Browsing by Author "Allayear, Shaikh Muhammad"

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A Cloud of Things (CoT) Approach for Monitoring Product Purchase and Price Hike
(Lecture Notes in Networks and Systems, Springer, 2020-05-15) Sadeq, Muhammad Jafar; Kabir, S. Rayhan; Haque, Rafita; Ferdaws, Jannatul; Akhtaruzzaman, Md.; Forhat, Rokeya; Allayear, Shaikh Muhammad
Price hike is common and one of the major issues all over the world. The prices of daily necessities are increasing day by day. Many shops, restaurants and transport systems are charging extra price from the customers over the products’ expected or maximum retail price. Moreover, in special occasions, such as festivals, the sellers take high price from customers. Furthermore, unauthorized VAT or other taxes are charged on products on which such taxes are exempted by the government. This study proposes a Cloud of Things (CoT)-based model for monitoring the products’ price during transactions, where a cloud server maintains historical pricing data and maximum retail prices. Two algorithms run on the server based on live invoice data from sellers and customer feedback to detect unfair price hike.
A computational technique for intelligent computers to learn and identify the human's relative directions
(IEEE Xplore, 2018-06-21) Kabir, S. Rayhan; Allayear, Shaikh Muhammad; Alam, Mirza Mohtashim; Munna, Md Tahsir Ahmed
The most broadly perceived relative directions are right, left, up, down, backward and forward. This research paper presents a new computational technique to learn human's relative directions, where one intelligent computer can learn any human's right, left, up, down, backward and forward or different relative directions. The present paper portrays models describing the essential structures of relative direction learning process between human and intelligent machine. We developed two proficient algorithms for solving this approach. In our experiment we propose Human Relative Direction Learning (HRDL) algorithm for learning human's relative directions and Human Direction Identification (HDI) algorithm for tracking any human position and identity human's relative directions from different direction points. Full Text Link: http://doi.org/10.1109/ISS1.2017.8389336
A Distributed Framework for Predictive Analytics Using Big Data and MapReduce Parallel Programming
(HINDAWI, 2023-02-01) Natesan, P.; Sathishkumar, V. E.; Mathivanan, Sandeep Kumar; Jayagopal, Prabhu; Allayear, Shaikh Muhammad
With the advancement of Internet technologies and the rapid increase of World Wide Web applications, there has been tremendous growth in the volume of digital data. This takes the digital world into a new era of big data. Various existing data processing technologies are not consistent and scalable in handling the complexity as well as the large-size datasets. Recently, there are many distributed data processing, and programming models have been proposed and implemented to handle big data applications. The open-source-implemented MapReduce programming model in Apache Hadoop is the foremost model for data exhaustive and also computational-intensive applications due to its inherent characteristics of scalability, fault tolerance, and simplicity. In this research article, a new approach for the prediction of target labels in big data applications is developed using a multiple linear regression algorithm and MapReduce programming model, named as MR-MLR. This approach promises optimum values for MAE, RMSE, and determination coefficient (R2) and thus shows its effectiveness in predictions in big data applications.
A Machine Learning Approach to Analyze and Reduce Features to a Significant Number for Employee’s Turn Over Prediction Model
(Scopus, 2020) Alam, Mirza Mohtashim; Mohiuddin, Karishma; Islam, Md. Kabirul; Hassan, Mehedi; Hoque, Md. Arshad-Ul; Allayear, Shaikh Muhammad
Turnover of employee considers as one of the major issue that every company faces. Especially, if the employee has advance skills at his/her working field, then the company faces great loss during that period. To find out the most dominant reasons of employee attrition, we approach by determining features and using machine learning algorithms where features have been processed and reduced beforehand. We have proposed a new model where particular attributes of employee turnover have been selected and adjusted accordingly. In first phase of our reduction method, Sequential Backward Selection Algorithm (SBS) has been used to reduce the features from a higher number to a relatively smaller significant number. After that Chi2 and Random Forest importance algorithm have been used together for the second phase of reduction to determine the common important features by both of the algorithms which can be considered as the foremost features that lead to employee turnover. Our two steps feature selection technique confirms that there are mainly three features that are responsible for employee’s departure. Later, these selected minimal features have been tested with state of the art algorithms of machine learning, such as Decision Tree, Random Forest, Support Vector Machine, Multi-layer Perceptron (MLP), K-Nearest Neighbor (kNN) and Gaussian Naïve Bayes. Lastly, the test result has been visualized by 3D representation to learn the features that are precisely involved for the employee’s turnover.
A Machine Learning Approach to Analyze and Reduce Features to a Significant Number for Employee’s Turn Over Prediction Model
(Springer Nature, 2018-11-02) Alam, Mirza Mohtashim; Mohiuddin, Karishma; Islam, Md. Kabirul; Hassan, Mehedi; Hoque, Md. Arshad-Ul; Allayear, Shaikh Muhammad
Turnover of employee considers as one of the major issue that every company faces. Especially, if the employee has advance skills at his/her working field, then the company faces great loss during that period. To find out the most dominant reasons of employee attrition, we approach by determining features and using machine learning algorithms where features have been processed and reduced beforehand. We have proposed a new model where particular attributes of employee turnover have been selected and adjusted accordingly. In first phase of our reduction method, Sequential Backward Selection Algorithm has been used to reduce the features from a higher number to a relatively smaller significant number. After that Chi2 and Random Forest importance algorithm have been used together for the second phase of reduction to determine the common important features by both of the algorithms which can be considered as the foremost features that lead to employee turnover. Our two steps feature selection technique confirms that there are mainly three features that are responsible for employee’s departure. Later, these selected minimal features have been tested with state of the art algorithms of machine learning, such as Decision Tree, Random Forest, Support Vector Machine, Multi-layer Perceptron (MLP), K-Nearest Neighbor and Gaussian Naïve Bayes. Lastly, the test result has been visualized by 3D representation to learn the features that are precisely involved for the employee’s turnover.
A Sentiment Analysis Based Approach for Understanding the User Satisfaction on Android Application
(Springer, 2020-01-09) Rahman, Md. Mahfuzur; Motiur Rahman, Sheikh Shah Mohammad; Allayear, Shaikh Muhammad; Patwary, Md. Fazlul Karim; Munna, Md. Tahsir Ahmed
The consistency of user satisfaction on mobile application has been more competitive because of the rapid growth of multi-featured applications. The analysis of user reviews or opinions can play a major role to understand the user’s emotions or demands. Several approaches in different areas of sentiment analysis have been proposed recently. The main objective of this work is to assist the developers in identifying the user’s opinion on their apps whether positive or negative. A sentiment analysis based approach has been proposed in this paper. NLP-based techniques Bags-of-Words, N-Gram, and TF-IDF along with Machine Learning Classifiers, namely, KNN, Random Forest (RF), SVM, Decision Tree, Naive Byes have been used to determine and generate a well-fitted model. It’s been found that RF provides 87.1% accuracy, 91.4% precision, 81.8% recall, 86.3% F1-Score. 88.9% of accuracy, 90.8% of precision, 86.4% of recall, and 88.5% of F1-Score are obtained from SVM.
A Smart Embedded System Model for the AC Automation with Temperature Prediction
(Scopus, 2020) Shamrat, F. M. Javed Mehedi; Allayear, Shaikh Muhammad; Alam, Md. Farhad; Jabiullah, Md. Ismail; Ahmed, Razu
A model of an automated temperature prediction on smart AC system for a room has been designed, developed and implemented with an embedded system. In a room, temperature of object (like human being) with the environment is detected, identified and analyzed, with an ideal temperature. Based on data, a mathematical formula can be derived and an algorithm has been formed by using the mathematical formula of the predicted temperature data and the values of the two sensors, where sensors are used for object temperature detection and the AC perform automatically turned on or turned off. Python programming language with its default library has been used to code for the successful implementation of the algorithm. This proposed embedded system can be implemented in any smart AC room where anyone can utilize the AC system automatically switched on/off with the predicted temperature. Exploit this embedded system in all over the places including for disabled peoples, personal room, conference room, hall room, classroom and transports, where manually control of Air conditioner is not feasible.
Adaptation Mechanism of iSCSI Protocol for NAS Storage Solution in Wireless Environment
(Springer, 2012) Allayear, Shaikh Muhammad; Park, Sung Soon; Ripon, Shamim H.; Kim, Gyeong Hun
The continued growth of both mobile appliances and wireless Internet technologies is bringing a new telecommunication revolution and it has extended the demand of various services with mobile appliances. However, in wireless environment the availability of mass storage is limited due to their limited size and weight. Although the problem can be alleviated by iSCSI (Internet Small Computer Interface) based Network Storage System, it has drawbacks in high availability and performance. To address this issue, this paper presents an architecture to adapt iSCSI protocol with traditional NAS (Network Attached Storage) cluster system with an error recovery method. To realize the access to a NAS storage system, our experiments suggest the optimal values for various parameters. The test cases show that the best values of the parameters are not always the default values specified in the iSCSI standard. Full Text Link: https://doi.org/10.1007/978-3-642-33050-6_12
An Evaluated iSCSI Protocol for an Embedded Multi-agent Based Health Care Service
(IEEE Computer Society, 2008-12-15) Allayear, Shaikh Muhammad; Park, Sung Soon
In this paper we have proposed a parameter-evaluated iSCSI protocol that automates the multi-agent coordination on the resource-constrained devices for the healthcare service. The coordination between the resource control devices and automated agents are maintained by a two-way handshaking mode iSCSI with some evaluated parameters. Full Text Link: http://doi.ieeecomputersociety.org/10.1109/FGCN.2008.210
Analysis on COVID-19 Infection Spread Rate during Relief Schemes Using Graph Theory and Deep Learning
(Daffodil International University, 2022-08-12) Palanivinayagam, Ashokkumar; Panneerselvam, Ramesh Kumar; Kumar, P. J.; Rajadurai, Hariharan; Maheshwari, V.; Allayear, Shaikh Muhammad
The novel coronavirus 2019 (COVID-19) disease is a pandemic which affects thousands of people throughout the world. It has rapidly spread throughout India since the first case in India was reported on 30 January 2020. The official report says that totally 4, 11,773 cases are positive, 2, 28,307 recovered, and the country reported 12,948 deaths as of 21 June 2020. Vaccination is the only way to prevent the spreading of COVID-19 disease. Due to various reasons, there is vaccine hesitancy across many people. Hence, the Indian government has the solution to avoid the spread of the disease by instructing their citizens to maintain social distancing, wearing masks, avoiding crowds, and cleaning your hands. Moreover, lots of poverty cases are reported due to social distancing, and hence, both the center government and the respective state governments decide to issue relief funds to all its citizens. The government is unable to maintain social distancing during the relief schemes as the population is huge and available support staffs are less. In this paper, the proposed algorithm makes use of graph theory to schedule the timing of the relief funds so that with the available support staff, the government would able to implement its relief scheme while maintaining social distancing. Furthermore, we have used LSTM deep learning model to predict the spread rate and analyze the daily positive COVID cases.
Analysis on the Bus Arrival Time Prediction Model for Human-Centric Services Using Data Mining Techniques
(Daffodil International University, 2022-09-26) Shanthi, N.; V. E., Sathishkumar; Babu, Upendra; Karthikeyan, P.; Rajendran, Sukumar; Allayear, Shaikh Muhammad
The human-computer interaction has become inevitable in digital world. HCI helps humans to incorporate technology to resolve even their day-to-day problems. The main objective of the paper is to utilize HCI in Intelligent Transportation Systems. In India, the most common and convenient mode of transportation is the buses. Every state government provides the bus transportation facility to all routes at an affordable cost. The main difficulty faced by the passengers (humans) is lack of information about bus numbers available for the particular route and Estimated Time of Arrival (ETA) of the buses. There may be different reasons for the bus delay. These include heavy traffic, breakdowns, and bad weather conditions. The passengers waiting in the bus stops are neither aware of the delay nor the bus arrival time. These issues can be resolved by providing an HCI-based web/mobile application for the passengers to track their bus locations in real time. They can also check the Estimated Time of Arrival (ETA) of a particular bus, calculated using machine learning techniques by considering the impacts of environmental dynamics, and other factors like traffic density and weather conditions and track their bus locations in real time. This can be achieved by developing a real-time bus management system for the benefit of passengers, bus drivers, and bus managers. This system can effectively address the problems related to bus timing transparency and arrival time forecasting. The buses are equipped with real-time vehicle tracking module containing Raspberry Pi, GPS, and GSM. The traffic density in the current location of the bus and weather data are some of the factors used for the ETA prediction using the Support Vector Regression algorithm. The model showed RMSE of 27 seconds when tested. The model is performing well when compared with other models.
Anatomical Analysis between Two Languages Alphabets
(Springer, 2020-07) Rahman, Mizanur; Uddin, Md. Salah; Hasan, Md. Samaun; Ghosh, Apurba; Boby, Sadia Afrin; Ahmed, Arif; Sadiur Rahman, Shah Muhammad; Allayear, Shaikh Muhammad
We have many different types of languages and alphabets in the world, but the alphabet of each language has its own unique design. We tried to bridge between the two countries/languages by reflecting the differences in the alphabet in another language by highlighting its distinctive features. By using the two most commonly used languages like English & Arabic. Here the transformation process of flavors one into another language has been introduced by replication. The alphabet has the same characteristics in the alphabet of the two languages and reflected in other languages. In this study, we have completed the task of transforming the character flavor of English and Arabic as well as the Arabic language in English. The process is finding characteristic patterns and connecting to any other language alphabet to achieving new tastes.
Augmented Reality and Virtual Reality: Prospective Technology of Bangladeshi Journalism
(Scopus, 2024-05-03) Allayear, Shaikh Muhammad; Hasan, Kazi Jahid
In recent years, journalists have experimented with 4IR technologies like virtual reality (VR) and augmented reality (AR), and some media organisations, including the BBC, CNN and The New York Times, have set up departments, particularly for its development. In addition to placing the audience in the heart of the action and enhancing reliability, the use of AR and VR for journalistic production calls for innovative skills, responsibilities to the viewers and distribution issues. In this chapter, we have focused on the current state of journalism in Bangladesh in the digital age. As a part of the presentation journalistic aspects and issues associated with using AR and VR in Bangalesh, recommendations for future research are also included.
Capable of Classifying the Tuples With Wireless Attacks Detection Using Machine Learning
(Daffodil International University, 2023-03-22) Islam, Tariqul; Allayear, Shaikh Muhammad
A wireless attack is a malicious action against wireless systems and wireless networks. In the last decade of years, wireless attacks are increasing day by day and it is now a very big problem for modern wireless communication systems. In this paper, the author’s used the Aegean Wi-Fi Intrusion Dataset (AWID3). There are two versions of this dataset, one is over 200 million tuples of full data and one is 1.8 million tuples of reduced data. As our dataset has millions of tuples and over 100 columns, it is easy to become overwhelmed because of its size. The authors used the reduced version and predicted if an attack was one of four types using the k-nearest-neighbors classifier. All of the attack types we used had a distribution that highly favored the non-attack class. Our best results were for the attack “arp” type where we attained the best accuracy with recall. The author’s primary goal of the paper was to attain the highest accuracy possible when creating a model that is capable of classifying the 4 attack types and detecting and classifying wireless attacks using Machine Learning models on the AWID3 dataset. One of the goals that supported this main objective was determining a way to avoid the curse of dimensionality.
Computational and Mathematical Methods in Medicine Glioma Brain Tumor Detection and Classification Using Convolutional Neural Network
(Daffodil International University, 2022-10-14) Saravanan, S.; Kumar, Vinoth; Sarveshwaran, Velliangiri; Indirajithu, Alagiri; Elangovan, D.; Allayear, Shaikh Muhammad
The classification of the brain tumor image is playing a vital role in the medical image domain, and it directly assists the clinicians to understand the severity and to take an appropriate solution. The magnetic resonance imaging tool is used to analyze the brain tissues and to examine the different portion of brain circumstance. We propose the convolutional neural network database learning along with neighboring network limitation (CDBLNL) technique for brain tumor image classification in medical image processing domain. The proposed system architecture is constructed with multilayer-based metadata learning, and they have integrated with CNN layer to deliver the accurate information. The metadata-based vector encoding is used, and the type of coding estimation for extra dimension is known as sparse. In order to maintain the supervised data in terms of geometric format, the atoms of neighboring limitation are built based on a well-structured -neighbored network. The resultant of the proposed system is considerably strong and subjective for classification. The proposed system used two different datasets, such as BRATS and REMBRANDT, and the proposed brain MRI classification technique outcome is more efficient than the other existing techniques.
Design and Analysis of Multilayered Neural Network-Based Intrusion Detection System in the Internet of Things Network
(Daffodil International University, 2022-09-20) Sangeetha, S. K. B.; Mani, Prasanna; Maheshwari, V.; Jayagopal, Prabhu; Kumar, M. Sandeep; Muhammad, Shaikh; Allayear, Shaikh Muhammad
A large array of objects is networked together under the sophisticated concept known as the Internet of Things (IoT). These connected devices collect crucial information that could have a big impact on society, business, and the entire planet. In hostile settings like the internet, the IoT is particularly susceptible to multiple threats. Standard high-end security solutions are insufficient for safeguarding an IoT system due to the low processing power and storage capacity of IoT devices. This emphasizes the demand for scalable, distributed, and long-lasting smart security solutions. Deep learning excels at handling heterogeneous data of varying sizes. In this study, the transport layer of IoT networks is secured using a multilayered security approach based on deep learning. The created architecture uses the intrusion detection datasets from CIC-IDS-2018, BoT-IoT, and ToN-IoT to evaluate the suggested multi-layered approach. Finally, the new design outperformed the existing methods and obtained an accuracy of 98% based on the examined criteria.
Development of Templates for Dictionary Entries of Bangla Roots and Primary Suffixes for Universal Networking Language
(IEEE Xplore, 2011-01-06) Hossain, Md. Zakir; Allayear, Shaikh Muhammad; Ali, Md. Nawab Yousuf; Das, Jugal Krishna
The Universal Networking Language (UNL) is a world wide generalizes form of human interactive language in a machine independent digital platform for defining, recapitulating, amending, storing and dissipating knowledge or information among people of different affiliations. The theoretical and applied research associated with this interdisciplinary endeavor facilitates in a number of practical applications in most domains of human activities such as creating globalization trends of markets or geopolitical interdependence among nations. In our research work we have tried to develop Templates for Dictionary Entries of Bangla Roots, Krit Prottoy (primary suffix) and Kria Bivokti (verbal inflexion) which will help to create a doorway for converting the Bangla Sentence to UNL and vice versa and subside the barrier between Bangla to other languages. Full Text Link: http://doi.org/10.1109/IALP.2010.29
Error Recovery Mechanism for iSCSI Protocol Based Mobile NAS Cluster System
(Springer, 2011) Allayear, Shaikh Muhammad; Park, Sung Soon; Ali, Md. Nawab Yousuf; Hasmat, Ullah Mohammad
In this paper, we proposed an error recovery module for iSCSI protocol based NAS (Network Attached Storage) system in wireless network environment, which ensures reliability and efficiency data transmission. Full Text Link: https://doi.org/10.1007/978-3-642-32573-1_24
Feature Learning-Based Generative Adversarial Network Data Augmentation for Class-Based Few-Shot Learning
(Daffodil International University, 2022-07-21) Subedi, Bharat; Sathishkumar, V. E.; Maheshwari, V.; Kumar, M. Sandeep; Jayagopal, Prabhu; Allayear, Shaikh Muhammad
As training deep neural networks enough requires a large amount of data, there have been a lot of studies to deal with this problem. Data augmentation techniques are basic solutions to increase training data using existing data. Geometric transformations and color space augmentations are well-known augmentation techniques, but they still require some manual work and can generate limited types of data only. Therefore, there are many interests in generative-model-based augmentation lately, which can learn the distribution of data. This study proposes a set of GAN-based data augmentation methods that can generate good quality training data. The proposed networks, f-DAGAN (data augmentation generative adversarial networks), have been motivated by the DAGAN that learns data distribution from two real data. The basic f-DAGAN uses dual discriminators handling both generated data and generated feature spaces for better learning the given data. The other versions of f-DAGANs have been proposed for generating hard or easy data that have additional dual classifiers for both generated data and feature spaces to control the generator. Hard data is useful for optimized training to increase the target performance such as classification accuracy. Easy data generation can be used especially in few-shot learning. The quality of generated data has been validated in two ways: using t-SNE visualization of generated data and classification accuracy by training with generated data using the MNIST data set. The t-SNE representations show that data generated by f-DAGAN are evenly distributed for every class better than the exiting generative model-based augmentation methods. The f-DAGAN also shows the best classification accuracy by training with generated data. The f-DAGAN version for easy and hard data generation generates data well from five-shot learning and performs well in sample data generation experiments.
Generation of Bangla Text from Universal Networking Language Expression
(Springer, 2011) Ali, Md. Nawab Yousuf; Allayear, Shaikh Muhammad; Ali, M. Ameer; Sorwar, Golam
This paper presents a work on generating Bangla sentences from an interlingua representation called Universal Networking Language (UNL). UNL represents knowledge in the form of semantic network like hyper-graphs which contains disambiguated words, binary semantic relations, and speech act like attributes associated with the words, assisted by the semantically rich lexicon and a set of analysis and generation rules. We have developed a set of generation rules for converting UNL expression to Bangla sentences. Our experiment shows that these rules successfully generate correct Bangla sentences from UNL expressions. Full Text Link: https://doi.org/10.1007/978-3-642-32573-1_25