Md. Monirul Islam
Other affiliations: Dr Emilio B Espinosa Sr Memorial State College of Agriculture and Technology, Monash University, Clayton campus, Bangladesh University of Engineering and Technology ...read more
Bio: Md. Monirul Islam is an academic researcher from University of Dhaka. The author has contributed to research in topics: Artificial neural network & Medicine. The author has an hindex of 34, co-authored 209 publications receiving 4988 citations. Previous affiliations of Md. Monirul Islam include Dr Emilio B Espinosa Sr Memorial State College of Agriculture and Technology & Monash University, Clayton campus.
Papers published on a yearly basis
TL;DR: This paper analyzes key aspects of the various AIA methods, including both feature extraction and semantic learning methods and provides a comprehensive survey on automatic image annotation.
Abstract: Nowadays, more and more images are available. However, to find a required image for an ordinary user is a challenging task. Large amount of researches on image retrieval have been carried out in the past two decades. Traditionally, research in this area focuses on content based image retrieval. However, recent research shows that there is a semantic gap between content based image retrieval and image semantics understandable by humans. As a result, research in this area has shifted to bridge the semantic gap between low level image features and high level semantics. The typical method of bridging the semantic gap is through the automatic image annotation (AIA) which extracts semantic features using machine learning techniques. In this paper, we focus on this latest development in image retrieval and provide a comprehensive survey on automatic image annotation. We analyse key aspects of the various AIA methods, including both feature extraction and semantic learning methods. Major methods are discussed and illustrated in details. We report our findings and provide future research directions in the AIA area in the conclusions
TL;DR: The spatial and temporal distribution of heavy metals in water, sediment and fish (dry weight basis) of Buriganga River, Bangladesh were determined by atomic absorption spectrophotometer as discussed by the authors.
Abstract: The spatial and temporal distribution of heavy metals in water, sediment and fish (dry weight basis) of Buriganga River, Bangladesh were determined by atomic absorption spectrophotometer. In water concentration of Pb, Cd, Ni, Cu and Cr varied seasonally and spatially from 58.17 to 72.45I¼g/L, 7.08 to 12.33I¼g/L, 7.15 to 10.32I¼g/L, 107.38 to 201.29I¼g/L and 489.27 to 645.26I¼g/L, respectively. Chromium was the most abundant in the water of Balughat during pre-monsoon, whereas, Cd was the most scarce in the water of Shawaryghat during monsoon. The sediment also showed spatial and temporal variation of Pb, Cd, Ni, Cu and Cr ranged from 64.71 to 77.13 mg/kg, 2.36 to 4.25 mg/kg, 147.06 to 258.17 mg/kg, 21.75 to 32.54 mg/kg and 118.63 to 218.39 mg/kg, respectively. Among all the metals studied in sediment, Ni was the highest at Foridabad during pre-monsoon and Cd was the lowest at Shawaryghat during monsoon. In six species of fish studied, the concentration of Pb, Cd, Ni, Cu and Cr varied seasonally from 8.03 to 13.52 mg/kg, 0.73 to 1.25 mg/kg, 8.25 to 11.21 mg/kg, 3.36 to 6.34 mg/kg and 5.27 to 7.38 mg/kg, respectively. Of the five metals studied Pb concentration was the highest in Gudusia chapra during monsoon, in contrast, Cd concentration was the lowest in Cirrhinus reba during post-monsoon. Some of the heavy metalsâ€™ concentrations are higher than the recommended value, which suggest that the Buriganga is to a certain extent a heavy metal polluted river and the water, sediment and fish are not completely safe for health.
TL;DR: The experimental results show that CNNE can produce NN ensembles with good generalization ability and the use of negative correlation learning and different training epochs reflect CNNEs emphasis on diversity among individual NNs in an ensemble.
Abstract: Presents a constructive algorithm for training cooperative neural-network ensembles (CNNEs). CNNE combines ensemble architecture design with cooperative training for individual neural networks (NNs) in ensembles. Unlike most previous studies on training ensembles, CNNE puts emphasis on both accuracy and diversity among individual NNs in an ensemble. In order to maintain accuracy among individual NNs, the number of hidden nodes in individual NNs are also determined by a constructive approach. Incremental training based on negative correlation is used in CNNE to train individual NNs for different numbers of training epochs. The use of negative correlation learning and different training epochs for training individual NNs reflect CNNEs emphasis on diversity among individual NNs in an ensemble. CNNE has been tested extensively on a number of benchmark problems in machine learning and neural networks, including Australian credit card assessment, breast cancer, diabetes, glass, heart disease, letter recognition, soybean, and Mackey-Glass time series prediction problems. The experimental results show that CNNE can produce NN ensembles with good generalization ability.
TL;DR: In this article, a comprehensive compilation and review of the latest literature regarding research works rendered to achieve improved efficiency through appropriate cooling systems is presented, which aims to enhance the efficiency of the solar PV systems and to ensure a longer life at the same time.
Abstract: Photovoltaic (PV) systems operate in a paradox; sunlight is the essential input to generate electricity with PV, but they suffer a digression in performance as the operating temperature goes higher. This work is a comprehensive compilation and review of the latest literature regarding research works rendered to achieve improved efficiency through appropriate cooling systems. Most of the research goals were twofold, that is to enhance the efficiency of the solar PV systems and to ensure a longer life at the same time. The passive cooling systems are found to achieve a reduction in PV module temperature in the range of 6–20 °C with an improvement in electrical efficiency up to 15.5% maximum. On the other side, active cooling systems’ performance are better, as may expected, with a reduction in PV module temperature as high as 30 °C with an improvement in electrical efficiency up to 22% maximum along with additional thermal energy output with efficiency reaching as high as 60%. Based on the wide-ranging review, it may be predicted that with the swelling growth of solar PV electricity worldwide, the compatible cooling system is becoming obligatory in order to ensure better energy harvest and utilization.
TL;DR: In this paper, the authors assess the vulnerability of fishery-based livelihoods to the impacts of climate variability and change in two coastal fishing communities in Bangladesh and use a composite index approach to calculate livelihood vulnerability and qualitative methods to understand how exposure, sensitivity, and adaptive capacity measured by sub-indices produce vulnerability.
Abstract: Globally, fisheries support livelihoods of over half a billion people who are exposed to multiple climatic stresses and shocks that affect their capacity to subsist. Yet, only limited research exists on the vulnerability of fishery-based livelihood systems to climate change. We assess the vulnerability of fishery-based livelihoods to the impacts of climate variability and change in two coastal fishing communities in Bangladesh. We use a composite index approach to calculate livelihood vulnerability and qualitative methods to understand how exposure, sensitivity, and adaptive capacity measured by sub-indices produce vulnerability. Our results suggest that exposure to floods and cyclones, sensitivity (such as dependence on small-scale marine fisheries for livelihoods), and lack of adaptive capacity in terms of physical, natural, and financial capital and diverse livelihood strategies construe livelihood vulnerability in different ways depending on the context. The most exposed community is not necessarily the most sensitive or least able to adapt because livelihood vulnerability is a result of combined but unequal influences of bio-physical and socio-economic characteristics of communities and households. But within a fishing community, where households are similarly exposed, higher sensitivity and lower adaptive capacity combine to create higher vulnerability. Initiatives to reduce livelihood vulnerability should be correspondingly multifaceted.
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).
TL;DR: In this article, the authors present a document, redatto, voted and pubblicato by the Ipcc -Comitato intergovernativo sui cambiamenti climatici - illustra la sintesi delle ricerche svolte su questo tema rilevante.
Abstract: Cause, conseguenze e strategie di mitigazione Proponiamo il primo di una serie di articoli in cui affronteremo l’attuale problema dei mutamenti climatici. Presentiamo il documento redatto, votato e pubblicato dall’Ipcc - Comitato intergovernativo sui cambiamenti climatici - che illustra la sintesi delle ricerche svolte su questo tema rilevante.
TL;DR: This paper reviews existing ensemble techniques and can be served as a tutorial for practitioners who are interested in building ensemble based systems.
Abstract: The idea of ensemble methodology is to build a predictive model by integrating multiple models. It is well-known that ensemble methods can be used for improving prediction performance. Researchers from various disciplines such as statistics and AI considered the use of ensemble methodology. This paper, review existing ensemble techniques and can be served as a tutorial for practitioners who are interested in building ensemble based systems.
01 Feb 2016