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Due to the rapid developments in technology and the sudden expansion of social media use, Dialect Arabic has become an important source of data that needs to be addressed when building Arabic corpora. In this paper, thirty-three Arabic corpora are surveyed to show that despite all of the developments in the literature, Saudi dialect (SD) corpora still need further expansion. This paper contributes to the literature on SD corpora by creating the largest Saudi corpus – the King Saud University Saudi Corpus (KSUSC) – with +1B total words, including +119M SD words. The KSUSC not only is the newest and largest SD corpus but is also diverse, covering 26 domains in text collected from five different sources. This paper also contributes to the literature by developing a new incremental preprocessing system that is used to create relevant lexicons that are then used to clean and normalize the collected data. This incremental system is scalable and can be adapted for different resources and dialects. Moreover, the collection process for building the KSUSC is discussed in detail, and the challenges in collecting SD text with respect to each platform are highlighted. By the end of this paper, different design criteria are proposed and used with the KSUSC to conclude that the resulting corpus can be of great benefit to researchers who are interested in integrating the corpus with their own work or using its resulting lexicons with Saudi-based NLP tasks.

COVID-19 was first discovered in Wuhan, China, in December 2019 and has since spread worldwide. An automated and fast diagnosis system needs to be developed for early and effective COVID-19 diagnosis. Hence, we propose two-and three-classifier diagnosis systems for classifying COVID-19 cases using transfer-learning techniques. These systems can classify X-ray images into three categories: healthy, COVID-19, and pneumonia cases. We used two X-ray image datasets (DATASET-1 and DATASET-2) collected from state-of-the-art studies and train the systems using deep learning architectures, such as VGG-19, NASNet, and MobileNet2, on these datasets. According to the validation and testing results, our proposed diagnosis systems achieved excellent results with the VGG-19 architecture. The two-classifier diagnosis system achieved high sensitivity for COVID-19, with 99.5% and 100% on DATASET-1 and DATASET-2, respectively. The three-classifier diagnosis system achieves high sensitivity for COVID-19, with 98.4% and 100% on DATASET-1 and DATASET-2, respectively. The high sensitivity of these diagnostic systems for COVID-19 will significantly improve the speed and precision of COVID-19 diagnosis.

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.

The use of cutting edge object detection techniques to build an accurate phoneme sequence recognition system for English and Arabic languages is investigated in this study. Recently, numerous techniques have been proposed for object detection in daily life applications using deep learning. In this paper, we propose the use of object detection techniques in speech processing tasks. We selected two state-of-the-art object detectors, namely YOLO and CenterNet, based on a trade-off between detection accuracy and speed. We tackled the problem of phoneme sequence recognition using three systems: the domain transfer learning system (DTS) from image to speech, intra-language transfer leaning system (IaTS) between speech corpora within the same language (English to English), and inter-language transfer learning system (IeTS) between speech corpora from dissimilar languages (English to Arabic). For English phoneme recognition, the Texas Instruments/Massachusetts Institute of Technology (TIMIT) corpus is used to evaluate the performance of the proposed systems. Our IaTS based on the CenterNet detector achieves the best results using the test core set of TIMIT with 15.89% phone error rate (PER). For Arabic phoneme recognition, the best performance, with 7.58% PER, was achieved using the CenterNet. These results show the effectiveness of using object detection techniques in phoneme recognition tasks. Furthermore, based on the findings of this study, speech processing tasks may be treated as object detection tasks.

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.

Crowdsourcing is a new mode of value creation in which organizations leverage numerous Internet users to accomplish tasks. However, because these workers have different backgrounds and intentions, crowdsourcing suffers from quality concerns. In the literature, tracing the behavior of workers is preferred over other methodologies such as consensus methods and gold standard approaches. This paper proposes two novel models based on workers’ behavior for task classification. These models newly benefit from time-series features and characteristics. The first model uses multiple time-series features with a machine learning classifier. The second model converts time series into images using the recurrent characteristic and applies a convolutional neural network classifier. The proposed models surpass the current state of-the-art baselines in terms of performance. In terms of accuracy, our feature-based model achieved 83.8%, whereas our convolutional neural network model achieved 76.6%.

Stereo vision is a technique used to recover depth information from stereo (two) images that capture the view from different perspectives. The stereo-matching/stereo-correspondence process presents an essential role in stereo vision. Much research has been done on this process. However, the accuracy of stereo-matching/stereo-correspondence algorithms is affected by many characteristics related to the scene, such as the lack of discriminative image features, repetitive patterns, specular reflections, and regions of uniform texture. In this paper, we propose a pre-processing schema called brightness-based enhancement for disparity maps (2BEDM). 2BEDM is used to enhance the accuracy of local stereo-matching/stereo-correspondence algorithms (which are affected by the aforementioned characteristics related to the scene in stereo images) and to enhance the output-disparity map of stereo-matching/stereo-correspondence algorithms. It is based on the brightness of each pixel in the stereo images (left image and right image) rather than on the pixel itself. 2BEDM works on the basis of the hypothesis that the brightness of each block of stereo images of the same views is equal. The experimental results illustrate the enhancement provided by the proposed 2BEDM schema.

Autonomous navigation systems of Wheeled Mobile Robot (WMR) include several interrelated concepts, techniques, and algorithms, such as perception, localization, mapping, planning, and path execution but mapping and path planning are at the core of all of them. Autonomous navigation remains one of the primary challenges in the mobile robot industry; many control algorithms and techniques have been recently developed that aim to overcome this challenge. In this research, we successfully proposed a smart fully functional mobile-robot navigation system (SFMRNS) for WMR, by developing and integrating many self-developed novel system and algorithms. The mapping system integrates two interrelated, self-developed novel components: (1) 2BEDM (Brightness-Based Enhancement for Disparity Map), and (2) I3MS (Intelligent Multi-Sensor Multi-Baseline Mapping System). For path-planning algorithm, we used a self-developed algorithm called AntStar (Ant system Star). For navigation, we used self-developed navigation system based on the behavior of the robots and obstacle. In additional, we used the dead-reckoning technique for localization, and a self-developed navigate system is used to navigate the robot to the target points. To ensure the success of all developed algorithms and systems, we test them individually, in as many experiments as possible, before integrating these components into a fully functional mobile-robot navigation system. The successful integration and interaction of all the proposed systems and algorithms demonstrates a high degree of applicability.

This study proposes using object detection techniques to recognize sequences of articulatory features (AFs) from speech utterances by treating AFs of phonemes as multi-label objects in speech spectrogram. The proposed system, called AFD-Obj, recognizes sequence of multi-label AFs in speech signal and localizes them. AFD-Obj consists of two main stages: firstly, we formulate the problem of AFs detection as an object detection problem and prepare the data to fulfill requirement of object detectors by generating a spectral three-channel image from the speech signal and creating the corresponding annotation for each utterance. Secondly, we use annotated images to train the proposed system to detect sequences of AFs and their boundaries. We test the system by feeding spectrogram images to the system, which will recognize and localize multi-label AFs. We investigated using these AFs to detect the utterance phonemes. YOLOv3-tiny detector is selected because of its real-time property and its support for multi-label detection. We test our AFD-Obj system on Arabic and English languages using KAPD and TIMIT corpora, respectively. Additionally, we propose using YOLOv3-tiny as an Arabic phoneme detection system (i.e., PD-Obj) to recognize and localize a sequence of Arabic phonemes from whole speech utterances. The proposed AFD-Obj and PD-Obj systems achieve excellent results for Arabic corpus and comparable to the state-of-the-art method for English corpus. Moreover, we showed that using only one-scale detection is suitable for AFs detection or phoneme recognition.

A method that uses fuzzy logic to classify two simple speech features for the automatic classification of voiced and unvoiced phonemes is proposed. In addition, two variants, in which soft computin...

Numerous applications, such as material handling, manufacturing, security, and automated transportation systems, use mobile robots. Autonomous navigation remains one of the primary challenges of the mobile robot industry; many new control algorithms have been recently developed that aim to overcome this challenge. These algorithms are primarily related by their adoption of new strategies for avoiding obstacles and minimizing the travel time to a target along an optimal path. In this paper, we introduce four different navigation systems for an autonomous mobile robot (PowerBot) and compare them. The four systems are based on a fuzzy logic controller (FLC). The FLC of one system is tuned by an inexperienced human (naive), while the three other FLCs are optimized through a genetic algorithm (GA), particle swarm optimization (PSO), and a human expert. We hope the comparison answers the question of which is the best controller. In other words, “who can win?,”the naive, the GA, the PSO, or the expert, in fine tuning the membership functions of the navigation and obstacle avoidance behavior of the mobile robot? To answer this question, we used four different techniques for optimization (the naive FLC, GA, PSO, and FLC-expert) and used many criteria for comparison, whereas other research papers have dealt with two techniques at a time.

Navigation and obstacle avoidance in an unknown environment is proposed in this paper using hybrid neural network with fuzzy logic controller. The overall system is termed as Adaptive Neuro Fuzzy Inference System (ANFIS). ANFIS combines the benefits of fuzzy logic and neural networks for the purpose of achieving robotic navigation task. Simulation results are presented using Khepera Simulator (KiKs) within MATLAB environment. Moreover, experimental results are obtained using Khepera III platform.

The control of autonomous mobile robot in an unknown environments include many challenge. Fuzzy logic controller is one of the useful tool in this field. Performance of fuzzy logic controlling depends on the membership function, so the membership function adjusting is a time consuming process. In this paper, we optimized a fuzzy logic controller (Fuzzy) by automatic adjusting the membership function using a particle swarm optimization (PSO). The proposed method (PSO-Fuzzy) is implemented and compared with Fuzzy using Khepera simulator. Moreover, the performance of these approaches compared through experiments using a real Khepera III platform.

Presently, lawyers, law enforcement agencies, and judges in courts use speech and other biometric features to recognize suspects. In general, speaker recognition is used for discriminating people based on their voices. The process of determining, if a suspected speaker is the source of trace, is called forensic speaker recognition. In such applications, the voice samples are most probably noisy, the recording sessions might mismatch each other, the sessions might not contain sufficient recording for recognition purposes, and the suspect voices are recorded through mobile channel. The identification of a person through his voice within a forensic quality context is challenging. In this paper, we propose a method for forensic speaker recognition for the Arabic language; the King Saud University Arabic Speech Database is used for obtaining experimental results. The advantage of this database is that each speaker’s voice is recorded in both clean and noisy environments, through a microphone and a mobile channel. This diversity facilitates its usage in forensic experimentations. Mel-Frequency Cepstral Coefficients are used for feature extraction and the Gaussian mixture model-universal background model is used for speaker modeling. Our approach has shown low equal error rates (EER), within noisy environments and with very short test samples.

The authors have reported that the funding information listed in the Acknowledgments section of their article [1] does not match the wording prescribed by their funding organization. The appropriately worded acknowledgment of the funding organization is given in this corrigendum, and reads as follows: Acknowledgments This work was supported by the Research Center of College of Computer and Information Sciences, King Saud University. The authors are grateful for this support. The authors and the publisher sincerely regret any inconvenience this oversight might have caused to the readership.

Background/Objectives: Nowadays, object detection and tracking is an important issue in robotics and computer vision systems, especially in video surveillance, robot navigation and autonomous vehicle navigation. Methods: In this paper, we propose a fast method for object tracking and recognition within the context of a mobile robot acquiring real time images from a top mounted IP camera. The aim of the proposed method is to let the robot identify multi-targets within a scene, then move toward the desired object. The method is based on a new vectored contour to identify objects from HSV images. Findings: Experimental results have shown that our method best fitted the mobile platform and gave excellent competitive results in real time tracking. Our proposed method has shown better adaptation com-pared to SURF and other state of the art tracking methods, especially in terms of time and simplicity, specifically when the camera is not in front of the target object, i.e. at different inclination angles and distances. Application: The object detection and tracking proposed in this work can be implemented on many fields such as video surveillance robotic navigation or in industry in classification of objects depending on their forms or colors.

This paper addresses the problem of making a non-holonomic wheeled mobile robot (WMR) move to a target object using computer vision and obstacle-avoidance techniques. If a priori information about the obstacles is available, pre-planning the desired path can be a good candidate method. However, in so many cases, obstacles are dynamic. Therefore, our first challenge is to make the WMR move to a desired target while autonomously avoiding any obstacle along its path. The second challenge deals with visual-tracking loss; that is, when the target is lost from the camera scope, the robot should use Dead Reckoning (DR) to get back on its path towards the target. The Visual Tracking (VT) algorithm then takes the relay to reach the final destination, compensating for any errors due to DR by calculating the distance to the target when it is within the scope of the camera. The proposed system also uses two fuzzy-logic controllers; the first controller avoids objects while the second manages the path to the target. Different complex scenarios have been implemented, showing the validity of our multi-controller model.

Software cost estimation approximate judgment of the cost and time required to complete the project successfully. The cost estimation is usually measured in terms of effort. The Constructive Cost Model (COCOMO) is one of the most important model for software cost estimation. In this paper, soft computing techniques: genetic algorithm has been used for tuning the parameters of COCOMO model to predicate the software cost more accurately. Moreover, the performance of proposed methods compared through COCOMO NASA Data Set were used.