Browsing by Author "Ibrahim, Sobhy M."

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A Critical Review and Prospect of NO2 and SO2 Pollution Over Asia
(Elsevier, 2023-06-10) Jion, Most. Mastura Munia Farjana; Jannat, Jannatun Nahar; Mia, Md. Yousuf; Ali, Md. Arfan; Islam, Md. Saiful; Ibrahim, Sobhy M.; Pal, Subodh Chandra; Islam, Aznarul; Sarker, Aniruddha; Malafaia, Guilherme; Bilal, Muhammad; Islam, Abu Reza Md Towfiqul
Nitrogen dioxide (NO2) and sulfur dioxide (SO2) are two major atmospheric pollutants that significantly threaten human health, the environment, and ecosystems worldwide. Despite this, only some studies have investigated the spatiotemporal hotspots of NO2 and SO2, their trends, production, and sources in Asia. Our study presents a literature review covering the production, trends, and sources of NO2 and SO2 across Asian countries (e.g., Bangladesh, China, India, Iran, Japan, Pakistan, Malaysia, Kuwait, and Nepal). Based on the findings of the review, NO2 and SO2 pollution are increasing due to industrial activity, fossil fuel burning, biomass burning, heavy traffic movement, electricity generation, and power plants. There is significant concern about health risks associated with NO2 and SO2 emissions in Bangladesh, China, India, Malaysia, and Iran, as they pay less attention to managing and controlling pollution. Even though the lack of quality datasets and adequate research in most Asian countries further complicates the management and control of NO2 and SO2 pollution. This study has NO2 and SO2 pollution scenarios, including hotspots, trends, sources, and their influences on Asian countries. This study highlights the existing research gaps and recommends new research on identifying integrated sources, their variations, spatiotemporal trends, emission characteristics, and pollution level. Finally, the present study suggests a framework for controlling and monitoring these two pollutants' emissions.
Early Prediction of Chronic Kidney Disease
(Daffodil International University, 22-08-29) Mondol, Chaity; Shamrat, F. M. Javed Mehedi; Hasan, Md. Robiul; Alam, Saidul; Ghosh, Pronab; Tasnim, Zarrin; Ahmed, Kawsar; Bui, Francis M.; Ibrahim, Sobhy M.
Chronic kidney disease (CKD) is one of the most life-threatening disorders. To improve survivability, early discovery and good management are encouraged. In this paper, CKD was diagnosed using multiple optimized neural networks against traditional neural networks on the UCI machine learning dataset, to identify the most efficient model for the task. The study works on the binary classification of CKD from 24 attributes. For classification, optimized CNN (OCNN), ANN (OANN), and LSTM (OLSTM) models were used as well as traditional CNN, ANN, and LSTM models. With various performance matrixes, error measures, loss values, AUC values, and compilation time, the implemented models are compared to identify the most competent model for the classification of CKD. It is observed that, overall, the optimized models have better performance compared to the traditional models. The highest validation accuracy among the tradition models were achieved from CNN with 92.71%, whereas OCNN, OANN, and OLSTM have higher accuracies of 98.75%, 96.25%, and 98.5%, respectively. Additionally, OCNN has the highest AUC score of 0.99 and the lowest compilation time for classification with 0.00447 s, making it the most efficient model for the diagnosis of CKD.
Identifying Factors Affecting Irrigation Metrics in the Haor Basin Using Integrated Shannon’s Entropy, Fuzzy Logic and Automatic Linear Model
(Elsevier, 2023-06-01) Mia, Md Yousuf; Islam, Abu Reza Md Towfiqul; Jannat, Jannatun Nahar; Jion, Most Mastura Munia Farjana; Sarker, Aniruddha; Tokatli, Cem; Siddique, Md Abu Bakar; Ibrahim, Sobhy M.; Senapathi, Venkatramanan
The sustainability of agricultural practices is seriously threatened by the quality of water used for irrigation. This paper aims to evaluate the suitability of irrigation water and identify the region suitable for agricultural use in the Haor basin of Bangladesh using conventional irrigation indices such as sodium adsorption ratio (SAR), percent sodium (Na%), magnesium hazard ratio (MHR), permeability index (PI), and Kelly's ratio (KR), as well as novel irrigation indices such as, Shannon's entropy index for irrigation water quality (EWQ) and fuzzy logic index for irrigation water quality (FIWQI). The main influences of groundwater and surface water parameters on irrigation indices were predicted using automatic linear modeling (ALM). Forty water samples were collected from shallow tube wells, rivers, canals, ponds, and drainage systems within agricultural land sampled and analyzed for cations and anions. SAR and KR show that 52.5% and 60% of the samples exceeded the allowable level, respectively, indicating that they were unsuitable for irrigation. According to EWQI, about 55% of the analyzed samples were of good quality, while 45% were of medium quality. ALM predicted that KR (0.98), Na% (0.87), and MHR (0.14) were the main significant factors affecting SAR and KR. ALM shows that elevated sodium, magnesium, and calcium are the most important factors affecting irrigation water suitability. The EWQI and FIWQI integrated models showed that water from nearly 30% of the sampling sites would need treatment before use. A new suitability map created by overlaying all parameters showed that surface water and some groundwater in the western and southwestern portions are suitable for agriculture. The north-central part is unsuitable for irrigation due to excessive sodium and magnesium levels. This paper will highlight the irrigation pattern for regional water resource use, identify new suitable regions, and improve sustainable agricultural practices in the Haor basin.
Machine Learning-based Statistical Analysis for Early Stage Detection of Cervical Cancer
(Computers in Biology and Medicine, Elsevier, 2021-12) Ali, Md Mamun; Ahmed, Kawsar; Bui, Francis M.; Paul, Bikash Kumar; Ibrahim, Sobhy M.; Quinn, Julian M.W.; Moni, Mohammad Ali
Cervical cancer (CC) is the most common type of cancer in women and remains a significant cause of mortality, particularly in less developed countries, although it can be effectively treated if detected at an early stage. This study aimed to find efficient machine-learning-based classifying models to detect early stage CC using clinical data. We obtained a Kaggle data repository CC dataset which contained four classes of attributes including biopsy, cytology, Hinselmann, and Schiller. This dataset was split into four categories based on these class attributes. Three feature transformation methods, including log, sine function, and Z-score were applied to these datasets. Several supervised machine learning algorithms were assessed for their performance in classification. A Random Tree (RT) algorithm provided the best classification accuracy for the biopsy (98.33%) and cytology (98.65%) data, whereas Random Forest (RF) and Instance-Based K-nearest neighbor (IBk) provided the best performance for Hinselmann (99.16%), and Schiller (98.58%) respectively. Among the feature transformation methods, logarithmic gave the best performance for biopsy datasets whereas sine function was superior for cytology. Both logarithmic and sine functions performed the best for the Hinselmann dataset, while Z-score was best for the Schiller dataset. Various Feature Selection Techniques (FST) methods were applied to the transformed datasets to identify and prioritize important risk factors. The outcomes of this study indicate that appropriate system design and tuning, machine learning methods and classification are able to detect CC accurately and efficiently in its early stages using clinical data.
Pollution Trends and Ecological Risks of Heavy Metal(loid)s in Coastal Zones of Bangladesh
(Elsevier, 2023-06-04) Jannat, Jannatun Nahar; Mia, Md. Yousuf; Jion, Most. Mastura Munia Farjana; Islam, Md. Saiful; Ali, Mir Mohammad; Siddique, Md. Abu Bakar; Rakib, Md. Refat Jahan; Ibrahim, Sobhy M.; Pal, Subodh Chandra; Costache, Romulus; Islam, Abu Reza Md Towfiqul
Heavy metal(loid)s inputs contribute to human and environmental stresses in the coastal zones of Bangladesh. Several studies have been conducted on metal(loid)s pollution in sediment, soil, and water in the coastal zones. However, they are sporadic, and no attempt has been made in coastal zones from the standpoint of chemometric review. The current work aims to provide a chemometric assessment of the pollution trend of metal(loid)s, namely arsenic (As), chromium (Cr), cadmium (Cd), lead (Pb), copper (Cu), zinc (Zn), and nickel (Ni) in sediments, soils, and water across the coastal zones from 2015 to 2022. The findings showed that 45.7, 15.2, and 39.1 % of studies on heavy metal(loid)s were concentrated in the eastern, central, and western zones of coastal Bangladesh. The obtained data were further modeled using chemometric approaches, such as the contamination factor, pollution load index, geoaccumulation index, degree of contamination, Nemerow's pollution index, and ecological risk index. The results revealed that metal(loid)s, primarily Cd, have severely polluted the sediments (contamination factor, CF = 5.20) and soils (CF = 9.35) of coastal regions. Water was moderately polluted (Nemerow's pollution index, =5.22 ± 6.26) in the coastal area. The eastern zone was the most polluted compared to other zones, except for a few observations in the central zone. The overall ecological risks posed by metal(loid)s highlighted the significant ecological risk in sediments (ecological risk index, RI = 123.50) and soils (RI = 238.93) along the eastern coast. The coastal zone may have higher pollution levels due to the proximity of industrial effluent, residential sewage discharge, agricultural activities, sea transport, metallurgical industries, shipbreaking and recycling operations, and seaport activities, which are the major sources of metal(loid)s. This study will provide useful information to the relevant authorities and serve as the foundation for future management and policy decisions to reduce metal(loid) pollution in the coastal zones of southern Bangladesh.