Abstract The navigation of emergency vehicles is a critical component of effective emergency response in a smart city. To improve response time, it is necessary to have a navigation system that can predict the shortest path to the destination and adjust in real time to current traffic conditions. This paper proposes a smart navigation system for emergency vehicles (SNSEV), a real-time navigation algorithm for emergency vehicles in smart cities that utilize fog and cloud computing technology for real-time traffic management. IoT devices such as sensors and cameras collect real-time data on traffic conditions and roadblocks, which is then processed and analyzed using fog computing technology. Cloud computing technology is then utilized to provide emergency vehicles with real-time navigation and priority control, reducing response time and ensuring that they reach their destination as quickly as possible. This paper presents the proposed SNSEV algorithm and the system architecture and discusses its potential benefits and challenges. The results show that SNSEV can significantly improve the efficiency and effectiveness of emergency response in a smart city, leading to improved public safety and well-being. According to the findings of several experiments, SNSEV works better than its competitors because it allows for the highest possible throughput, the lowest possible bandwidth usage, and the shortest possible delay.

Abstract Plant diseases can cause severe losses in agricultural production, impacting food security and safety. Early detection of plant diseases is crucial to minimize crop damage and ensure agricultural sustainability. Manual monitoring is often impractical due to the complexity and time involved, making automated disease recognition essential. This study presents a new Plant Disease Detection Algorithm (PDDA) called DeepLeaf focused on identifying four common grapevine diseases: leaf blight, black rot, stable, and black measles. The PDDA integrates three key modules: an Image Preprocessing Module, a Feature Extraction Module, and an Optimized Convolutional Neural Network (OCNN)-based Classification Module. The OCNN forms the core of the classification system, with its hyperparameters fine-tuned using fuzzy optimization to enhance performance. Preprocessing techniques are applied to analyze diseased leaves, and a logistic regression algorithm is used to downsample the features for better analysis. The CNN is trained on images from the Plant Village dataset, allowing it to detect and classify grapevine leaf diseases accurately. The proposed model's efficiency in the automated diagnosis of grapevine diseases is demonstrated by its remarkable 99.7% accuracy rate. This high accuracy indicates that the PDDA may help with more effective and scalable plant disease monitoring, which will ultimately allow better agricultural practices.

The COVID-19 pandemic has had a significant impact on human migration worldwide, affecting transportation patterns in cities. Many cities have issued "stay-at-home" orders during the outbreak, causing commuters to change their usual modes of transportation. For example, some transit/bus passengers have switched to driving or car-sharing. As a result, urban traffic congestion patterns have changed dramatically, and understanding these changes is crucial for effective emergency traffic management and control efforts. While previous studies have focused on natural disasters or major accidents, only a few have examined pandemic-related traffic congestion patterns. This paper uses correlations and machine learning techniques to analyze the relationship between COVID-19 and transportation. The authors simulated traffic models for five different networks and proposed a Traffic Prediction Technique (TPT), which includes an Impact Calculation Methodology that uses Pearson's Correlation Coefficient and Linear Regression, as well as a Traffic Prediction Module (TPM). The paper's main contribution is the introduction of the TPM, which uses Convolutional Neural Network to predict the impact of COVID-19 on transportation. The results indicate a strong correlation between the spread of COVID-19 and transportation patterns, and the CNN has a high accuracy rate in predicting these impacts.

Abstract A significant issue in the field of illness diagnostics is the early detection and diagnosis of leukemia, that is, the accurate distinction of malignant leukocytes with minimal costs in the early stages of the disease. Flow cytometer equipment is few, and the methods used at laboratory diagnostic centers are laborious despite the high prevalence of leukemia. The present systematic review was carried out to review the works intending to identify and categories leukemia by utilizing machine learning. It was motivated by the potential of machine learning (machine learning (ML)) in disease diagnosis. Leukemia is a blood-forming tissues cancer that affects the bone marrow and lymphatic system. It can be treated more effectively if it is detected early. This work developed a new classification model for blood microscopic pictures that distinguishes between leukemia-free and leukemia-affected images. The general proposed method in this paper consists of three main steps which are: (i) Image_Preprocessing, (ii) Feature Extraction, and (iii) Classification. An optimized CNN (OCNN) is used for classification. OCNN is utilized to detect and classify the photo as "normal" or "abnormal". Fuzzy optimization is used to optimize the hyperparameters of CNN. It is a quite beneficial to use fuzzy logic in the optimization of CNN. As illustrated from results it is shown that, with the using of OCNN classifier and after the optimization of the hyperparameters of the CNN, it achieved the best results due to the enhancement of the performance of the CNN. The OCNN has achieved 99.99% accuracy with C-NMC_Leukemia dataset.

Abstract Sleep is an essential physiological process that is crucial for human health and well-being. However, with the rise of technology and increasing work demands, people are experiencing more and more disrupted sleep patterns. Poor sleep quality and quantity can lead to a wide range of negative health outcomes, including obesity, diabetes, and cardiovascular disease. This research paper proposes a smart sleeping enhancement system, named SleepSmart, based on the Internet of Things (IoT) and continual learning using bio-signals. The proposed system utilizes wearable biosensors to collect physiological data during sleep, which is then processed and analyzed by an IoT platform to provide personalized recommendations for sleep optimization. Continual learning techniques are employed to improve the accuracy of the system's recommendations over time. A pilot study with human subjects was conducted to evaluate the system's performance, and the results show that SleepSmart can significantly improve sleep quality and reduce sleep disturbance. The proposed system has the potential to provide a practical solution for sleep-related issues and enhance overall health and well-being. With the increasing prevalence of sleep problems, SleepSmart can be an effective tool for individuals to monitor and improve their sleep quality.

Abstract Crop Recommendation Systems are invaluable tools for farmers, assisting them in making informed decisions about crop selection to optimize yields. These systems leverage a wealth of data, including soil characteristics, historical crop performance, and prevailing weather patterns, to provide personalized recommendations. In response to the growing demand for transparency and interpretability in agricultural decision-making, this study introduces XAI-CROP an innovative algorithm that harnesses eXplainable artificial intelligence (XAI) principles. The fundamental objective of XAI-CROP is to empower farmers with comprehensible insights into the recommendation process, surpassing the opaque nature of conventional machine learning models. The study rigorously compares XAI-CROP with prominent machine learning models, including Gradient Boosting (GB), Decision Tree (DT), Random Forest (RF), Gaussian Naïve Bayes (GNB), and Multimodal Naïve Bayes (MNB). Performance evaluation employs three essential metrics: Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared (R2). The empirical results unequivocally establish the superior performance of XAI-CROP. It achieves an impressively low MSE of 0.9412, indicating highly accurate crop yield predictions. Moreover, with an MAE of 0.9874, XAI-CROP consistently maintains errors below the critical threshold of 1, reinforcing its reliability. The robust R 2 value of 0.94152 underscores XAI-CROP's ability to explain 94.15% of the data's variability, highlighting its interpretability and explanatory power.

Abstract Stress is now thought to be a major cause to a wide range of human health issues. However, many people may ignore their stress feelings and disregard to take action before serious physiological and mental disorders take place. The heart rate (HR) and blood pressure (BP) are the most physiological markers used in various studies to detect mental stress for a human, and because they are captured non-invasively using wearable sensors, these markers are recommended to provide information on a person’s mental state. Most stress assessment studies have been undertaken in a laboratory-based controlled environment. This paper proposes an approach to identify the mental stress of automotive drivers based on selected biosignals, namely, ECG, EMG, GSR, and respiration rate. In this study, six different machine learning models (KNN, SVM, DT, LR, RF, and MLP) have been used to classify between the stressed and relaxation states. Such system can be integrated with a Driver Assistance System (DAS). The proposed stress detection technique (SDT) consists of three main phases: (1) Biosignal Pre-processing, in which the signal is segmented and filtered. (2) Feature Extraction, in which some discriminate features are extracted from each biosignal to describe the mental state of the driver. (3) Classification. The results show that the RF classifier outperforms other techniques with a classification accuracy of 98.2%, sensitivity 97%, and specificity 100% using the drivedb dataset.

Abstract Leukemia is a malignancy that affects the blood and bone marrow. Its detection and classification are conventionally done through labor-intensive and specialized methods. The diagnosis of blood cancer in children is a critical task that requires high precision and accuracy. This study proposes a novel approach utilizing attention mechanism-based machine learning in conjunction with image processing techniques for the precise detection and classification of leukemia cells. The proposed attention-augmented algorithm for blood cancer detection in children (A2M-LEUK) is an innovative algorithm that leverages attention mechanisms to improve the detection of blood cancer in children. A2M-LEUK was evaluated on a dataset of blood cell images and achieved remarkable performance metrics: Precision = 99.97%, Recall = 100.00%, F1-score = 99.98%, and Accuracy = 99.98%. These results indicate the high accuracy and sensitivity of the proposed approach in identifying and categorizing leukemia, and its potential to reduce the workload of medical professionals and improve the diagnosis of leukemia. The proposed method provides a promising approach for accurate and efficient detection and classification of leukemia cells, which could potentially improve the diagnosis and treatment of leukemia. Overall, A2M-LEUK improves the diagnosis of leukemia in children and reduces the workload of medical professionals.