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Search Results: 1 - 10 of 401419 matches for " M. Kamruzzaman Munshi "
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Comparative Study of Raw and Boiled Silver Pomfret Fish from Coastal Area and Retail Market in Relation to Trace Metals and Proximate Composition
Roksana Huque,M. Kamruzzaman Munshi,Afifa Khatun,Mahfuza Islam,Afzal Hossain,Arzina Hossain,Shirin Akter,Jamiul Kabir,Yeasmin Nahar Jolly,Ashraful Islam
International Journal of Food Science , 2014, DOI: 10.1155/2014/826139
Abstract: Trace metals concentration and proximate composition of raw and boiled silver pomfret (Pampus argenteus) from coastal area and retail market were determined to gain the knowledge of the risk and benefits associated with indiscriminate consumption of marine fishes. The effects of cooking (boiling) on trace metal and proximate composition of silver pomfret fish were also investigated. Trace element results were determined by the Energy Dispersive X-ray Fluorescence (EDXRF) Spectrometer wherein fish samples from both areas exceeded the standard limits set by FAO/WHO for manganese, lead, cadmiumm and chromium and boiling has no significant effects on these three metal concentrations. Long-term intake of these contaminated fish samples can pose a health risk to humans who consume them. 1. Introduction Fish is a healthy food for most of the world’s population particularly developing countries in contrast to meat, poultry, and eggs. Fish provides comparatively cheap and readily available protein sources (about 15 to 20 percent) in addition to long chains of n-3 fatty acids, amino acids, vitamins, and minerals which contributes to healthier nutritional options for a balance dietary intake [1, 2]. Among the all fishes, marine fish are very rich sources of protein and various mineral components. The total content of minerals in raw flesh of marine fish is in the range of 0.6–1.5% of wet weight [3]. Trace metals are present in water from natural sources such as the rocks of the sea bed and also accumulated as a result of human activities such as emissions from industrial processes. These elements are taken up by marine fishes which are higher up the food chain. As a result, the concentrations of many elements including mercury, arsenic, lead, and cadmium in fish can be relatively high compared to other foods. Many of these metals such as iron, copper, cobalt, manganese, molybdenum, nickel, and zinc are essential trace elements and play important roles in biological systems. Meanwhile, mercury, lead, and cadmium are toxic, even in trace amounts [4]. Moreover, elevated concentration of manganese and nickel has been found to be toxic to aquatic organism [5, 6]. To monitor trace metals concentrations in the coastal environment, marine fishes have been widely used as bioindicators due to their wide range of distribution. Several studies have been carried out on metal pollution in different species of edible fish. Predominantly, fish toxicological and environmental studies have prompted interest in the determination of toxic elements in seafood [7–10]. According to the
CR-MAC: A Multichannel MAC Protocol for Cognitive Radio AD HOC Networks
S. M. Kamruzzaman
International Journal of Computer Networks & Communications , 2010,
Abstract: This paper proposes a cross-layer based cognitive radio multichannel medium access control (MAC)protocol with TDMA, which integrate the spectrum sensing at physical (PHY) layer and the packetscheduling at MAC layer, for the ad hoc wireless networks. The IEEE 802.11 standard allows for the useof multiple channels available at the PHY layer, but its MAC protocol is designed only for a singlechannel. A single channel MAC protocol does not work well in a multichannel environment, because ofthe multichannel hidden terminal problem. Our proposed protocol enables secondary users (SUs) toutilize multiple channels by switching channels dynamically, thus increasing network throughput. In ourproposed protocol, each SU is equipped with only one spectrum agile transceiver, but solves themultichannel hidden terminal problem using temporal synchronization. The proposed cognitive radioMAC (CR-MAC) protocol allows SUs to identify and use the unused frequency spectrum in a way thatconstrains the level of interference to the primary users (PUs). Our scheme improves network throughputsignificantly, especially when the network is highly congested. The simulation results show that ourproposed CR-MAC protocol successfully exploits multiple channels and significantly improves networkperformance by using the licensed spectrum band opportunistically and protects PUs from interference,even in hidden terminal situations.
Web Page Categorization Using Artificial Neural Networks
S. M. Kamruzzaman
Computer Science , 2010,
Abstract: Web page categorization is one of the challenging tasks in the world of ever increasing web technologies. There are many ways of categorization of web pages based on different approach and features. This paper proposes a new dimension in the way of categorization of web pages using artificial neural network (ANN) through extracting the features automatically. Here eight major categories of web pages have been selected for categorization; these are business & economy, education, government, entertainment, sports, news & media, job search, and science. The whole process of the proposed system is done in three successive stages. In the first stage, the features are automatically extracted through analyzing the source of the web pages. The second stage includes fixing the input values of the neural network; all the values remain between 0 and 1. The variations in those values affect the output. Finally the third stage determines the class of a certain web page out of eight predefined classes. This stage is done using back propagation algorithm of artificial neural network. The proposed concept will facilitate web mining, retrievals of information from the web and also the search engines.
RGANN: An Efficient Algorithm to Extract Rules from ANNs
S. M. Kamruzzaman
Computer Science , 2010,
Abstract: This paper describes an efficient rule generation algorithm, called rule generation from artificial neural networks (RGANN) to generate symbolic rules from ANNs. Classification rules are sought in many areas from automatic knowledge acquisition to data mining and ANN rule extraction. This is because classification rules possess some attractive features. They are explicit, understandable and verifiable by domain experts, and can be modified, extended and passed on as modular knowledge. A standard three-layer feedforward ANN is the basis of the algorithm. A four-phase training algorithm is proposed for backpropagation learning. Comparing them to the symbolic rules generated by other methods supports explicitness of the generated rules. Generated rules are comparable with other methods in terms of number of rules, average number of conditions for a rule, and predictive accuracy. Extensive experimental studies on several benchmarks classification problems, including breast cancer, wine, season, golf-playing, and lenses classification demonstrate the effectiveness of the proposed approach with good generalization ability.
Extracting Symbolic Rules for Medical Diagnosis Problem
S. M. Kamruzzaman
Computer Science , 2010,
Abstract: Neural networks (NNs) have been successfully applied to solve a variety of application problems involving classification and function approximation. Although backpropagation NNs generally predict better than decision trees do for pattern classification problems, they are often regarded as black boxes, i.e., their predictions cannot be explained as those of decision trees. In many applications, it is desirable to extract knowledge from trained NNs for the users to gain a better understanding of how the networks solve the problems. An algorithm is proposed and implemented to extract symbolic rules for medical diagnosis problem. Empirical study on three benchmarks classification problems, such as breast cancer, diabetes, and lenses demonstrates that the proposed algorithm generates high quality rules from NNs comparable with other methods in terms of number of rules, average number of conditions for a rule, and predictive accuracy.
REx: An Efficient Rule Generator
S. M. Kamruzzaman
Computer Science , 2010,
Abstract: This paper describes an efficient algorithm REx for generating symbolic rules from artificial neural network (ANN). Classification rules are sought in many areas from automatic knowledge acquisition to data mining and ANN rule extraction. This is because classification rules possess some attractive features. They are explicit, understandable and verifiable by domain experts, and can be modified, extended and passed on as modular knowledge. REx exploits the first order information in the data and finds shortest sufficient conditions for a rule of a class that can differentiate it from patterns of other classes. It can generate concise and perfect rules in the sense that the error rate of the rules is not worse than the inconsistency rate found in the original data. An important feature of rule extraction algorithm, REx, is its recursive nature. They are concise, comprehensible, order insensitive and do not involve any weight values. Extensive experimental studies on several benchmark classification problems, such as breast cancer, iris, season, and golf-playing, demonstrate the effectiveness of the proposed approach with good generalization ability.
CR-MAC: A multichannel MAC protocol for cognitive radio ad hoc networks
S. M. Kamruzzaman
Computer Science , 2010, DOI: 10.5121/ijcnc.2010.2501
Abstract: This paper proposes a cross-layer based cognitive radio multichannel medium access control (MAC) protocol with TDMA, which integrate the spectrum sensing at physical (PHY) layer and the packet scheduling at MAC layer, for the ad hoc wireless networks. The IEEE 802.11 standard allows for the use of multiple channels available at the PHY layer, but its MAC protocol is designed only for a single channel. A single channel MAC protocol does not work well in a multichannel environment, because of the multichannel hidden terminal problem. Our proposed protocol enables secondary users (SUs) to utilize multiple channels by switching channels dynamically, thus increasing network throughput. In our proposed protocol, each SU is equipped with only one spectrum agile transceiver, but solves the multichannel hidden terminal problem using temporal synchronization. The proposed cognitive radio MAC (CR-MAC) protocol allows SUs to identify and use the unused frequency spectrum in a way that constrains the level of interference to the primary users (PUs). Our scheme improves network throughput significantly, especially when the network is highly congested. The simulation results show that our proposed CR-MAC protocol successfully exploits multiple channels and significantly improves network performance by using the licensed spectrum band opportunistically and protects PUs from interference, even in hidden terminal situations.
An Energy Efficient Multichannel MAC Protocol for Cognitive Radio Ad Hoc Networks
S. M. Kamruzzaman
Computer Science , 2010,
Abstract: This paper presents a TDMA based energy efficient cognitive radio multichannel medium access control (MAC) protocol called ECR-MAC for wireless Ad Hoc Networks. ECR-MAC requires only a single half-duplex radio transceiver on each node that integrates the spectrum sensing at physical (PHY) layer and the packet scheduling at MAC layer. In addition to explicit frequency negotiation which is adopted by conventional multichannel MAC protocols, ECR-MAC introduces lightweight explicit time negotiation. This two-dimensional negotiation enables ECR-MAC to exploit the advantage of both multiple channels and TDMA, and achieve aggressive power savings by allowing nodes that are not involved in communication to go into doze mode. The IEEE 802.11 standard allows for the use of multiple channels available at the PHY layer, but its MAC protocol is designed only for a single channel. A single channel MAC protocol does not work well in a multichannel environment, because of the multichannel hidden terminal problem. The proposed energy efficient ECR-MAC protocol allows SUs to identify and use the unused frequency spectrum in a way that constrains the level of interference to the primary users (PUs). Extensive simulation results show that our proposed ECR-MAC protocol successfully exploits multiple channels and significantly improves network performance by using the licensed spectrum band opportunistically and protects QoS provisioning over cognitive radio ad hoc networks.
Text Classification using Artificial Intelligence
S. M. Kamruzzaman
Computer Science , 2010,
Abstract: Text classification is the process of classifying documents into predefined categories based on their content. It is the automated assignment of natural language texts to predefined categories. Text classification is the primary requirement of text retrieval systems, which retrieve texts in response to a user query, and text understanding systems, which transform text in some way such as producing summaries, answering questions or extracting data. Existing supervised learning algorithms for classifying text need sufficient documents to learn accurately. This paper presents a new algorithm for text classification using artificial intelligence technique that requires fewer documents for training. Instead of using words, word relation i.e. association rules from these words is used to derive feature set from pre-classified text documents. The concept of na\"ive Bayes classifier is then used on derived features and finally only a single concept of genetic algorithm has been added for final classification. A system based on the proposed algorithm has been implemented and tested. The experimental results show that the proposed system works as a successful text classifier.
The Influence of High Pressure Coolant on Temperature, Tool Wear and Surface Finish in Turning 17CrNiMo6 and 42CrMo4 Steels
M. Kamruzzaman,N. R. Dhar
Journal of Engineering and Applied Sciences , 2009,
Abstract: Machining of steel and other hard materials under high speed-feed condition requires instant heat transfer from the cutting interface of the tool and the work material where the intensity of cutting temperature is the maximum to avoid surface distortion and to improve tool life. Conventional cooling fails to control the cutting temperature and to maintain the product quality. Moreover it is hazardous for human being and a major source of pollution in the industries. High pressure and high velocity coolant may provide the best control to reduce cutting temperature and tool wear as well as to increase tool life. This paper deals with an experimental investigation of the effect of high-pressure coolant on temperature, tool wear and surface roughness in machining of 17CrNiMo6 and 42CrMo4 steels using uncoated carbide tools and comparing them under dry cut condition as well as the materials themselves. The inspiring experimental results include the reduction of cutting temperature and tool wear and improvement of surface finish with the use of high-pressure coolant. But increasing hardness increases cutting temperature and tool wear rate.
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