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Md. Rajibul Islam, Md. Asif Mahmod Tusher Siddique, Md Amiruzzaman, M. Abdullah-Al-Wadud, Shah Murtaza Rashid Al Masud and Aloke Kumar Saha Abstract: Leaf disease in tomatoes is one of the most common and treacherous diseases. It directly affects the production of tomatoes, resulting in enormous economic loss each year. As a result, studying the detection of tomato leaf diseases is essential. To that aim, this work introduces a novel mechanism for selecting the most effective hyperparameters for improving the detection accuracy of deep CNN. Several cutting-edge CNN algorithms were examined in this study to diagnose tomato leaf diseases. The experiment is divided into three stages to find a full proof technique. A few pre-trained deep convolutional neural networks were first employed to diagnose tomato leaf diseases. The superlative combined model has then experimented with changes in the learning rate, optimizer, and classifier to discover the optimal parameters and minimize overfitting in data training. In this case, 99.31% accuracy was reached in DenseNet 121 using AdaBound Optimizer, 0.01 learning rate, and Softmax classifier. The achieved detection accuracy levels (above 99%) using various learning rates, optimizers, and classifiers were eventually tested using K-fold cross-validation to get a better and dependable detection accuracy. The results indicate that the proposed parameters and technique are efficacious in recognizing tomato leaf disease and can be used fruitfully in identifying other leaf diseases. Keywords: Convolutional Neural Network; Deep Learning; Disease Recognition; Multi-label Classification; Tomato Leaves. |
Razuan Karim, Mukter Zaman and Wong H. Yong Abstract: Neonatal jaundice is a frequent cause of substantial illness and mortality in newborns. The newborn infant’s skin, eyes, and other tissues turn yellow because bilirubin contains a pigment or coloring. Jaundice that manifests in the first few days is highly dangerous and typically needs to be treated right away. It is typically “physiologic” when jaundice emerges on the second or third day. Hyperbilirubinemia refers to an abnormally high bilirubin level in the blood. During the decomposition of red blood cells, bilirubin is formed. Bilirubin can build up in the blood, bodily fluids, and other tissues of newborn babies because they are not naturally able to expel it. Kernicterus or irreversible brain damage can result from untreated jaundice if the abnormally high levels of bilirubin are not controlled. In cases of neonatal jaundice, there is currently a variety of estimating methods for measuring bilirubin levels. The goal of this research is to provide a thorough evaluation of various non-invasive frameworks for the identification of newborn jaundice. For this review article, a critical analysis has done by using 51 articles from 2009 to 2022 where all articles are based on the detection of neonatal jaundice. This literary work on non-invasive methods and neonatal jaundice results appear to be an understanding of the avant-garde procedures created and used in this domain. The review also compares and contrasts different non-invasive strategies for predicting an infant’s state of serum bilirubin based on different data such as social media data, and clinical data. At last, the open issues and future challenges of using a non-invasive method to better understand as well as diagnose the neonatal jaundice state of any individual were discussed. From the literature study, usually apparent that the utilization of non-invasive methods in neonatal jaundice has yielded noteworthy fulfillment within the regions of diagnosis, support, research, and clinical governance. Keywords: Bilirubin; Hyperbilirubinemia; Kernicterus; Neonatal Jaundice; Newborn. |
Stacked Ensemble-Based Type-2 Diabetes Prediction Using Machine Learning Techniques Md Abdur Rahim, Md Alfaz Hossain, Md Najmul Hossain, Jungpil Shin and Keun Soo Yun Abstract: Diabetes is a long-term disease caused by the human body's inability to make enough insulin or to use it properly. This is one of the curses of the present world. Although it is not very severe in the initial stage, over time, it takes a deadly shape and gradually affects a variety of human organs, such as the heart, kidney, liver, eyes, and brain, leading to death. Many researchers focus on the machine and in-depth learning strategies to efficiently predict diabetes based on numerous risk variables such as insulin, BMI, and glucose in this healthcare issue. We proposed a robust approach based on the stacked ensemble method for predicting diabetes using several machine learning (ML) methods. The stacked ensemble comprises two models: the base model and the meta-model. Base models use a variety of models of ML, such as Support Vector Machine (SVM), K Nearest Neighbor (KNN), Naïve Bayes (NB), and Random Forest (RF), which make different assumptions about predictions, and meta-models make final predictions using Logistic Regression from predictive outputs from base models. To assess the efficiency of the proposed model, we have considered the PIMA Indian Diabetes Dataset (PIMA-IDD). We used linear and stratified sampling to ensure dataset consistency and K-fold cross-validation to prevent model overfitting. Experiments revealed that the proposed stacked ensemble model outperformed the model specified in the base classifier as well as the comprehensive methods, with an accuracy of 94.17%. Keywords: Base and Meta Model; Diabetes Type 2; Machine Learning Techniques; Stacked Ensemble. |
A Predictive Cyber Threat Model for Mobile Money Services Mistura Laide Sanni, Bodunde Odunola Akinyemi, Dauda Akinwuyi Olalere, Emmanuel Ajayi Olajubu and Ganiyu Adesola Aderounmu Abstract: Mobile Money Services (MMS), enabled by the wide adoption of mobile phones, offered an opportunity for financial inclusion for the unbanked in developing nations. Meanwhile, the risks of cybercrime are increasing, becoming more widespread, and worsening. This is being aggravated by the inadequate security practises of both service providers and the potential customers' underlying criminal intent to undermine the system for financial gain. Predicting potential mobile money cyber threats will afford the opportunity to implement countermeasures before cybercriminals explore this opportunity to impact mobile money assets or perpetrate financial cybercrime. However, traditional security techniques are too broad to address these emerging threats to Mobile Financial Services (MFS). Furthermore, the existing body of knowledge is not adequate for predicting threats associated with the mobile money ecosystem. Thus, there is a need for an effective analytical model based on intelligent software defence mechanisms to detect and prevent these cyber threats. In this study, a dataset was collected via interview with the mobile money practitioners, and a Synthetic Minority Oversampling Technique (SMOTE) was applied to handle the class imbalance problem. A predictive model to detect and prevent suspicious customers with cyber threat potential during the onboarding process for MMS in developing nations using a Machine Learning (ML) technique was developed and evaluated. To test the proposed model's effectiveness in detecting and classifying fraudulent MMS applicant intent, it was trained with various configurations, such as binary or multiclass, with or without the inclusion of SMOTE. Python programming language was employed for the simulation and evaluation of the proposed model. The results showed that ML algorithms are effective for modelling and automating the prediction of cyber threats on MMS. In addition, it proved that the logistic regression classifier with the SMOTE application provided the best classification performance among the various configurations of logistic regression experiments performed. This classification model will be suitable for secure MMS, which serves as a key deciding factor in the adoption and acceptance of mobile money as a cash substitute, especially among the unbanked population. Keywords: Cyberspace; Machine Learning; Mobile Money; Predictive Model; Threats. |
The Theory of Probabilistic Hierarchical Learning for Classification Ziauddin Ursani and Ahsan Ahmad Ursani Abstract: Providing the ability of classification to computers has remained at the core of the faculty of artificial intelligence. Its application has now made inroads towards nearly every walk of life, spreading over healthcare, education, defence, economics, linguistics, sociology, literature, transportation, agriculture, and industry etc. To our understanding most of the problems faced by us can be formulated as classification problems. Therefore, any novel contribution in this area has a great potential of applications in the real world. This paper proposes a novel way of learning from classification datasets i.e., hierarchical learning through set partitioning. The theory of probabilistic hierarchical learning for classification has been evolved through several works while widening its scope with each instance. The theory demonstrates that the classification of any dataset can be learnt by generating a hierarchy of learnt models each capable of classifying a disjoint subset of the training set. The basic assertion behind the theory is that an accurate classification of complex datasets can be achieved through hierarchical application of low complexity models. In this paper, the theory is redefined and revised based on four mathematical principles namely, principle of successive bifurcation, principle of two-tier discrimination, principle of class membership and the principle of selective data normalization. The algorithmic implementation of each principle is also discussed. The scope of the approach is now further widened to include ten popular real-world datasets in its test base. This approach does not only produce their accurate models but also produced above 95% accuracy on average with regard to the generalising ability, which is competitive with the contemporary literature. Keywords: Classification; Hierarchical learning; Probabilistic learning; Set partitioning; Supervised learning. |
Similarity Detection of Time-Sensitive Online News Articles Based on RSS Feeds and Contextual Data Mohammad Daoud Abstract: This article tackles the problem of finding similarity between web time-sensitive news articles, which can be a challenge. This challenge was approached with a novel methodology that uses supervised learning algorithms with carefully selected features (Semantic, Lexical and Temporal features (content and contextual features)). The proposed approach considers not only the textual content, which is a well-studied approach that may yield misleading results, but also the context, community engagement, and community-deduced importance of that news article. This paper details the major procedures of title pair pre-processing, analysis of lexical units, feature engineering, and similarity measures. Thousands of web articles are being published every second, and therefore, it is essential to determine the similarity of these articles efficiently without wasting time on unnecessary text processing of the bodies. Hence, the proposed approach focuses on short contents (titles) and context. The conducted experiment showed high precision and accuracy on a Really Simple Syndication (RSS) dataset of 8000 Arabic news article pairs collected automatically from 10 different news sources. The proposed approach achieved an accuracy of 0.81. Contextual features increased the accuracy and the precision. The proposed algorithm achieved a 0.89 correlation with the evaluations of two human judges based on Pearson’s Correlation Coefficient. The results outperform the state-of-the-art systems on Arabic news articles. Keywords: Arabic NLP; News aggregators; Recommendation systems; Semantic similarity; Web personalization. |
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