There have been several empirical studies addressing breast cancer using machine learning and soft computing techniques. Many claim that their algorithms are faster, easier, or more accurate than others are. This study is based on genetic programming and machine learning algorithms that aim to construct a system to accurately differentiate between benign and malignant breast tumors. The aim of this study was to optimize the learning algorithm. In this context, we applied the genetic programming technique to select the best features and perfect parameter values of the machine learning classifiers. The performance of the proposed method was based on sensitivity, specificity, precision, accuracy, and the roc curves. The present study proves that genetic programming can automatically find the best model by combining feature preprocessing methods and classifier algorithms.

Mobile robot navigation has remained an open problem over the last two decades. Mobile robots are required to navigate in unknown and dynamic environments, and in recent years the use of mobile robots in material handling has considerably increased. Usually workers push carts around warehouses and manually handle orders which is not very cost-effective. To this end, a potential method to control a swarm of mobile robots in a warehouse with static and dynamic obstacles is to use the wireless control approach. Further, to be able to control different types of mobile robots in the warehouse, the fuzzy logic control approach has been chosen. Therefore, in this paper, an on-line navigation technique for a wheeled mobile robot (WMR) in an unknown dynamic environment using fuzzy logic techniques is investigated. In this paper, we aim to use the robot in application in a warehouse. Experimental results show the effectiveness of the proposed algorithm.

This paper presents a novel Arabic Sign Language (ArSL) recognition system, using selected 2D hands and body key points from successive video frames. The system recognizes the recorded video signs, for both signer dependent and signer independent modes, using the concatenation of a 3D CNN skeleton network and a 2D point convolution network. To accomplish this, we built a new ArSL video-based sign database. We will present the detailed methodology of recording the new dataset, which comprises 80 static and dynamic signs that were repeated five times by 40 signers. The signs include Arabic alphabet, numbers, and some daily use signs. To facilitate building an online sign recognition system, we introduce the inverse efficiency score to find a sufficient optimal number of successive frames for the recognition decision, in order to cope with a near real-time automatic ArSL system, where tradeoff between accuracy and speed is crucial to avoid delayed sign classification. For the dependent mode, best results were obtained for dynamic signs with an accuracy of 98.39%, and 88.89% for the static signs, and for the independent mode, we obtained for the dynamic signs an accuracy of 96.69%, and 86.34% for the static signs. When both the static and dynamic signs were mixed and the system trained with all the signs, accuracies of 89.62% and 88.09% were obtained in the signer dependent and signer independent modes respectively.

According to the Food and Agriculture Organization, the world production of date fruits is 8,526,218 tons and around 1,302,859 tons in the Kingdom of Saudi Arabia (KSA) in 2018. There are several types of date fruits, and the most common in KSA are Barhi, Khalas, Meneifi, Naboot Saif, and Sullaj. Moreover, there are around five main maturity levels: Immature, Khalal, Khalal with Rutab, Pre-Tamar, and Tamar. Harvesting date fruits is performed according to its maturity level and type, which is a critical decision that significantly affects profit. In this paper, we propose a smart harvesting decision system to estimate date fruits type, maturity level, and weight using computer vision (CV) and deep learning (DL) techniques. The proposed system consists of three sub-systems: Dates maturity estimation system (DMES), type estimation system (DTES), and dates weight estimation system (DWES). We utilized four DL architectures, including ResNet, VGG-19, Inception-V3, and NASNet for both DMES and DTES and support vector machine (SVM) (regression and linear) for DWES. We evaluated the performance of the proposed system using the dataset collected by the Center of Smart Robotics Research. Using multiple performance metrics, DTES achieved maximum performance of 99.175% accuracy, an F1 score of 99.225%, 99.8% average precision, and 99.05% average recall. The maximum performance of DMES was 99.058% accuracy, F1 score of 99.34%, 99.64% average precision, and 99.08% average Recall. DWES achieved a maximum performance of 84.27% using SVM-Linear.

Date is the main fruit crop of the Kingdom of Saudi Arabia (KSA), approximately covering 72% of the total area under permanent crops. The Food and Agriculture Organization states that date production worldwide was 3,430,883 tons in 1990, which increases yearly, reaching 8,526,218 tons in 2018. Date production in KSA was around 527,881 tons in 1990, approximately reaching 1,302,859 tons in 2018. Harvesting date fruits at an appropriate time according to a specific maturity stage or level is a critical decision that significantly affects profit. In the present study, we proposed an intelligent harvesting decision system (IHDS) based on date fruit maturity level. The proposed decision system used computer vision and deep learning (DL) techniques to detect seven different maturity stages/levels of date fruit (Immature stage 1, Immature stage 2, Pre-Khalal, Khalal, Khalal with Rutab, Pre-Tamar, and Tamar). In the IHDS, we developed six different DL systems, and each one produced different accuracy levels in terms of the seven aforementioned maturity stages. The IHDS used datasets that have been collected by the Center of Smart Robotics Research. The maximum performance metrics of the proposed IHDS were 99.4%, 99.4%, 99.7%, and 99.7% for accuracy, F1 score, sensitivity (recall), and precision, respectively.