Multi-modal medical image fusion (MMIF) is increasingly recognized as an essential technique for enhancing diagnostic precision and facilitating effective clinical decision-making within computer-aided diagnosis systems. MMIF combines data from X-ray, MRI, CT, PET, SPECT, and ultrasound to create detailed, clinically useful images of patient anatomy and pathology. These integrated representations significantly advance diagnostic accuracy, lesion detection, and segmentation. This comprehensive review meticulously surveys the evolution, methodologies, algorithms, current advancements, and clinical applications of MMIF. We present a critical comparative analysis of traditional fusion approaches, including pixel-, feature-, and decision-level methods, and delves into recent advancements driven by deep learning, generative models, and transformer-based architectures. A critical comparative analysis is presented between these conventional methods and contemporary techniques, highlighting differences in robustness, computational efficiency, and interpretability. The article addresses extensive clinical applications across oncology, neurology, and cardiology, demonstrating MMIF's vital role in precision medicine through improved patient-specific therapeutic outcomes. Moreover, the review thoroughly investigates the persistent challenges affecting MMIF's broad adoption, including issues related to data privacy, heterogeneity, computational complexity, interpretability of AI-driven algorithms, and integration within clinical workflows. It also identifies significant future research avenues, such as the integration of explainable AI, adoption of privacy-preserving federated learning frameworks, development of real-time fusion systems, and standardization efforts for regulatory compliance. This review organizes key knowledge, outlines challenges, and highlights opportunities, guiding researchers, clinicians, and developers in advancing MMIF for routine clinical use and promoting personalized healthcare. To support further research, we provide a GitHub repository that includes popular multi-modal medical imaging datasets along with recent models in our shared GitHub repository.

Cryptocurrencies, recognized by their extreme volatility due to dependency on multiple direct and indirect factors, offer a significant challenge regarding precise price forecasting. This uncertainty has led to investment hesitation within the digital currency market. Previous research attempts have presented methodologies for price forecasting and trend prediction in cryptocurrencies. However, these forecasts have typically suffered from increased error rates, leaving the opportunity for improvement in this field. Furthermore, the influence of sentiment-based factors could compromise the reliability of price predictions. In this research, we have proposed a machine learning-driven framework that provides precise cryptocurrency price projections and adds an alert mechanism to guide investors. Our fundamental analyzer, Bi-LSTM and GRU hybrid model use historical data of digital currencies to train and reliably anticipate future values. Complementing this, a sentiment analyzer, utilizing a BERT and VADER hybrid model, analyzes sentiments to assess the forecast price as trustworthy or uncertain. Besides assisting investor decision-making, this technique also helps risk management in the dynamic realm of cryptocurrency. Our suggested approach delivers highly precise price predictions with dramatically decreased error rates compared to prior competitive studies. The proposed Bi-LSTM-GRU-BERT-VADER model is tested for three cryptocurrencies, namely BTC, ETH, and Dogecoin and reports an average root mean square error (RMSE) of 0.0241%, 0.0645%, and 0.0978%, respectively.

Cotton is a major cash crop, and increasing its production is extremely important worldwide, especially in agriculture-led economies. The crop is susceptible to various diseases, leading to decreased yields. In recent years, advancements in deep learning methods have enabled researchers to develop automated methods for detecting diseases in cotton crops. Such automation not only assists farmers in mitigating the effects of the disease but also conserves resources in terms of labor and fertilizer costs. However, accurate classification of multiple diseases simultaneously in cotton remains challenging due to multiple factors, including class imbalance, variation in disease symptoms, and the need for real-time detection, as most existing datasets are acquired under controlled conditions. This research proposes a novel method for addressing these challenges and accurately classifying seven classes, including six diseases and a healthy class. We address the class imbalance issue through synthetic data generation using conventional methods like scaling, rotating, transforming, shearing, and zooming and propose a customized StyleGAN for synthetic data generation. After preprocessing, we combine features extracted from MobileNet and VGG16 to create a comprehensive feature vector, passed to three classifiers: Long Short Term Memory Units, Support Vector Machines, and Random Forest. We propose a StackNet-based ensemble classifier that takes the output probabilities of these three classifiers and predicts the class label among six diseases—Bacterial blight, Curl virus, Fusarium wilt, Alternaria, Cercospora, Greymildew—and a healthy class. We trained and tested our method on publicly available datasets, achieving an average accuracy of 97%. Our robust method outperforms state-of-the-art techniques to identify the six diseases and the healthy class.

Urdu is a widely spoken and narrated language in several South-Asian countries and communities worldwide. It is relatively hard to recognize Urdu text compared to other languages due to its cursive writing style. The Urdu text script belongs to a non-Latin cursive family script like Arabic, Hindi and Chinese. Urdu is written in several writing styles, among which ‘Nastaleeq’ is the most popular and widely used font style. A gap still poses a challenge for localization/detection and recognition of Urdu Nastaleeq text as it follows modified version of Arabic script. This research study presents a methodology to recognize and classify Urdu text in Nastaleeq font, regardless of the text position in the image. The proposed solution is comprised of a two-step methodology. In the first step, text detection is performed using the Connected Component Analysis (CCA) and Long Short-Term Memory Neural Network (LSTM). In the second step, a hybrid Convolution Neural Network and Recurrent Neural Network (CNN-RNN) architecture is deployed to recognize the detected text. The image containing Urdu text is binarized and segmented to produce a single-line text image fed to the hybrid CNN-RNN model, which recognizes the text and saves it in a text file. The proposed technique outperforms the existing ones by achieving an overall accuracy of 97.47%.

Integrating environmental features into software requirements during the requirements engineering (RE) process is known as sustainable requirements engineering. Unlike previous studies, we found that there is a strong relationship between nonfunctional requirements and sustainable environmental factors. This study presents a novel methodology correlating nonfunctional requirements (NFRs) with precise, sustainable green IT factors. Our mapping methodology consists of two steps. In the first step, we link sustainability dimensions to the two groups of green IT aspects. In the second step, we connect NFRs to sustainability aspects. Our proposed methodology is based on the extended PROMISE_exp dataset in combination with the Bidirectional Encoder Representations from Transformers (BERT) language model. Moreover, we evaluate the model by inserting a new binary classification column into the dataset to classify the sustainability factors into socio-economic and eco-technical groups. The performance of the model is assessed using four performance metrics: accuracy, precision, recall, and F1 score. With 16 epochs and a batch size of 32, 90% accuracy was achieved. The proposed model indicates an improvement in performance metrics values yielding an increase of 3.4% in accuracy, 3% in precision, 3.4% in recall, and 16% in F1 score values compared to the competitive previous studies. This acts as a proof of concept for automating the evaluation of sustainability realization in software during the initial stages of development.

Spontaneous abortion, commonly known as miscarriage, is a significant concern during early pregnancy. Histopathological examination of tissue samples is a widely used method to diagnose and classify tissue phenotypes found in products of conception (POC) after spontaneous abortion. Background: Histopathological examination is subjective and dependent on the skill and experience of the examiner. In recent years, artificial intelligence (AI)-based techniques have emerged as a promising tool in medical imaging, offering the potential to revolutionize tissue phenotyping and improve the accuracy and reliability of the histopathological examination process. The goal of this study was to investigate the use of AI techniques for the detection of various tissue phenotypes in POC after spontaneous abortion and evaluate the accuracy and reliability of these techniques compared to traditional manual methods. Methods: We present a novel publicly available dataset named HistoPoC, which is believed to be the first of its kind, focusing on spontaneous abortion (miscarriage) in early pregnancy. A diverse dataset of 5666 annotated images was prepared from previously diagnosed cases of POC from Atia General Hospital, Karachi, Pakistan, for this purpose. The digital images were prepared at 10× through a camera-connected microscope by a consultant histopathologist. Results: The dataset’s effectiveness was validated using several deep learning-based models, demonstrating its applicability and supporting its use in intelligent diagnostic systems. Conclusions: The insights gained from this study could illuminate the causes of spontaneous abortion and guide the development of novel treatments. Additionally, this study could contribute to advancements in the field of tissue phenotyping and the wider application of deep learning techniques in medical diagnostics and treatment.