CUSBoost: Cluster-based Under-sampling with Boosting for Imbalanced Classification

Class imbalance classification is a challenging research problem in data mining and machine learning, as most of the real-life datasets are often imbalanced in nature. Existing learning algorithms maximise the classification accuracy by correctly classifying the majority class, but misclassify the minority class. However, the minority class instances are representing the concept with greater interest than the majority class instances in real-life applications. Recently, several techniques based on sampling methods (under-sampling of the majority class and over-sampling the minority class), cost-sensitive learning methods, and ensemble learning have been used in the literature for classifying imbalanced datasets. In this paper, we introduce a new clustering-based under-sampling approach with boosting (AdaBoost) algorithm, called CUSBoost, for effective imbalanced classification. The proposed algorithm provides an alternative to RUSBoost (random under-sampling with AdaBoost) and SMOTEBoost (synthetic minority over-sampling with AdaBoost) algorithms. We evaluated the performance of CUSBoost algorithm with the state-of-the-art methods based on ensemble learning like AdaBoost, RUSBoost, SMOTEBoost on 13 imbalance binary and multi-class datasets with various imbalance ratios. The experimental results show that the CUSBoost is a promising and effective approach for dealing with highly imbalanced datasets.Comment: CSITSS-201

MCFFA-Net: Multi-Contextual Feature Fusion and Attention Guided Network for Apple Foliar Disease Classification

Numerous diseases cause severe economic loss in the apple production-based industry. Early disease identification in apple leaves can help to stop the spread of infections and provide better productivity. Therefore, it is crucial to study the identification and classification of different apple foliar diseases. Various traditional machine learning and deep learning methods have addressed and investigated this issue. However, it is still challenging to classify these diseases because of their complex background, variation in the diseased spot in the images, and the presence of several symptoms of multiple diseases on the same leaf. This paper proposes a novel transfer learning-based stacked ensemble architecture named MCFFA-Net, which is composed of three pre-trained architectures named MobileNetV2, DenseNet201, and InceptionResNetV2 as backbone networks. We also propose a novel multi-scale dilated residual convolution module to capture multi-scale contextual information with several dilated receptive fields from the extracted features. Channel-based attention mechanism is provided through squeeze and excitation networks to make the MCFFA-Net focused on the relevant information in the multi-receptive fields. The proposed MCFFA-Net achieves a classification accuracy of 90.86%.Comment: 7 pages, 6 figures, ICCIT 2022 submission, Conferenc

Classification of Human Monkeypox Disease Using Deep Learning Models and Attention Mechanisms

As the world is still trying to rebuild from the destruction caused by the widespread reach of the COVID-19 virus, and the recent alarming surge of human monkeypox disease outbreaks in numerous countries threatens to become a new global pandemic too. Human monkeypox disease syndromes are quite similar to chickenpox, and measles classic symptoms, with very intricate differences such as skin blisters, which come in diverse forms. Various deep-learning methods have shown promising performances in the image-based diagnosis of COVID-19, tumor cell, and skin disease classification tasks. In this paper, we try to integrate deep transfer-learning-based methods, along with a convolutional block attention module (CBAM), to focus on the relevant portion of the feature maps to conduct an image-based classification of human monkeypox disease. We implement five deep-learning models, VGG19, Xception, DenseNet121, EfficientNetB3, and MobileNetV2, along with integrated channel and spatial attention mechanisms, and perform a comparative analysis among them. An architecture consisting of Xception-CBAM-Dense layers performed better than the other models at classifying human monkeypox and other diseases with a validation accuracy of 83.89%.Comment: This paper is currently under review at ICCIT 202

Morphological responses of three contrasting Soybean (Glycine max (L.) Merrill) genotypes under different levels of salinity stress in the coastal region of Bangladesh

Soil salinity, a global environmental issue, inhibits plant development and production. Soybean is an economically important legume crop whose yield and quality are highly affected by excessive levels of salt in the root zone. A factorial experiment was conducted in a net house from October 2019 to January 2020 to evaluate the performance of three distinct soybean genotypes under varying levels of salinity stress. The experiment followed a completely randomized design (CRD) with three replications. Three soybean cultivars, namely BINA Soybean 1, BINA Soybean 2, and BINA Soybean 4 were used in this experiment. The soil salinity treatments were 0 mM NaCl, 50 mM NaCl, 100 mM NaCl, 150 mM NaCl, and 200 mM NaCl. The electrical conductivity (EC) of the soil sample was 0.91dS/m. Six seeds were sown 3 cm deep in each pot. A total of 45 pots were used in this experiment. The performance of each variety was evaluated based on its germination percentage, time of germination, no. of branches/plant, no. of leaves/plant, no. of flowers/plant, plant height (cm), no. of pods/plant, pod length (cm), seeds/pod, and root length (cm). Based on the results obtained from this research trial, it can be inferred that the BINA Soybean 2 variety along with 0 mM NaCl, 50 mM NaCl, and 100 mM NaCl treatments exhibited superior performance in all parameters compared to the other varieties. This study provides clear evidence that the soybean, particularly the BINA Soybean 2 variety, holds significant promise as a crop suitable for coastal regions. Furthermore, it suggests that the cultivation of soybeans in such areas could potentially enhance agricultural productivity, particularly in the presence of mild saline conditions. Nevertheless, it exhibits limited growth potential in environments with elevated salinity levels

Problems and prospects of mobile banking in Bangladesh

The main objective of the study is to find out the problem and prospect of mobile banking in Bangladesh. For this research primary data were used. This study adopts with descriptive in nature. Total respondents were 120 within that 61 % respondents think it saves time than traditional banking, the highest number of respondents use mobile banking for Air-time top-up service, that is 21%, out of 120 respondents 56% replied it is less costlier than traditional banking, 100% respondents did agree that it is speedy, and 38% respondents are upper class. Although this concept is new in Bangladesh but its potentiality is high. From this research, other researchers and policy makers will get an insight about the problems and prospects of mobile banking in Bangladesh. Key words: Problem, prospect, Mobile banking

Customers' attitude towards agro based benefits provided by the telecommunication operators in Bangladesh

The main objective of this research is to find out attitude of users only towards agro based benefits provided by telecommunication operators in Bangladesh. Agriculture is the most important sector of the economy of Bangladesh which provides 63% employment and contributing 18.6% to the national GDP. But unfortunately the farmer's community is one of the most deprived ones in the country and frequent access to information remains one of the crying needs for a long time period. It can enable them to enhance their quality of life. It has been argued that telecommunication operators can come to aid in this respect. Telecommunications operators have already expanded their services and provided specialized agro-based services to the farmers. It also explored the characteristics of the user's and their perception. Data were collected from 120 respondents who were the users of telecommunication operators in Bangladesh. The data were collected through a structured interview schedule. Evidence from interviews suggests that most of the users were young, had little farming experience with small farm size and from small to medium families. These services were treated helpful to overcome their obstacles to information collection but still not efficient like the means they use to collect information traditionally. They want information in various fields of agriculture especially in the area of price, weather information, cultivation technique, disease treatment, fertilizer dose etc. Small farmers found it as a very effective any of information service especially in case of emergency situation and due to its cost effectiveness. But the mechanism need to use this services found sometimes difficult especially for the illiterate farmers. From this research, students other researchers and policy makers will get an insight about the ‘Users attitude towards agro based services provide by the telecommunication operators in Bangladesh. Key Words: Attitude, Agro, Services, Telecommunication, Bangladesh