Analysis of a Conceptual Model and Assessment of the e-Procurement System in Bangladesh

The public procurement system in Bangladesh has been running by traditional manual process under the flagship of Public Procurement Act (PPA 2006) and Public Procurement Rules (PPR 2008). Public procurement agencies have been facing challenges in this manual tendering system. To overcome this problem and to bring reality to the “Digital Bangladesh” slogan, the Government of Bangladesh introduced the e-Procurement system under the e-GP (Electronic Government Procurement) guideline 2011. After the inception of e-procurement, there is no e-procurement assessment model to improve the e-GP system. The purpose of this research is to develop a conceptual framework and to design an e-procurement assessment model. With this view, we have considered one of the biggest entity Roads and Highways Department (RHD) of the Government of Bangladesh, for field study. Mixed methods along with FGD (Focus Group Discussion), KII (Key Informant Interview), and survey questionnaires are used to collect data from RHD, and then Statistical Package for Social Science (SPSS) software is used for regression analysis and hypothesis testing to develop the e-Procurement assessment model. The novel contribution of the study lies in the test of the hypothesis that focuses on developing the conceptual model of the e-procurement assessment system in Bangladesh. Findings of the study are essential for all Procurement Entity (PE) and suppliers i.e. contractors of RHD who are engaged in the construction of the infrastructure project development project.

A Secure Model for Preventing the Spread of COVID-19 in Bangladesh

An alarmingly high mortality and morbidity rate has been observed in COVID-19. As a new disease (similar group of SARS-CoV 2), it has been spread to more than two hundred countries and millions of people. However, COVID-19 infection is increasing in Bangladesh also. Therefore, an effective public health response is needed to control the outbreak of the mentioned new disease. If this high increasing factor turns out of control then it provides potential negative impact on social, economic, and political life of Bangladeshi people. One of the most effective ways to reduce or prevent the growth of COVID-19 is to wear masks. Many variants of COVID-19 are available in the world currently. Latest, there is a variant of omicron. This variant is too infectious. It means the vaccine seems to be not perfect against this deadliest disease COVID-19. However, people are not conscious enough about this matter generally. Nowadays, they are very reluctant to wear a mask. Their main thought is to get vaccinated and it can protect themselves. Definitely, the vaccine can protect at a certain level. However, Omicron proves that vaccine is not the ultimate solution. But, we can protect ourselves to wear a mask and keeping physical distance. This is the borderline of our research work. In this work, we proposed a system that will monitor the people who are outside the home/office via a close circuit camera. It evaluates whether the people are mask-free or not. If one does not wear a mask then an image will be captured and compared with database of national-data center for example NID server. As sensitive data, it needs security and protection. Hence, we adopt a cryptographic solution such as authenticated encryption or tag generation. As a result, receiver will be assured that a valid sender provides data to the valid receiver. The goal of this system proposal is to ensure wear face masks. As well, people will be punished based on the findings of mask-wearing. Furthermore, message authentication or tag generation secures sensitive information or data, because of accessing NID server. Specially, this model will be suitable for Bangladesh and other overcrowded countries. Because manmade or policing for all-time monitoring system is quite impossible for the populated country. Hence, we should start an automated system in a secure manner that can monitor the people.

Comparing the Cost and Time of Tender Opening Committee Report Preparation for Manual and E-Procurement Tenders in the Roads and Highways Department of Bangladesh

Bangladesh’s public procurement system has been traditional, i.e., a manual tendering system, under the legislative guidance of PPA-2006 and PPR-2008. Manual tendering has long been a source of concern for public procurement agencies regarding tender opening related to cost and time. To resolve this challenge and make the dream of a Digital Bangladesh a reality, the Government of Bangladesh launched the e-Procurement system in 2011 under the flagship of e-GP Guidelines. Following the successful pilot testing conducted under the umbrella of the CPTU, all public PEs are attempting to implement electronic procurement tenders. This study aims to examine the cost and time involved in preparing Tender Opening Committee (TOC) Reports for manual and e-procurement tenders in Bangladesh’s Roads and Highways Department. A mixed-method was used to collect data from the population of 11 RHD zones, including KII and primarily survey questionnaires. The independent samples t-test was used to compare data and hypothesis tests from two groups using SPSS software. The results indicated that e-Procurement tendering, tender opening preparation costs and time were more efficient than manual tendering. Academicians, students, practitioners, researchers, and policymakers will benefit from the study’s conclusions. Additionally, the government will be urged to improve policies such as the PPA 2006, the PPR 2008, and the 2011 e-GP Guideline.

Macroplastics on Soil-Plant System: Inhibiting Effects of Macroplastics on the Growth of Green Amaranth (<i>Amaranthus viridis</i>)

In recent time Bangladesh faces a serious problem of soil pollution due to plastic contamination. However, the degree of the extent to which the effects of plastics on plant growth occur is not properly identified. An experiment was conducted to measure the effects of mixed plastic (polyethylene and disposable plastic glass) on the growth of Amaranthus viridis. Different doses of mixed plastics (T0, T1, T2, and T3) were applied with a fixed amount of soil for each of the treatments e.g., T0 (control), T1 (10 gm mixed plastics/3kg soil), T2 (15 gm mixed plastics/3kg soil) and T3 (20 gm mixed plastic/3kg soil), and the growth response of Amaranthus viridis against plastic was observed for six consecutive weeks. The growth was measured in terms of plant height and girth diameter. The results showed that the presence of mixed plastic had a significant effect on the growth of Amaranthus viridis and particularly in treatment T3 (3 kg soil/20gm mixed plastic), the plants showed a slower growth response compared to control and the rest of the treatments applied in case of both plant height as well as girth diameter. The statistical analysis (one-way Analysis of Variance) also proved the significance of the treatments (p-values < 0.05) for six consecutive weeks. The experiment was successfully able to set an index on which plastics had their effects on the growth of green amaranth. In addition, the obtained data will be helpful in future research of the study in determining the possible effects of plastic on plant growth viz. green amaranth.

Performance Evaluation of Multiple Classifiers for Predicting Fake News

The rise of fake news on social media has had a detrimental effect on society. Numerous performance evaluations on classifiers that can detect fake news have previously been undertaken by researchers in this area. To assess their performance, we used 14 different classifiers in this study. Secondly, we looked at how soft voting and hard voting classifiers performed in a mixture of distinct individual classifiers. Finally, heuristics are used to create 9 models of stacking classifiers. The F1 score, prediction, recall, and accuracy have all been used to assess performance. Models 6 and 7 achieved the best accuracy of 96.13 while having a larger computational complexity. For benchmarking purposes, other individual classifiers are also tested.

Comparing the Efficiency of Manual and Electronic Tender Document Preparation: Cost and Time Context

In Bangladesh, both manual and e-GP system is used to perform public procurement. Before tender publication, tender documents have to be prepared by the Procurement Entities (PEs). The problem is that the manual tendering system involves more cost and time in preparing tender documents. The study’s purpose is to compare now the efficiency of tender document preparation in the manual system with the e-GP system. This study tests the cost and time involved in tender document preparation, both manual and e-procurement tenders. Sample data was collected from the 11 RHD zones. Structured survey questionnaires were used to collect primary data from the PE officers of RHD. A hypothesis test was performed using the model of independent samples t-test. The test results indicated that e-Procurement tender document preparation costs and time were less than manual tendering. Academicians, researchers, PE officers, and policymakers will benefit from the study’s conclusions.

Efficient Fuel Handling and Management in Port:Context Bangladesh

In the last two decades,the supply and demand of fuel in Bangladesh increased enormously because of her economic growth.Likewise,energy and power require helping the industry and basic supplies to the citizens.Recent development of electric power industry,is essential to arrange the necessary facilities at seaport and its further transportation to the consignee premises by applying JIT(just-in-time)approach inevitably.Driven in particular,innovation is required in the prime ports Chittagong and Mongla,what’s more,in newly established Payra port.In addition,regional demand especially from Nepal,Bhutan and Seven Sisters of India for importing fuel through Bangladeshi seaports or pertaining intermodal freight transportation of containerized fuel or tanktainer that cloaked up the derived demand of increasing seaport facilitates in Bangladesh.Another factor that will influence the importing and handling of fuel is the environmental compliance and stringent environmental regulations enacted by IMO(International Maritime Organization),hereby,necessary steps to be taken by port authority or government accordingly.Emerging opportunities,in addition to traditional fuel management at port,coal,LPG(liquefied petroleum gas)and LNG(liquefied natural gas)are the new products for Bangladesh and its neighbors as growing demand of receiving in Bangladeshi ports in a big volume progressively.This paper aims to explore the role of port authorities in the process of developing port and intermodal infrastructure and others to mitigate the uprising demand of fuel handling in port protected area and quick transfer of fuel to the final destination.

Efficient Fuel Handling and Management in Port:Context Bangladesh

In the last two decades,the supply and demand of fuel in Bangladesh increased enormously because of her economic growth.Likewise,energy and power require helping the industry and basic supplies to the citizens.Recent development of electric power industry,it is essential to arrange the necessary facilities at seaport and its further transportation to the consignee premises by applying Just-In-Time(JIT)approach inevitably.Driven in particular,innovation is required in the prime ports Chittagong and Mongla,what’s more,in newly established Payra port.In addition,regional demand especially from Nepal,Bhutan and Seven Sisters of India for importing fuel through Bangladeshi seaports or pertaining intermodal freight transportation of containerized fuel or tanktainer that cloaked up the derived demand of increasing seaport facilitates in Bangladesh.Another factor that will influence the importing and handling of fuel is the environmental compliance and stringent environmental regulations enacted by IMO,hereby,necessary steps to be taken by port authority or government accordingly.Emerging opportunities,in addition to traditional fuel management at port,coal,LPG and LNG are the new products for Bangladesh and its neighbors as growing demand of receiving in Bangladeshi ports in a big volume progressively.This paper aims to explore the role of port authorities in the process of developing port and intermodal infrastructure and others to mitigate the uprising demand of fuel handling in port protected area and quick transfer of fuel to the final destination.

Brain Tumor Auto-Segmentation on Multimodal Imaging Modalities Using Deep Neural Network

Due to the difficulties of brain tumor segmentation, this paper proposes a strategy for extracting brain tumors from three-dimensional MagneticResonance Image (MRI) and Computed Tomography (CT) scans utilizing3D U-Net Design and ResNet50, taken after by conventional classificationstrategies. In this inquire, the ResNet50 picked up accuracy with 98.96%, andthe 3D U-Net scored 97.99% among the different methods of deep learning.It is to be mentioned that traditional Convolutional Neural Network (CNN)gives 97.90% accuracy on top of the 3D MRI. In expansion, the imagefusion approach combines the multimodal images and makes a fused image toextricate more highlights from the medical images. Other than that, we haveidentified the loss function by utilizing several dice measurements approachand received Dice Result on top of a specific test case. The average mean scoreof dice coefficient and soft dice loss for three test cases was 0.0980. At thesame time, for two test cases, the sensitivity and specification were recordedto be 0.0211 and 0.5867 using patch level predictions. On the other hand,a software integration pipeline was integrated to deploy the concentratedmodel into the webserver for accessing it from the software system using theRepresentational state transfer (REST) API. Eventually, the suggested modelswere validated through the Area Under the Curve–Receiver CharacteristicOperator (AUC–ROC) curve and Confusion Matrix and compared with theexisting research articles to understand the underlying problem. ThroughComparative Analysis, we have extracted meaningful insights regarding braintumour segmentation and figured out potential gaps. Nevertheless, the proposed model can be adjustable in daily life and the healthcare domain to identify the infected regions and cancer of the brain through various imagingmodalities.

A Secure Image Steganography Using Advanced Encryption Standard and Discrete Cosine Transform

Cryptography and Steganography are two prominent techniques to obtain secure communication over the shared media like the Internet. Steganography is slightly ahead of cryptography because of its stealthy characteristics. In this paper, a new method has been proposed which combines cryptography and steganography to ensure even more secure communication. The Advanced Encryption Standard (AES) in spatial domain of the carrier/cover image and Least Significant Bit (LSB) replacement in the transformed domain of the same image has been used after performing a Discrete Cosine Transform (DCT) on the pixels. An additional layer of security has been introduced by applying XOR operation on the AES encrypted message with the pixel values of the carrier image. The Peak Signal to Noise Ratio (PSNR) of the proposed algorithm is better than most of the similar algorithms. With better PSNR, the proposed method depicts a three layer of security of the information and error free decryption.

Mainstreaming Decentralization and Collaboration in Disaster Risk Management:Insights from Coastal Bangladesh

Bangladesh is renowned in disaster risk reduction(DRR)for active involvement of community people and local disaster management institutions in DRR activities.Our study aimed to describe the disaster risk management(DRM)institutions and assess their functioning in six coastal unions across the three coastal zones of Bangladesh.Both qualitative and quantitative research approaches were used.The study focused on two key local institutions—the Union Disaster Management Committees(UDMCs)and the Cyclone Preparedness Program(CPP)—functioning at the union level in DRM.Such institutions have both horizontal and vertical collaborations with other institutions.However,we argue that the UDMCs'external dependencies in their functioning indicate their limited financial and administrative autonomy,which is a barrier to successfully institutionalizing disaster management.The results show that the CPP is the most successful program,markedly increasing the trust of the people in warning dissemination and evacuation efforts in the event of a cyclone.Although the adoption of decentralized risk management systems has resulted in significant progress in increased rate of evacuation and reduced death rate and damage,lack of funding and equipment,limited coordination between institutions,lack of skilled and knowledgeable workforce,and inappropriate power structures may reduce the effectiveness of DRR activities prior to,during,and following disasters.

Performance analysis of machine learning models for intrusion detection system using Gini Impurity-based Weighted Random Forest (GIWRF) feature selection technique

To protect the network, resources, and sensitive data, the intrusion detection system (IDS) has become a fundamental component of organizations that prevents cybercriminal activities. Several approaches have been introduced and implemented to thwart malicious activities so far. Due to the effectiveness of machine learning (ML) methods, the proposed approach applied several ML models for the intrusion detection system. In order to evaluate the performance of models, UNSW-NB 15 and Network TON_IoT datasets were used for offline analysis. Both datasets are comparatively newer than the NSL-KDD dataset to represent modern-day attacks. However, the performance analysis was carried out by training and testing the Decision Tree (DT), Gradient Boosting Tree (GBT), Multilayer Perceptron (MLP), AdaBoost, Long-Short Term Memory (LSTM), and Gated Recurrent Unit (GRU) for the binary classification task. As the performance of IDS deteriorates with a high dimensional feature vector, an optimum set of features was selected through a Gini Impurity-based Weighted Random Forest (GIWRF) model as the embedded feature selection technique. This technique employed Gini impurity as the splitting criterion of trees and adjusted the weights for two different classes of the imbalanced data to make the learning algorithm understand the class distribution. Based upon the importance score, 20 features were selected from UNSW-NB 15 and 10 features from the Network TON_IoT dataset. The experimental result revealed that DT performed well with the feature selection technique than other trained models of this experiment. Moreover, the proposed GIWRF-DT outperformed other existing methods surveyed in the literature in terms of the F1 score.