Developing countries face significant challenges in healthcare, education, technological advancement, and farming, with agriculture playing an important role in economic growth. Ensuring adequate food production is essential for citizens' survival and economic stability. Early detection of plant disease is critical for increasing agricultural yields through efficient control methods. As a result, autonomous plant disease detection models are vital for reducing labor-intensive tasks. Machine learning and deep learning models have recently been introduced to automate disease identification in plants by detecting symptoms on their leaves. This study reviews various publications that use machine learning and deep learning approaches to detect plant and crop diseases. This review provides (i) an automated plant disease detection system, (ii) an automated wheat leaf rust detection system, (iii) publicly available plant disease datasets, (iv) an overview of machine learning and deep learning methods for plant disease detection, (v) remote sensing technology in plant disease detection, and (vi) future trends and research directions. This review is contributing to the literature by establishing a solid foundation for the development of more valuable machine learning and deep learning methods for plant disease detection. This review study critically examines various problems, including model generalization, dataset diversity, and computing factors, to assist prospective researchers in developing robust and scalable solutions. Additionally, this review presented a detailed overview of the limitations, challenges, and recent advancements in plant disease detection, especially in wheat leaf rust detection and remote sensing technology. Furthermore, current challenges and future directions in wheat disease detection are thoroughly discussed. This study provides a fundamental guide for the development of AI-driven algorithms, providing strategies to enhance production, profitability, and sustainability against climate change and population growth.

Background: Artificial intelligence (AI) persists as a focal subject within the realm of medical imaging, heralding a multitude of prospective applications that span the comprehensive imaging lifecycle. However, a key hurdle to the development and real-world application of AI algorithms is the necessity for large amounts of well-organized and carefully planned training data, including professional annotations (labelling). Modern supervised AI techniques require thorough data curation to efficiently train, validate, and test models. Objective: The proper processing of medical images for use by AI-driven solutions is a critical component in the development of dependable and resilient AI algorithms. Currently, research organizations and corporate entities frequently confront data access limits, working with small amounts of data from restricted geographic locations. Methods: This study provides an in-depth examination of the publicly accessible datasets in the field of medical imaging. This work also determines the methods required for preparing medical imaging data for the development of AI algorithms, emphasizes current limitations in dataset curation. Furthermore, this study explores inventive strategies to address the challenge of data availability, offering a detailed overview of data curation technologies. Results: This study provides a comprehensive evaluation of medical imaging datasets emphasizes their vital significance in improving diagnostic accuracy and AI models, while also addressing key problems such as dataset diversity, labelling, and ethical implications. Conclusions: The paper concludes with an insightful discussion and analysis of challenges in medical image analysis, along with potential future directions in the field.

Deploying and effectively utilizing wireless sensor networks (WSNs) in underwater habitats remains a challenging task. In underwater wireless sensors networks (UWSNs), the availability of a continuous energy source for communicating with nodes is either very costly or is prohibited due to the marine life law enforcement agencies. So, in order to address this issue, we present a Q-learning-based approach to designing an energy-efficient medium access control (MAC) protocol for UWSNs through collision avoidance. The main goal is to prolong the network’s lifespan by optimizing the communication methods, specifically focusing on improving the energy efficiency of the MAC protocols. Factors affecting the energy consumption in communication are adjustments to the interference ranges, i.e., changing frequencies repeatedly to obtain optimal communication; data packet retransmissions in case of a false acknowledgment; and data packet collision occurrences in the channel. Our chosen protocol stands out by enabling sensor (Rx) nodes to avoid collisions without needing extra communication or prior interference knowledge. According to the results obtained through simulations, our protocol may increase the network’s performance in terms of network throughput by up to 23% when compared to benchmark protocols depending on the typical traffic load. It simultaneously decreases end-to-end latency, increases the packet delivery ratio (PDR), boosts channel usage, and lessens packet collisions by over 38%. All these gains result in minimizing the network’s energy consumption, with a proportional gain.

Historical documents such as newspapers, invoices, contract papers are often difficult to read due to degraded text quality. These documents may be damaged or degraded due to a variety of factors such as aging, distortion, stamps, watermarks, ink stains, and so on. Text image enhancement is essential for several document recognition and analysis tasks. In this era of technology, it is important to enhance these degraded text documents for proper use. To address these issues, a new bi-cubic interpolation of Lifting Wavelet Transform (LWT) and Stationary Wavelet Transform (SWT) is proposed to enhance image resolution. Then a generative adversarial network (GAN) is used to extract the spectral and spatial features in historical text images. The proposed method consists of two parts. In the first part, the transformation method is used to de-noise and de-blur the images, and to increase the resolution effects, whereas in the second part, the GAN architecture is used to fuse the original and the resulting image obtained from part one in order to improve the spectral and spatial features of a historical text image. Experiment results show that the proposed model outperforms the current deep learning methods.

Automated paraphrase detection is crucial for natural language processing (NL) applications like text summarization, plagiarism detection, and question-answering systems. Detecting paraphrases in Urdu text remains challenging due to the language’s complex morphology, distinctive script, and lack of resources such as labelled datasets, pre-trained models, and tailored NLP tools. This research proposes a novel bidirectional long short-term memory (BiLSTM) framework to address Urdu paraphrase detection’s intricacies. Our approach employs word embeddings and text preprocessing techniques like tokenization, stop-word removal, and label encoding to effectively handle Urdu’s morphological variations. The BiLSTM network sequentially processes the input, leveraging both forward and backward contextual information to encode the complex syntactic and semantic patterns inherent in Urdu text. An essential contribution of this work is the creation of a large-scale Urdu Paraphrased Corpus (UPC) comprising 400,000 potential sentence pair duplicates, with 150,000 pairs manually identified as paraphrases. Our findings reveal a significant improvement in paraphrase detection performance compared to existing methods. We provide insights into the underlying linguistic features and patterns that contribute to the robustness of our framework. This resource facilitates training and evaluating Urdu paraphrase detection models. Experimental evaluations on the custom UPC dataset demonstrate our BiLSTM model’s superiority, achieving 94.14% accuracy and outperforming state-of-the-art methods like CNN (83.43%) and LSTM (88.09%). Our model attains an impressive 95.34% accuracy on the benchmark Quora dataset. Furthermore, we incorporate a comprehensive linguistic rule engine to handle exceptional cases during paraphrase analysis, ensuring robust performance across diverse contexts.