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Abstract MRI and CT images are widely utilized for detecting tumors in internal organs. However, accurately distinguishing tumors using these images only, poses a challenge. This demands human expertise to effectively mark tumors in a given MRI or CT image, which is time-consuming. In this study, MONAI Label and MONAI Dynamic U-Net (DynU-Net) were used to segment tumors in any given 3D CT image of the pelvic bone, aiming to assist radiologists further. This method helps to run the model without needing a GPU which is better than traditional approaches. In addition, a convolutional neural network (CNN) was used to classify the tumors as benign or malignant and to predict three grades of tumors (low, medium, and high). The use of CNN in classification and prediction gives higher results than other studies. A dataset of 178 3D CT picture images was employed to feed the networks with the help of Adam optimizer and Categorical cross-entropy. We employ a set of 3D CT scans because of their greater contrast and spatial resolution which is better used for pelvic bone tumors. The Dice similarity coefficient (DSC) and Hausdorff distance (HD) have been used to perform calculations in segmentation. The results show a DSC of 0.7660280 and an HD of 16.75480. A range of performance metrics, such as sensitivity, specification, and F1-score for classification and prediction methods, are used to evaluate the accuracy of the proposed system. The system has accuracy (99.4%) for classification and (97.8%) for prediction. These findings indicate that MONAI Label is effective for automatic segmentation of tumors in a given CT scan; with high accuracy. Moreover, CNN is useful for classification and prediction systems with high accuracy. However, achieving better results is possible with an abundance of training samples.

Arabic language processing presents significant challenges due to its complex linguistic patterns and shortage of resources. This study describes MSG-ATS, a new technique to abstractive text summarization in Arabic that aims to overcome these issues. The key challenge is producing coherent and high-quality summaries given the Arabic language’s rich syntactic, semantic, and contextual elements. Traditional approaches, such as word2vec, frequently fail to capture these subtleties well. MSG-ATS uses multilevel semantic graphs and deep learning techniques to create a more thorough representation of Arabic text. This approach improves traditional text generation and embedding approaches by collecting syntactic, semantic, and contextual information fully. MSG-ATS uses a deep neural network to create high-quality summaries that are coherent and contextually appropriate. To verify MSG-ATS, we performed rigorous assessments that compared its performance to word2vec, a fundamental word embedding approach. These assessments employed a unique dataset created expressly for this study and included automated assessment using the ROUGE measure. The results are compelling: MSG-ATS outperformed the baseline model by 42.4% in precision, 23.8% in recall, and 38.3% overall. The outcomes of this study highlight MSG-ATS’s potential to considerably increase Arabic text summarization by providing a strong framework that solves the constraints of existing models while also laying the groundwork for future developments in the area.

Controlling a quadcopter is a challenging task because of the inherent high nonlinearity of a quadcopter system. In this paper, a new quaternion based nonlinear feedback controller for attitude and altitude regulation of a quadcopter is proposed. The dynamic model of the quadcopter is derived using Newton and Euler equations. The proposed controller is established based on a feedback linearization technique to control and regulate the quadcopter. Global asymptotic stability of the designed controller is verified using Lyapunov stability criterion. A comparison of the proposed controller performance and that of the state-of-the-art quadcopter controllers is performed to ensure the effectiveness of the proposed model. The efficiency of the proposed controller is clearly shown when the quadcopter is in or near a corner pose. Simulations are performed to assess the transient and steady state performance. Steady State Error ( $E_{ss}$ ) and Max Error ( $E_{M}$ ) are used as evaluation metrics of the proposed model performance.

Unmanned aerial vehicle quadcopters have applications in different real-life areas. They are nonlinear systems that necessitate the utilization of nonlinear control techniques. In this paper, we propose a new quaternion-based tracking controller for an underactuated quadcopter based on the pseudo linear feedback linearization technique. The quadcopter dynamic model was derived using Newton and Euler equations, and the global asymptotic stability of the quadcopter was verified using the Lyapunov stability criterion. The proposed controller has been compared to three state-of-the-art quadcopter controllers. Through simulation results, it has been shown that the proposed model has an effective and better performance than others. The metrics used in this evaluation are the steady-state error, maximum error, overshoot, and settling time. The different metrics proved the good performance of the proposed model in most of the different states that are presented.

Genome assemblies sequenced by a Whole Genome Shotgun (WGS) project predict an organism’s function and history. Sequence alignment is the foundation of bioinformatics by a computational search through large genome sequence databases, which generally requires enormous amounts of memory and takes a long execution time. In this paper, an Optimized Smith-Waterman algorithm based on the Gotoh algorithm with an affine gap for accuracy alignment, the divide and conquer technique, and the MapReduce framework implemented to establish a parallel process. This model was implemented on Virtex 7 field-programmable gate arrays (FPGAs). These techniques provide a better performance, reduce the hardware requirements, improve the accuracy, increase the computational throughput, and accelerate the alignment process for big data available in a complete Y chromosome. The hardware proposed system can achieve high performance, low time consumption 1.699 ns, and decrease FPGA utilization for big data alignments Y chromosome is used as an example.

Data compression is an important part of information security because compressed data is more secure and easy to handle. Effective data compression technology creates efficient, secure, and easy-to-connect data. There are two types of compression algorithm techniques, lossy and lossless. These technologies can be used in any data format such as text, audio, video, or image file. The main objective of this study was to reduce the physical space on the various storage media and reduce the time of sending data over the Internet with a complete guarantee of encrypting this data and hiding it from intruders. Two techniques are implemented, with data loss (Lossy) and without data loss (Lossless). In the proposed paper a hybrid data compression algorithm increases the input data to be encrypted by RSA (Rivest-Shamir-Adleman) cryptography method to enhance the security level and it can be used in executing lossy and lossless compacting Steganography methods. This technique can be used to decrease the amount of every transmitted data aiding fast transmission while using slow internet or take a small space on different storage media. The plain text is compressed by the Huffman coding algorithm, and also the cover image is compressed by Discrete wavelet transform DWT based that compacts the cover image through lossy compression in order to reduce the cover image's dimensions. The least significant bit LSB will then be used to implant the encrypted data in the compacted cover image. We evaluated that system on criteria such as percentage Savings percentage, Compression Time, Compression Ratio, Bits per pixel, Mean Squared Error, Peak Signal to Noise Ratio, Structural Similarity Index, and Compression Speed. This system shows a high-level performance and system methodology compared to other systems that use the same methodology.

Robust automatic speech emotional-speech recognition architectures based on hybrid convolutional neural networks (CNN) and feedforward deep neural networks are proposed and named in this paper as: BFN, CNA, and HBN. BFN is a combination between bag-of-Audio-word (BoAW) and feedforward deep neural network, CNA based on CNN, finally, HBN is hybrid architecture between BFN and CNA. Overall accuracy is achieved by leveraging Mel-frequency cepstral coefficient features and bag-of-acoustic-words to feed the network, resulting in promising classification performance. In addition, the concatenated output from the proposed hybrid networks is fed into a softmax layer to produce a probability distribution over categorical classifications for speech recognition. The three proposed models are trained on eight emotional classes from the Ryerson Audio-Visual Database of Emotional Speech and Song audio (RAVDESS) dataset. Our proposed models achieved overall precision between 81.5% and 85.5% and overall accuracy between 80.6% and 84.5%, hence outperforming state-of-the-art models using the same dataset.

The performance of controlled hybrid renewable hydro matrix wheels/ wind in micro grid application during the variations of water speed and load is described and investigated. This hybrid contained three wheels and three wind turbines modeled in MATLAB Simulink. This presented hybrid model consists of a water wheel and a generator in the main channel, wind turbine drives an induction generator, battery, variable static load, DC link, and control unit. Appropriate controllers are used to maintain the DC-link voltage constant at its desired value with variations of load, water and wind speed. The obtained simulated result shows that the studied hybrid system with the proposed controller and the storage system have a better performance of load voltage and current waveform compared with the case of not using an energy storage unit and the case of without a controller under the water, wind speed and load variations.

Abstract One of the goals of smart environments is to improve the quality of human life in terms of comfort and efficiency. The Internet of Things (IoT) paradigm has recently evolved into a technology for building smart environments. Security and privacy are considered key issues in any real-world smart environment based on the IoT model. The security vulnerabilities in IoT-based systems create security threats that affect smart environment applications. Thus, there is a crucial need for intrusion detection systems (IDSs) designed for IoT environments to mitigate IoT-related security attacks that exploit some of these security vulnerabilities. Due to the limited computing and storage capabilities of IoT devices and the specific protocols used, conventional IDSs may not be an option for IoT environments. This article presents a comprehensive survey of the latest IDSs designed for the IoT model, with a focus on the corresponding methods, features, and mechanisms. This article also provides deep insight into the IoT architecture, emerging security vulnerabilities, and their relation to the layers of the IoT architecture. This work demonstrates that despite previous studies regarding the design and implementation of IDSs for the IoT paradigm, developing efficient, reliable and robust IDSs for IoT-based smart environments is still a crucial task. Key considerations for the development of such IDSs are introduced as a future outlook at the end of this survey.

Abstract Brain tumour is a serious disease, and the number of people who are dying due to brain tumours is increasing. Manual tumour diagnosis from magnetic resonance images (MRIs) is a time consuming process and is insufficient for accurately detecting, localizing, and classifying the tumour type. This research proposes a novel two-phase multi-model automatic diagnosis system for brain tumour detection and localization. In the first phase, the system structure consists of preprocessing, feature extraction using a convolutional neural network (CNN), and feature classification using the error-correcting output codes support vector machine (ECOC-SVM) approach. The purpose of the first system phase is to detect brain tumour by classifying the MRIs into normal and abnormal images. The aim of the second system phase is to localize the tumour within the abnormal MRIs using a fully designed five-layer region-based convolutional neural network (R-CNN). The performance of the first phase was assessed using three CNN models, namely, AlexNet, Visual Geometry Group (VGG)-16, and VGG-19, and a maximum detection accuracy of 99.55% was achieved with AlexNet using 349 images extracted from the standard Reference Image Database to Evaluate Response (RIDER) Neuro MRI database. The brain tumour localization phase was evaluated using 804 3D MRIs from the Brain Tumor Segmentation (BraTS) 2013 database, and a DICE score of 0.87 was achieved. The empirical work proved the outstanding performance of the proposed deep learning-based system in tumour detection compared to other non-deep-learning approaches in the literature. The obtained results also demonstrate the superiority of the proposed system concerning both tumour detection and localization.