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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 R2 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 FROM AUTHOR]

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 FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

Diagnosing a brain tumor takes a long time and relies heavily on the radiologist's abilities and experience. The amount of data that must be handled has increased dramatically as the number of patients has increased, making old procedures both costly and ineffective. Many researchers investigated a variety of algorithms for detecting and classifying brain tumors that were both accurate and fast. Deep Learning (DL) approaches have recently been popular in developing automated systems capable of accurately diagnosing or segmenting brain tumors in less time. DL enables a pre-trained Convolutional Neural Network (CNN) model for medical images, specifically for classifying brain cancers. The proposed Brain Tumor Classification Model based on CNN (BCM-CNN) is a CNN hyperparameters optimization using an adaptive dynamic sine-cosine fitness grey wolf optimizer (ADSCFGWO) algorithm. There is an optimization of hyperparameters followed by a training model built with Inception-ResnetV2. The model employs commonly used pre-trained models (Inception-ResnetV2) to improve brain tumor diagnosis, and its output is a binary 0 or 1 (0: Normal, 1: Tumor). There are primarily two types of hyperparameters: (i) hyperparameters that determine the underlying network structure; (ii) a hyperparameter that is responsible for training the network. The ADSCFGWO algorithm draws from both the sine cosine and grey wolf algorithms in an adaptable framework that uses both algorithms' strengths. The experimental results show that the BCM-CNN as a classifier achieved the best results due to the enhancement of the CNN's performance by the CNN optimization's hyperparameters. The BCM-CNN has achieved 99.98% accuracy with the BRaTS 2021 Task 1 dataset. [ABSTRACT FROM AUTHOR]

In power transformers, accurate diagnosis prediction plays a crucial role in ensuring the reliability and efficiency of electrical systems. This paper presents an enhanced diagnostic prediction approach for power transformers by combining the Synthetic Minority Over-sampling Technique (SMOTE) and Deep Neural Networks (DNN). Our approach addresses the challenge of imbalanced data in power transformer diagnosis prediction by leveraging SMOTE to generate synthetic samples and balance the dataset. By effectively handling rare events through the augmentation of minority class instances, our predictive model becomes more robust. Additionally, using deep neural networks enables the exploration of complex patterns and relationships within the transformer dataset. The DNN architecture integrates multiple layers of learnable features, enabling the prediction model to extract high-level representations and achieve improved accuracy and performance. To evaluate the effectiveness of our proposed approach, we conducted extensive experiments using real-world power transformer datasets. Comparative analysis of the results demonstrates that the combination of SMOTE and DNN outperforms traditional classification techniques applied to imbalanced datasets. The proposed model achieves a higher training accuracy of 98.22 % and a testing accuracy of 94.6 % and mitigates the risks associated with misdiagnosis, contributing to power systems' overall reliability and maintenance efficiency. [ABSTRACT FROM AUTHOR]

Urban mobility is one of the critical challenges in modern cities that should be tackled carefully. The exponential growth of cars number badly impacts the transportation system (TS) on which most cities are living on. Traffic control is one of the most critical issues in TS, which depends on a set of cooperative traffic lights. Smart Traffic lights, which can receive and analyze traffic data, can solve traffic problems by efficiently predict the accurate waiting time for each traffic lane at the intersections. This can improve the traffic flow and accordingly promotes the transportation system performance. This paper introduces a Fog-Based Traffic Light Management Strategy (TLMS) based on Fuzzy Inference Engine. TLMS can accurately calculate the optimal waiting time for each traffic lane at the intersections to decrease the average waiting time for the stopped vehicles. TLMS applies Vehicle to infrastructure protocol (V2I) that allows vehicles to interact directly with the infrastructure of the road such as GPS sensors and the traffic light signals. At each traffic intersection, the number of waiting vehicles, their locations relative to TLMS, and their sizes are detected and sent to TLMS in real time. Then, based on fuzzy inference, TLMS can calculate the optimal waiting time for each lane, which optimizes the traffic flow at the intersection by minimizing the waiting time for the vehicles. The performance of the proposed TLMS has been compared and tested against recently proposed techniques via simulation. The results of the experiment showed that TLMS outperforms recent technologies as it minimizes the average waiting time of vehicles around the intersections and accordingly maximizes the performance of the traffic system. [ABSTRACT FROM AUTHOR]

Personality reflects how people can behave in different situations and affects their decisions. Analyzing personality is useful in many fields, for example in the prediction of performance in a job. Emotion recognition is another important research topic due to the wide spread of social media. People express their feeling in form of Facebook posts, tweeter real reactions, and shares. Understanding both personality and emotions from the written text is much easier when it comes to humans. However, this task is impossible with the huge amount of data spread all other social media. The use of machine learning algorithms for personality and emotion recognition from text data is a new research field. In this paper, we propose an enhanced recognition system for personality recognition and emotion recognition. The proposed enhanced recognition system is composed of four main modules, namely data acquisition module, data preprocessing module, personality recognition module, and emotion recognition module. Several machine learning algorithms are used for the multiclass classification process. Gray wolf optimization (GWO) algorithm is used for hyperparameter optimization, while group GWO (GGWO) algorithm is used for feature selection. The proposed model could achieve an accuracy of 99.99% using the random forest algorithm for personality detection and 88.06% using a decision tree for emotion recognition, which outperforms other state-of-the-art studies. We can profit from social media despite some of its drawbacks by understanding people's emotions through their tweets, posts, etc. For instance, before someone commits suicide, we can tell what their intentions are. Most suicide committers, according to recent studies, leave suicide notes on their social media accounts, and these letters need to be taken seriously. [ABSTRACT FROM AUTHOR]