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Your search keyword '"Rezaul, Karim Mohammed"' showing total 25 results
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25 results on '"Rezaul, Karim Mohammed"'

1. Internet of Things-Driven Precision in Fish Farming: A Deep Dive into Automated Temperature, Oxygen, and pH Regulation.

Author

Nayoun, Md. Naymul Islam, Hossain, Syed Akhter, Rezaul, Karim Mohammed, Siddiquee, Kazy Noor e Alam, Islam, Md. Shabiul, and Jannat, Tajnuva

Subjects
WATER quality management, FISH farming, FOOD supply, FISHERIES, AGRICULTURE
Abstract

The research introduces a revolutionary Internet of Things (IoT)-based system for fish farming, designed to significantly enhance efficiency and cost-effectiveness. By integrating the NodeMcu12E ESP8266 microcontroller, this system automates the management of critical water quality parameters such as pH, temperature, and oxygen levels, essential for fostering optimal fish growth conditions and minimizing mortality rates. The core of this innovation lies in its intelligent monitoring and control mechanism, which not only supports accelerated fish development but also ensures the robustness of the farming process through automated adjustments whenever the monitored parameters deviate from desired thresholds. This smart fish farming solution features an Arduino IoT cloud-based framework, offering a user-friendly web interface that enables fish farmers to remotely monitor and manage their operations from any global location. This aspect of the system emphasizes the importance of efficient information management and the transformation of sensor data into actionable insights, thereby reducing the need for constant human oversight and significantly increasing operational reliability. The autonomous functionality of the system is a key highlight, designed to persist in adjusting the environmental conditions within the fish farm until the optimal parameters are restored. This capability greatly diminishes the risks associated with manual monitoring and adjustments, allowing even those with limited expertise in aquaculture to achieve high levels of production efficiency and sustainability. By leveraging data-driven technologies and IoT innovations, this study not only addresses the immediate needs of the fish farming industry but also contributes to solving the broader global challenge of protein production. It presents a scalable and accessible approach to modern aquaculture, empowering stakeholders to maximize output and minimize risks associated with fish farming, thereby paving the way for a more sustainable and efficient future in the global food supply. [ABSTRACT FROM AUTHOR]

Published
2024
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3. Enhancing Audio Classification Through MFCC Feature Extraction and Data Augmentation with CNN and RNN Models.

Author

Rezaul, Karim Mohammed, Jewel, Md., Islam, Md Shabiul, e Alam Siddiquee, Kazy Noor, Barua, Nick, Rahman, Muhammad Azizur, Shan-A-Khuda, Mohammad, Bin Sulaiman, Rejwan, Imam Shaikh, Md Sadeque, Hamim, Md Abrar, Tanmoy, F. M., Ul Haque, Afraz, Nipun, Musarrat Saberin, Dorudian, Navid, Kareem, Amer, Farid, Ahmmed Khondokar, Mubarak, Asma, Jannat, Tajnuva, and Tuj Asha, Umme Fatema

Published
2024
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4. Impact of Microfinance Operations on the Livelihood of the Clients: A Review of the Existing Literature

Author

Hossain, Aliar, Hossain, Mohammad Kamal, Rezaul, Karim Mohammed, Akan, Ozgur, Series editor, Bellavista, Paolo, Series editor, Cao, Jiannong, Series editor, Dressler, Falko, Series editor, Ferrari, Domenico, Series editor, Gerla, Mario, Series editor, Kobayashi, Hisashi, Series editor, Palazzo, Sergio, Series editor, Sahni, Sartaj, Series editor, Shen, Xuemin (Sherman), Series editor, Stan, Mircea, Series editor, Xiaohua, Jia, Series editor, Zomaya, Albert, Series editor, Coulson, Geoffrey, Series editor, Telesca, Luigi, editor, Stanoevska-Slabeva, Katarina, editor, and Rakocevic, Veselin, editor

Published
2010
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7. HEAF: A Novel Estimator for Long-Range Dependent Self-similar Network Traffic

Author

Rezaul, Karim Mohammed, Pakstas, Algirdas, Gilchrist, R., Chen, Thomas M., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Dough, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Koucheryavy, Yevgeni, editor, Harju, Jarmo, editor, and Iversen, Villy B., editor

Published
2006
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24. A Comparison of Methods for Estimating the Tail Index of Heavy-tailed Internet Traffic.

Author

Sobh, Tarek, Elleithy, Khaled, Mahmood, Ausif, Karim, Mohammed, Rezaul, Karim Mohammed, and Grout, Vic

Abstract

Many researchers have discussed the effects of heavy-tailedness in network traffic patterns and shown that Internet traffic flows exhibit characteristics of self-similarity that can be explained by the heavy-tailedness of the various distributions involved. Self-similarity and heavy-tailedness are of great importance for network capacity planning purposes in which researchers are interested in developing analytical methods for analysing traffic characteristics. Designers of computing and telecommunication systems are increasingly interested in employing heavy-tailed distributions to generate workloads for use in simulation although simulations employing such workloads may show unusual characteristics. In this paper, we describe some of the most useful mechanisms for estimating the tail index, particularly for distributions having the power law observed in different contexts in the Internet. [ABSTRACT FROM AUTHOR]

Published
2007
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25. HEAF: A Novel Estimator for Long-Range Dependent Self-similar Network Traffic.

Author

Koucheryavy, Yevgeni, Harju, Jarmo, Iversen, Villy B., Rezaul, Karim Mohammed, Pakstas, Algirdas, Gilchrist, R., and Chen, Thomas M.

Abstract

Long-range dependent (LRD) self-similar chaotic behaviour has been found to be present in internet traffic by many researchers. The ‘Hurst exponent', H, is used as a measure of the degree of long-range dependence. A variety of techniques exist for estimating the Hurst exponent; these deliver a variable efficacy of estimation. Whilst ways of exploiting these techniques for control and optimization of traffic in real systems are still to be discovered, there is need for a reliable estimator which will characterise the traffic. This paper uses simulation to compare established estimators and introduces a new estimator, HEAF, a ‘Hurst Exponent Autocorrelation Function' estimator. It is demonstrated that HEAF(2), based on the sample autocorrelation of lag2, yields an estimator which behaves well in terms of bias and mean square error, for both fractional Gaussian and FARIMA processes. Properties of various estimators are investigated and HEAF(2) is shown to have promising results. The performance of the estimators is illustrated by experiments with MPEG/Video traces. [ABSTRACT FROM AUTHOR]

Published
2006
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