive big data frameworks need to be developed to speed up the big data analysis process. Proper multi-dimensional big data assessment is also needed to take into account for serving effective data analysis tasks. Industry automation is also heavily influenced by the proper utilization of big data. While an intelligent agent can make many processes and heavy production loads in the manufacturing industry, it can work in a risky environment such as mines efficiently. To train agents for working in a specific environment big data can be used.
A Content-Based Image Retrieval Semantic Modelfor Shaped and Unshaped Objects
(Researchgate, 2016-02-24) Shamsujjoha, Md; Bhuiyan, Touhid
This paper presents an efficient content based image retrieval scheme for both the shaped and unshaped objects. The local regions of an unshaped image have been classified with respect to the frequency of occurrence. Then the semantic concept is evaluated throughRGB histogram dissimilarity factor, overall dissimilarity factor and regional dissimilarity factor. These dissimilarities cooperativelydetermine the local concept for theunshaped object. In addition, the semantic concept for shaped objects is measured through the normalized color findings, synchronized edge detection, small unnecessary particleremotion, and shape similarity checking. All these measurements mutually rank the shaped objects according to their probability of occurrences. In addition, several algorithms and theoretical explanations of the proposed semantic models have been presented. The corresponding examples and simulations prove that the proposed methods work accurately. The comparative results show that the proposed models have significantly better scalability than the existing approaches. Full Text Link: http://doi.org/10.9790/0661-18124360
A Model of a Feed Forward Multi Layered Neural Network to Recognize Hand Written Bengali Digits
(The 12th International Conference on Artificial Intelligence, 2001-07-15) Bhuiyan, Touhid
This work deals with the recognition of handwritten Bengali digits using feed forward multi layered neural network approach. It proposes an overall guideline of how to construct and train a neural network in order to give it the recognition capability. The paper presents findings of different experiments undertaken and concludes on the quality of recognition. The key aspect of this research is the solution network that successfully recognized handwritten digits with as high accuracy as 96% on casually written digits and 100% accuracy on carefully written digits. It also features two very simple, yet interesting and efficient network structures that were used. The advantage of these features is the small size, which takes smaller storage space and helps to train faster. Another major achievement is the implementation of a new activation function which theoretically reaches ‘zero error’ at some stage of the training. A sophisticated conversion and compression algorithm for digitizing the handwritten images has also been developed and described in the paper.
A New Model for Real-Time Intrusion Prevention Systems for DDoS Attacks
(Daffodil International University, 22-05-10) Ali, Mohammed Nadir Bin; Hossain, Mohamed Emran; Bhuiyan, Touhid; Hoque, Mohammed Shamsul; Karthikeyan, J.
Nowadays the internet has made a momentous impact on our daily life but we are not safe enough in the internet world. Last two decades, network security scholars have shown several innovative and practical solutions to save us from network and internet attacks. Among all the internet threats denial-of-service (DoS) and distributed denial-of-service (DDoS) attacks are considered the most notorious and devastating ones. These attacks are one of the main threats that are a serious security problem for today’s internet. To exhaust the resources of target networks, these attacks are launched by generating a huge amount of network traffic. This study proposes a new model for real-time intrusion prevention systems for DDoS attacks. It is true that creative attackers are continuously developing effective attacking tools and techniques to impose maximum damage due to the rapid technological advancement. The proposed Efficient Detection System of Network Intrusion (EDSONI) model makes use of both the detection and prevention of this malicious activity properly. CICIDS2017 dataset has been applied to this proposed system to experiment with the detection and prevention performance.
A priority based dynamic resource mapping algorithm for load balancing in cloud
(IEEE, 2018-02-15) Sadia, Farzana; Jahan, Nusrat; Rawshan, Lamisha; Jeba, Madina Tul; Bhuiyan, Touhid
Cloud computing is a rising technology which is responsible for supplying of computing resources on the basis of demand, as and when needed. Cloud provides many facilities due to its vast resources such as sharing resources for different purposes. Cloud computing faces many challenges in respect of performance and efficiency. To increase the cloud computing environment's efficiency, Virtual Machines (VM) has been employed for resource provisioning. In this paper, we proposed an algorithm that performs load distribution of workloads among different VM based on priority. This algorithm is proposed in the aim of load balancing of different nodes by considering maximum throughput with minimum execution time. To achieve that, the VM are sorted according to their processing powers and job requests are assigned to VM based on their instruction numbers and priorities. The proposed algorithm is experimented using CloudSim simulator and the results demonstrated that the performance of the algorithm is better than other conventional algorithms.
A Review of Trust in Online Social Networks to Explore New Research Agenda
(The 11th International Conference on Internet Computing, 2010) Bhuiyan, Touhid; Xu, Yue; Josang, Audun
Trust has become important topic of research in many fields including sociology, psychology, philosophy, economics, business, law and of course in IT. In recent years, there is a dramatic growth in number and popularity of online social networks. There are many networks available with more than 100 million registered users such as Facebook, MySpace, QZone, Windows Live Spaces etc. People may connect, discover and share by using these online social networks. The exponential growth of online communities in the area of social networks attracts the attention of the researchers about the importance of managing trust in online environment. The major challenge of the current online user is to identify the trustworthiness of the agents they are communicating. Trust can be calculated between two unknown agents who are not directly connected in a trust network rather connected somehow through the network. In this paper, we have reviewed the state-of-the-art research work on trust in online social network and discussed about the relevant research agenda
A Review on Automatic Speech Emotion Recognition with an Experiment Using Multilayer Perceptron Classifier
(Springer, 2020-11-28) Sardar, Abdullah Al Mamun; Islam, Md. Sanzidul; Bhuiyan, Touhid
Human–machine interaction is becoming popular day by day; to interact with machine, speech emotion recognition is as important as human to human interaction. In this research, we demonstrate a speech emotion recognition system which takes speech as input and classify emotions that the speech contains. We choose multilayer perceptron (MLP) classifier to do this task. Features that we have extracted from speech are mel-frequency cepstral coefficients (MFCC), chroma and mel-spectrogram frequency. RADVES dataset has been used and we have got 73% accuracy.
A Review on Automatic Speech Emotion Recognition with an Experiment Using Multilayer Perceptron Classifier
(Springer, 2020-11-28) Sardar, Abdullah Al Mamun; Islam, Md. Sanzidul; Bhuiyan, Touhid
Human–machine interaction is becoming popular day by day; to interact with machine, speech emotion recognition is as important as human to human interaction. In this research, we demonstrate a speech emotion recognition system which takes speech as input and classify emotions that the speech contains. We choose multilayer perceptron (MLP) classifier to do this task. Features that we have extracted from speech are mel-frequency cepstral coefficients (MFCC), chroma and mel-spectrogram frequency. RADVES dataset has been used and we have got 73% accuracy.
A Review on Automatic Speech Emotion Recognition with an Experiment Using Multilayer Perceptron Classifier
(Scopus, 2021) Sardar, Abdullah Al Mamun; Islam, Md. Sanzidul; Bhuiyan, Touhid
https://link.springer.com/chapter/10.1007/978-981-15-7394-1_36Human–machine interaction is becoming popular day by day; to interact with machine, speech emotion recognition is as important as human to human interaction. In this research, we demonstrate a speech emotion recognition system which takes speech as input and classify emotions that the speech contains. We choose multilayer perceptron (MLP) classifier to do this task. Features that we have extracted from speech are mel-frequency cepstral coefficients (MFCC), chroma and mel-spectrogram frequency. RADVES dataset has been used and we have got 73% accuracy.
A Review on the Impacts of Social Media on the Mental Health
(IEEE, 2023-12-20) Tapu, Md. Abu Bakar Siddiq; Akash, Rashik Shahriar; Fahim, Hafiz Al; Jarin, Tanin Mohammad; Bhuiyan, Touhid; Reza, Ahmed Wasif; Arefin, Mohammad Shamsul
There are numerous effects of social media use on people’s daily lives. Every day we are connected a lot of time with social media. As a result, our brains become unbalanced, and we feel a lot of illness in our bodies. As a result, the primary objective of this analysis is to provide information on how social media affects its users. Nineteen studies were included in this paper regarding the main purpose. We categorized the papers into four types, Status and Post based research, Research paper and Database analysis-based research, Survey based research, and disease-based research. After a comprehensive analysis of the available literature, we have concluded that utilizing the social side has an effect on our mental health and actions. The results highlighted a number of critical aspects, such as the varied approaches to determining the influence of social media, the limitations of the studies, and our thoughts on what should be enhanced.
A Risk Based Analysis on Linux Hosted E-Commerce Sites in Bangladesh
(Springer, 2020-07-30) Royel, Rejaul Islam; Sharif, Md. Hasan; Risha, Rafika; Bhuiyan, Touhid; Hassan, Md. Maruf; Hassan, Md. Sharif
E-commerce plays a significant role to grow its business globally by satisfying the modern consumer’s expectations. Without the help of Operating System (OS), e-commerce applications cannot be operated as well as broadcasted on the web. It is evident after analyzing this study that web administrators of the business are sometimes being careless, in some cases unaware about the risk of cyber-attack due the lack of vulnerability research on their OS. Therefore, a good number of the e-commerce applications are faced different type of OS exploitations through different types of attack e.g. denial of service, bypass, DECOVF, etc. that breaches the OS’s confidentiality, integrity and availability. In this paper, we analyzed 140 e-commerce sites servers’ information and its related 1138 vulnerabilities information to examine the risks and risky versions of the OS in e-commerce business. The probabilities of vulnerability are calculated using Orange 3 and feature selection operation has been performed using Weka through IBM statistical tool SPSS. This study identifies few versions of Ubuntu that are found in critical status in terms of risk position.
A Study on Dengue Fever in Bangladesh:
(5th International Conference on Intelligent Computing and Control Systems (ICICCS), IEEE, 2021-05-26) Islam, Md. Sanzidul; Khushbu, Sharun Akter; Rabby, Akm Shahariar Azad; Bhuiyan, Touhid
The “2019 Dengue Outbreak” was a nationwide pandemic situation in Bangladesh, particularly in Dhaka city. About 179 people died and 101,354 confirmed dengue cases were found all over the country. The developing countries like Bangladesh have some limitations in the medical sector and many people don't get proper treatment in time. Henceforth, this research work has attempted to predict the chances to get infected with dengue fever from some external behaviors, like-fever, pain, sitophobia, headache etc. This article has demonstrated a model to predict the probability of dengue fever before taking the pathological test. So, the suspective patient may get some initial diagnosis by giving their anatomical symptoms as input and further this will decrease the dependency on the pathological test for acquiring the primary treatment. Different machine learning models are used to predict the probability and an accuracy near to 100% has been achieved finally.
A survey on the relationship between trust and interest similarity in online social networks
(QUT ePrints, 2011-04-26) Bhuiyan, Touhid
A remarkable growth in quantity and popularity of online social networks has been observed in recent years. There is a good number of online social networks exists which have over 100 million registered users. Many of these popular social networks offer automated recommendations to their users. This automated recommendations are normally generated using collaborative filtering systems based on the past ratings or opinions of the similar users. Alternatively, trust among the users in the network also can be used to find the neighbors while making recommendations. To obtain the optimum result, there must be a positive correlation exists between trust and interest similarity. Though the positive relations between trust and interest similarity are assumed and adopted by many researchers; no survey work on real life people’s opinion to support this hypothesis is found. In this paper, we have reviewed the state-of-the-art research work on trust in online social networks and have presented the result of the survey on the relationship between trust and interest similarity. Our result supports the assumed hypothesis of positive relationship between the trust and interest similarity of the users. Full Text Link: http://doi.org/10.4304/jetwi.2.4.291-299
Air Pollution or Gases Behind Toxicity for People Awareness
(Scopus, 2024) Ferdous, Munira; Mojumdar, Mayen Uddin; Chakraborty, Narayan Ranjan; Bhuiyan, Touhid
A report by world quality showed Bangladesh was ranked the second most air polluted country in 2019. The PM2.5 intensity on typical was 83.3 µg per cubic meter. Forests and trees are being cut down for creating buildings. Deforestation impacts badly the air quality of Bangladesh. Another reason for air pollution is overpopulation. Dhaka is one of the most overpopulated cities in Bangladesh. Air quality in Dhaka is 200 micrograms per cubic meter. In this research, we’ve collected some of the most common and dangerous gases, their toxicity, and their effect on the human body. For a healthy atmosphere air quality should be in the range of Nitrogen (N2) 78.084, Oxygen (O2) it is 20.947, Argon (Ar) − 0.934, and Carbon dioxide (CO2) 0.0314 and the others. If the percentage is high then the air becomes polluted. In this paper, we show the main gases in the Dhaka area. We collected the data for CO2, CO, SO2, O3, NH2, and PM2.5 in Air for awarding about the pollution. The objective is to create public awareness through mobile applications or show air pollution’s impact on everyday life. This paper suggested working with govt. or non-govt. environmental organizations to carry out necessary steps.
An Analysis of Trust Transitivity Taking Base Rate into Account
(IEEE Xplore, 2009-11-10) Bhuiyan, Touhid; Josang, Audun; Xu, Yue
Trust transitivity, as trust itself, is a human mental phenomenon, so there is no such thing as objective transitivity, and trust transitivity therefore lends itself to different interpretations. Trust transitivity and trust fusion both are important elements in computational trust. This paper analyses the parameter dependence problem in trust transitivity and proposes some definitions considering the effects of base rate. In addition, it also proposes belief functions based on subjective logic to analyze trust transitivity of three specified cases with sensitive and insensitive based rate. Then it presents a quantitative analysis of the issue of exaggerated beliefs in mass hysteria based on subjective logic. Full Text Link: http://doi.org/10.1109/UIC-ATC.2009.64
An Efficient Multi-sensing and GSM Equipped Fire Monitoring System
(MATEC, 2017-12-11) Fahad Bin Zamal, Md.; Sayed, Shehrin; Bhuiyan, Touhid; Rahman, Mostafijur
The principal goal of fire monitoring system is to react promptly to a fire and not to misleading particulate signatures produced by nuisance sources. In this paper we proposed a system that not only able to detect and prevent fire at early stages but also capable of interact with surround environment. Recent researches lead us to detect fire by light and heat sensor, image processing, smoke detection mechanism but failed to integrate those in one The advancement on fire detection technologies has been significant over the last few decade due to rapid progress in communication technologies, advances in sensing devices and greater understanding of fire physics. But lack of intelligence among fire monitoring system often failed to make an impact on fire incidents. Our proposed fire monitoring system is incorporated in such a way that can communicate with environment by itself through the help of GSM Modem. Here we introduce an intelligent and advance fire monitoring system that can communicate by itself with fire station and can detect fire at its early stage and extinguish it in the shortest time subject to a few effective factors. Full Text Link: http://doi.org/10.1051/matecconf/201714001003