This study introduces a fully automated collision detection and notification system specifically engineered to safeguard parked vehicles against accidental impacts in densely populated areas such as commercial parking lots and urban streets. The system employs an integrated network of front and rear cameras, proximity sensors, and vibration sensors to provide continuous environmental monitoring around a stationary vehicle. When a foreign object or vehicle encroaches within a predefined proximity, the system initiates real-time surveillance by activating on-board cameras. Simultaneously, visual alert mechanisms, such as high-intensity flashing lights, are triggered to attract the attention of nearby drivers and prevent potential collisions. In the event of physical contact, the system immediately begins continuous video recording, capturing high-resolution footage of the incident. This evidence is securely transmitted to the vehicle owner's mobile device via a dedicated application, delivering instant notification and remote access to the recorded material. The design emphasizes affordability and accessibility, ensuring that advanced vehicle protection is available to a broad user base. By combining proactive collision deterrence with post-incident documentation and real-time communication, the proposed system offers a comprehensive and practical solution to mitigate the risk and consequences of parked vehicle collisions. Experimental validation confirms the system's reliability, responsiveness, and effectiveness in real-world parking scenarios, demonstrating its value as a robust enhancement to vehicular safety infrastructure.

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.

Diabetic retinopathy (DR), a leading cause of vision loss among individuals with diabetes, necessitates accurate and timely diagnosis for effective management. This paper evaluates two classification models: the gray-level co-occurrence matrix (GLCM) and the convolutional neural network (CNN) ResNet-50 architecture, for automated DR diagnosis. The study employs retinal images from Kaggle and Zenodo datasets, assesses model performance, and optimizes the ResNet-50 parameters to enhance classification accuracy. The results demonstrate the superior performance of ResNet-50 compared to GLCM. The achieved accuracies for distinguishing normal and diabetic retinal images are $\mathbf{9 7. 8 8 9 \%}$ and $\mathbf{9 2. 0 5 3} \%$, based on Kaggle and Zenodo datasets, respectively. This indicates a robust performance of ResNet- 50 in multi-class classification tasks and highlights its potential for improving DR diagnosis systems. These findings underscore the significance of advanced computational techniques in early DR detection, offering enhanced diagnostic efficiency and potentially alleviating healthcare burdens.

In recent years, the proliferation of data and sensor measurements in various scientific fields, particularly within the realm of the Internet of Things, has opened new avenues for knowledge extraction through advanced data analysis techniques. However, the presence of outliers and anomalies poses significant challenges, leading to inaccuracies that can compromise analytical outcomes. Outliers are defined as data points that deviate markedly from other observations, often resulting from measurement errors or inconsistencies within the dataset. Their detection and removal during the data cleaning process are crucial for enhancing data quality and ensuring robust analysis. This study systematically investigates the impact of outliers and their detection on the accuracy and performance of various machine learning algorithms and statistical models in regression and classification tasks. A series of MATLAB simulations is conducted on standard datasets to evaluate the effects of outliers and validate the performance of different methodologies. The findings highlight the critical importance of effective outlier detection, demonstrating a marked improvement in the accuracy and reliability of analytical results.

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 paper presents the use of a circular Complementary Split Ring Resonator (CSRR) sensor for water quality assessment. The sensor is used particularly to detect the PH-change and Total Dissolved Solids (TDS) as main contaminants of water. Two circular CSRR are designed at two different frequencies namely, at 1 GHz and 3 GHz, respectively to measure the sensitivity to multiple water samples of different pH and TDS levels. The results showed improved sensitivity for pH measurement as compared to other recent CSRR sensors published in the open scientific literature.

ChatGPT is an extensive language model under the umbrella of generative artificial intelligence that produces answers from data and images curated from online resources. Despite the capability to produce accurate responses, but requires verification; the responses are based on statistical patterns rather than true comprehension, i.e., it does not have consciousness and does not understand the questions from the perspective of human comprehension. The ability of ChatGPT to understand and react to questions in a humanistic way has garnered a lot of public and scientific interest over the past year. This study analyzes responses of ChatGPT to 100 questions on epilepsy in order to assess the validity of the tool in this field. Besides, this work sheds light on the advantages and disadvantages of the approach in this particular topic by analyzing responses of ChatGPT to queries on epilepsy. The study evaluates the model performance by looking at the completeness, correctness, and relevancy of responses. The findings in this paper indicate that ChatGPT has limits because of its training data and design structure, even though it could give insightful and appropriate answers to inquiries about epilepsy. It is concluded that ChatGPT can be an advantageous tool for medical professionals working on the subject of epilepsy. Nonetheless, it should be noted that ChatGPT should be utilized cautiously and in conjunction with various information sources, like clinical practice guidelines and peer-reviewed studies.

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.

Deception detection plays a vital role in various domains, from security and law enforcement to human behavior analysis. In this paper, we propose a comprehensive system for deception detection that leverages S&A smart sensing device, deep transfer learning, deep learning techniques, and explainable artificial intelligence. Our approach combines visual, auditory, thermal, cardiovascular, and respiratory cues, offering enhanced accuracy and resistance to countermeasures. Deep Transfer Learning is employed to adapt pre-trained models to the deception detection task, overcoming data limitations. Incorporating Explainable AI techniques enhances transparency and interpretability, fostering trust and collaboration in human-machine interactions. Our research lays the groundwork for future advancements in deception detection technology, addressing challenges and providing promising opportunities in the realm of deception detection.

This paper introduces a simple and efficient Microwave Imaging (MWI) setup for detecting fractures in superficial bones, specifically in the tibia. This setup holds promise for the use by first-responders in swiftly assessing fractures in emergency scenarios where X-ray equipment may not be readily available or recommended. The key component of this setup is a single wearable monopole antenna, employed to linearly scan the bone across an ultra-wideband frequency range of 8.5 GHz (3.5-12 GHz), with a maximum gain of 5.7 dBi and an efficiency of more than 90%. The antenna system is designed to fit the human body shape without experiencing any performance degradation as compared to the conventional planar antenna counterpart. The practicality of the proposed antenna is demonstrated through simulations involving a bone model, wherein the distribution of electric fields (E-fields) inside the bone is examined. The reconstructed images resulting from these simulations underscore the potential of this conceptual model as a portable platform for efficiently detecting and pinpointing 1 mm fractures within bones by utilizing the extracted field distributions at 4.7 GHz and at 7.9 GHz.