Rice is considered one the most important plants globally because it is a source of food for over half the world’s population. Like other plants, rice is susceptible to diseases that may affect the quantity and quality of produce. It sometimes results in anywhere between 20–40% crop loss production. Early detection of these diseases can positively affect the harvest, and thus farmers would have to be knowledgeable about the various disease and how to identify them visually. Even then, it is an impossible task for farmers to survey the vast farmlands on a daily basis. Even if this is possible, it becomes a costly task that will, in turn, increases the price of rice for consumers. Machine learning algorithms fitted to drone technology combined with the Internet of Things (IoT) can offer a solution to this problem. In this paper, we propose a Deep Convolutional Neural Network (DCNN) transfer learning-based approach for the accurate detection and classification of rice leaf disease. The modified proposed approach includes a modified VGG19-based transfer learning method. The proposed modified system can accurately detect and diagnose six distinct classes: healthy, narrow brown spot, leaf scald, leaf blast, brown spot, and bacterial leaf blight. The highest average accuracy is 96.08% using the non-normalized augmented dataset. The corresponding precision, recall, specificity, and F1-score were 0.9620, 0.9617, 0.9921, and 0.9616, respectively. The proposed modified approach achieved significantly better results compared with similar approaches using the same dataset or similar-size datasets reported in the extant literature.

Diabetic Retinopathy (DR) is a medical condition present in patients suffering from long-term diabetes. If a diagnosis is not carried out at an early stage, it can lead to vision impairment. High blood sugar in diabetic patients is the main source of DR. This affects the blood vessels within the retina. Manual detection of DR is a difficult task since it can affect the retina, causing structural changes such as Microaneurysms (MAs), Exudates (EXs), Hemorrhages (HMs), and extra blood vessel growth. In this work, a hybrid technique for the detection and classification of Diabetic Retinopathy in fundus images of the eye is proposed. Transfer learning (TL) is used on pre-trained Convolutional Neural Network (CNN) models to extract features that are combined to generate a hybrid feature vector. This feature vector is passed on to various classifiers for binary and multiclass classification of fundus images. System performance is measured using various metrics and results are compared with recent approaches for DR detection. The proposed method provides significant performance improvement in DR detection for fundus images. For binary classification, the proposed modified method achieved the highest accuracy of 97.8% and 89.29% for multiclass classification.

Machine learning in Education is receiving more attention from researchers as the number of students at all levels globally is increasing. To ensure students’ success in K-12 educational institutions and higher education institutions work needs to be done to assist students, teachers/professors, parents, and all stakeholders to provide the support that students need. The need and motivation for such systems are very well-established and thus the aim of this work is to develop a system based on modified machine learning models to automatically predict students’ performance and subsequently identify students at risk. The DEEDs dataset is used in this study. Novel features were extracted and applied to well-known classifiers some of which are ensemble classifiers. These classifiers were also combined with base learners such as bagging and boosting. The problem was divided into three scenarios; binary classification of the pass and fail, three class scenarios, and four class scenarios. It was shown that ensemble methods combined with base learners of boosting and bagging significantly increase the accuracy for binary classification, slightly increase accuracy for three class problems, and have no significance in increasing the accuracy when the problem is 4 classes. The ensemble algorithm of bagging and boosting FDT achieved an accuracy of 98.25% for binary classification and 89.47% for three classes. The standard ensemble FDT achieved an accuracy of 77.19% for four classes. The results obtained for binary classification were compared with results reported in the extant literature using the same dataset proving that the proposed modified algorithms achieved better results than similarly proposed methods. The three-class and four-class results could not be compared because according to the author’s knowledge, there are no research papers published for the same dataset for multi-class classification.

Background/Objectives: Breast cancer is among the most frequently diagnosed cancers and leading cause of mortality worldwide. The accurate classification of breast cancer from the histology photographs is very important for the diagnosis and effective treatment planning. Methods: In this article, we propose a DenseNet121-based deep learning model for breast cancer detection and multi-class classification. The experiments were performed using whole-slide histopathology images collected from the BreakHis dataset. Results: The proposed method attained state-of-the-art performance with a 98.50% accuracy and an AUC of 0.98 for the binary classification. In multi-class classification, it obtained competitive results with 92.50% accuracy and an AUC of 0.94. Conclusions: The proposed model outperforms state-of-the-art methods in distinguishing between benign and malignant tumors as well as in classifying specific malignancy subtypes. This study highlights the potential of deep learning in breast cancer diagnosis and establishes the foundation for developing advanced diagnostic tools.

Visual impairment should not hinder an individual from achieving their aspirations, nor should it be a hindrance to their contributions to society. The age in which persons with disabilities were treated unfairly is long gone, and individuals with disabilities are productive members of society nowadays, especially when they receive the right education and are given the right tools to succeed. Thus, it is imperative to integrate the latest technologies into devices and software that could assist persons with disabilities. The Internet of Things (IoT), artificial intelligence (AI), and Deep Learning (ML)/deep learning (DL) are technologies that have gained momentum over the past decade and could be integrated to assist persons with disabilities—visually impaired individuals. In this paper, we propose an IoT-based system that can fit on the ring finger and can simulate the real-life experience of a visually impaired person. The system can learn and translate Arabic and English braille into audio using deep learning techniques enhanced with transfer learning. The system is developed to assist both visually impaired individuals and their family members in learning braille through the use of the ring-based device, which captures a braille image using an embedded camera, recognizes it, and translates it into audio. The recognition of the captured braille image is achieved through a transfer learning-based Convolutional Neural Network (CNN).

This study investigates the perceptions of university students in Pakistan on the use of ChatGPT in learning. It aims to understand the students’ views on the benefits, challenges, and overall impact of ChatGPT on their academic experiences. A quantitative approach was employed, collecting data from 400 students across various universities in Pakistan through a structured questionnaire. The validity and reliability of the questionnaire were ensured through pilot testing and statistical analysis. The study found that while most students perceive ChatGPT as a beneficial tool for improving their learning, significant concerns remain about academic integrity, over-reliance on artificial intelligence (AI), and the accuracy of information provided by ChatGPT. The findings suggest that perceptions vary across demographic factors such as age, gender, and type of university. These findings can benefit policymakers and educational institutions in effectively integrating AI tools such as ChatGPT into educational systems, ensuring that the value is maximized while addressing potential disadvantages. The study contributes to the existing body of knowledge on AI utilization in education, particularly within the context of developing countries.