Retinopathy is a group of retinal disabilities that causes severe visual impairments or complete blindness. Due to the capability of optical coherence tomography to reveal early retinal abnormalities, many researchers have utilized it to develop autonomous retinal screening systems. However, to the best of our knowledge, most of these systems rely only on mathematical features, which might not be helpful to clinicians since they do not encompass the clinical manifestations of screening the underlying diseases. Such clinical manifestations are critically important to be considered within the autonomous screening systems to match the grading of ophthalmologists within the clinical settings. To overcome these limitations, we present a novel framework that exploits the fusion of vision language correlation between the retinal imagery and the set of clinical prompts to recognize the different types of retinal disabilities. The proposed framework is rigorously tested on six public datasets, where, across each dataset, the proposed framework outperformed state-of-the-art methods in various metrics. Moreover, the clinical significance of the proposed framework is also tested under strict blind testing experiments, where the proposed system achieved a statistically significant correlation coefficient of 0.9185 and 0.9529 with the two expert clinicians. These blind test experiments highlight the potential of the proposed framework to be deployed in the real world for accurate screening of retinal diseases.

This study presents a performance comparison of different deep machine learning models for the intelligent segregation of date fruit bunches of numerous varieties. The shape, size, and color of the various types of dates with different maturity conditions require experienced farmers to estimate the accrual class. This study has used a transfer learning approach on various machine learning models (VGG-19, MobileNetV2, DenseNet, and NASNet) to classify five different kinds of date bunches (Barhi, Khalas, Menifi, Nabout saif, and Sullaj). Each model is trained on a dataset of 11,944 images of five different date classes. The comparison deduces that DenseNet produces the best accuracy, precision, and recall when compared with the performance of other models. The maximum achieved accuracy of the trained DenseNet model is $100 \%$ for the validation dataset and 98.4% for the test dataset.

This study presents the design and development of a light fidelity (Li-Fi) technology-based smart rover for sustainable monitoring of marine environments. A rover equipped with Li-Fi transmitters, receivers, and environmental monitoring sensors (pH and temperature) is designed for underwater navigation and environmental data collection. Data transmission between the rover (transmitter) and a receiver is achieved by aligning a laser and a photo-resistor in a direct line-of-sight configuration. The smart-designed rover has 6 degrees of freedom (DoF) movement capabilities, allowing it to continuously and effectively gather important marine environmental data using integrated sensors. The rover’s movements are controlled through an externally designed mobile application, which enhances operational flexibility. This application also offers real-time sensor data visualization, facilitating comprehensive data analysis and monitoring. The performance of the realized rover was tested in different environments using a commercial pool setup. The recorded readings of pH levels and temperature were in good agreement with the change in the water quality. Integrating Li-Fi technology into marine environment monitoring offers significant potential for revolutionizing underwater communication and advancing environmental sustainability.

Wildlife conservation is a pressing global concern, with illegal poaching posing a severe threat to many endangered species. In recent years, advanced technologies like machine learning and digital signal processing have shown significant potential in supporting conservation efforts by detecting and preventing illegal poaching. This work explores the integration of these technologies into wildlife conservation strategies, focusing on their role in identifying and combating poachers. Two datasets are utilized: ‘Poacher Detection 3 Classes” and “Illegal Poacher Detection”. Several machine learning models including Support Vector Machines (SVM), Random Forest (RF), Decision Trees (DT), and Convolutional Neural Networks (CNN) are applied to detect illegal activities in these datasets. For the first dataset, the SVM model achieved the best accuracy of ${9 3 \%}$, while the CNN model performed best on the second dataset, achieving accuracy of $75 \%$. In the final phase, both datasets were combined, and augmented images were introduced to increase data diversity. On the combined dataset, the Random Forest model achieved the highest accuracy of ${8 3 \%}$. These results demonstrate the effectiveness of machine learning in improving wildlife conservation efforts.

This paper presents a thorough review of recent advancements in screening heart and lung diseases via auscultation using artificial intelligence (AI) methods. Auscultation has historically been fundamental in diagnosing cardiopulmonary conditions; however, conventional techniques depend significantly on clinician proficiency, rendering diagnosis vulnerable to human error. Recent advancements in digital stethoscopes and AI-based sound analysis algorithms have transformed the conventional analysis, facilitating more precise, real-time identification of anomalies such as murmurs, arrhythmias, wheezes, and crackles. This paper delineates the principal methodologies employed in sound acquisition, feature extraction, and disease classification, while assessing the reliability of diverse models of machine learning and deep learning. Moreover, the paper addresses the obstacles in implementing these technologies in clinical practice, including data standardization, computational constraints, and integration with current healthcare systems. The results indicate that AI-augmented stethoscope systems have significant potential to enhance early diagnosis and patient outcomes for cardiac and pulmonary conditions.

This work proposes a flexible, foldable, lightweight, robust, and smart cane to aid visually impaired individuals and patients in navigating unfamiliar environments safely. The developed smart cane is equipped with an ultrasonic sensor, RFID reader, and an AI machine vision sensor for object detection and identification in the range of 30 cm to 9 m. A mobile application with a text-to-voice feature is developed that connects with the cane using Bluetooth to provide voice warnings to the user based on processed sensor output. The developed cane is also equipped with a vibration sensor activated when an obstacle is closer than 30 cm, serving as a backup of voice warnings. Object detection and classification using multiple sources (AI vision sensor, ultrasonic sensor, and RFID readers) ensures robust and uninterrupted operation in case of failure of one sensor. In emergencies, users can send text messages with their location to a pre-selected caretaker’s phone number using the installed SOS button. The rechargeable battery offers more than 24 hours of operation and warns users when the battery is low. The prototype has been successfully executed and tested in various environments and aims to improve the safety and quality of life for visually impaired individuals and hospital patients.

Breast cancer is a prevalent and diverse type of cancer that exhibits unique clinicopathologic characteristics, making the correct identification of its subtype critical to providing targeted treatment and increasing survival rates. This identification process involves testing for the presence of four key molecular biomarkers, namely estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and antigen Ki67. For accurate diagnosis ,the expertise of a pathologist and immunohistochemistry is required. To overcome this diagnostic challenge, we present a novel approach based on a deep learning pipeline for automated classification. Our approach can detect tumor and non-tumoral regions of the HER2 biomarker. Our deep learning framework comprises a Dense Convolutional Network (DenseNet), which process whole slide images (WSIs) of breast tissues, dividing them into patches for input into the network. Moreover, our approach provides both patchwise and pixelwise classification and analyzes ten WSIs of breast cancer histology. Our proposed approach generates an image map that classifies slide images on the pixel-level, detecting the status of hormone HER2 receptor as either positive or negative. The obtained results show that our deep learning-based approach has the potential to enhance the pathologist’s capabilities in diagnosing histopathological images with automated classification.

Prostate cancer (PC) is the most common cancer, a significant cause of morbidity, and is the second vital cancer that causes death in the US. Early PC detection is one of the major factors in decreasing mortality. We introduce a deep learning (DL) system for automated Gleason system grading (Gleason pattern (GP) and Gleason score (GS)) and grade groups (GG) using whole slide images (WSIs) of the digitized prostate biopsy specimens (PBSs). The DL is a pyramidal convolution neural network (CNN) approach consisting of progressively larger patch-sized shallow CNN to provide hierarchical information features. We used three patches sizes 100×100 (small), 150×150 (median), and 200×200 (large) pixels, so the pyramidal CNN affords us varying contextual features. The small patches give more local information, while the large patches provide global features. The patch-wise classification yields five probabilities representing the GP types from 1 to 5 at each pyramidal level. Then, we get the average for those three levels. We used three metrics to evaluate the GP classification diagnostic: recall, accuracy, and precision. The classification accuracy for the CNNL(large patches) is 0.77, the best among the three CNNs. The GG results are between 50% to 75% for recall. GG’s results are highlighted in our DL systems by comparing them with the current work.

Early detection and diagnosis of thyroid nodules are very important to rescue patients before the cancer spreads all over the patient’s body. A computer-aided diagnosis (CAD) system is proposed to detect the malignancy of thyroid nodules using magnetic resonance imaging (MRI) scans. This system extracts three descriptive features from T2-weighted (T2) MRI. These features are 1st-order reflectivity, 2nd-order reflectivity, and spherical harmonic. The 1st-order reflectivity is represented by sufficient statistics, (i.e. CDF percentiles), extracted from the cumulative distribution function (CDF) generated from it. After-ward, these features are fed to a neural network (NN) individually for diagnosis. Then, the classification outputs for these networks are fused using another NN for final diagnosis. The developed system is trained and tested using leave-one-subject-out (LOSO) cross-validation technique on MRI scans from 63 patients. The proposed fusion system shows incredible improvements in diagnostic accuracy, compared with other machine learning approach and a well-know pretrained deep learning network as well as individual feature classification. The overall sensitivity, specificity, F1-score, and accuracy of the proposed system are 91.3%, 95%, 91.3%, and 93.65%, respectively. The reported results, based on the fusion of reflectivity features as well as morphological feature, show the promise of the developed system in differentiating between benign and malignant thyroid nodules.