Arab Academy for Science, Technology, and Maritime Transport

It is important to detect breast cancer as early as possible. In this manuscript, a new methodology for classifying breast cancer using deep learning and some segmentation techniques are introduced. A new computer aided detection (CAD) system is proposed for classifying benign and malignant mass tumors in breast mammography images. In this CAD system, two segmentation approaches are used. The first approach involves determining the region of interest (ROI) manually, while the second approach uses the technique of threshold and region based. The deep convolutional neural network (DCNN) is used for feature extraction. A well-known DCNN architecture named AlexNet is used and is fine-tuned to classify two classes instead of 1,000 classes. The last fully connected (fc) layer is connected to the support vector machine (SVM) classifier to obtain better accuracy. The results are obtained using the following publicly available datasets (1) the digital database for screening mammography (DDSM); and (2) the Curated Breast Imaging Subset of DDSM (CBIS-DDSM). Training on a large number of data gives high accuracy rate. Nevertheless, the biomedical datasets contain a relatively small number of samples due to limited patient volume. Accordingly, data augmentation is a method for increasing the size of the input data by generating new data from the original input data. There are many forms for the data augmentation; the one used here is the rotation. The accuracy of the new-trained DCNN architecture is 71.01% when cropping the ROI manually from the mammogram. The highest area under the curve (AUC) achieved was 0.88 (88%) for the samples obtained from both segmentation techniques. Moreover, when using the samples obtained from the CBIS-DDSM, the accuracy of the DCNN is increased to 73.6%. Consequently, the SVM accuracy becomes 87.2% with an AUC equaling to 0.94 (94%). This is the highest AUC value compared to previous work using the same conditions.

This study proposes a multi-objective index-based approach to optimally determine the size and location of multi-distributed generation (DG) units in distribution system with non-unity power factor considering different load models. It is shown that load models can significantly affect the optimal location and sizing of DG resources in distribution systems. The proposed multi-objective function to be optimised includes a short-circuit-level parameter to represent the protective device requirements. The proposed function also considers a wide range of technical issues such as active and reactive power losses of the system, the voltage profile, the line loading and the Mega Volt Ampere (MVA) intake by the grid. The optimisation technique based on particle swarm optimisation is introduced. The analysis of continuation power flow to determine the effect of DG units on the most sensitive buses to voltage collapse is carried out. The proposed algorithm is tested using the 38-bus radial system and the IEEE 30-bus meshed system. The results show the effectiveness of the proposed algorithm.

This study concerns the ability of adults to achieve nativelike competence in second language when the acquisition context lacks formal instruction and, therefore, more closely resembles the environment for first language acquisition. The study presents the results of extensive testing of an adult who has apparently acquired native proficiency in Egyptian Arabic (EA) in an untutored setting. The goal is to determine to what extent her linguistic competence matches that of native speakers. Measures employed to assess her level of achievement are a speech production task, a grammaticality judgment task, a translation task, an anaphoric interpretation task, and an accent recognition task. Results are compared to those of native speakers as well as to those of a proficient learner of EA with extensive formal instruction. The results lead the authors to reexamine the critical period hypothesis while addressing the role of talent in adult language learning. The study concludes with an evaluation of our subject's language learning history to discover what factors differentiate her from less successful naturalistic adult acquirers.

Recent researches oriented to photovoltaic (PV) systems feature booming interest in current decade. For efficiency improvement, maximum power point tracking (MPPT) of PV array output power is mandatory. Although classical MPPT techniques offer simplified structure and implementation, their performance is degraded when compared with artificial intelligence‐based techniques especially during partial shading and rapidly changing environmental conditions. Artificial neural network (ANN) algorithms feature several capabilities such as: (i) off‐line training, (ii) nonlinear mapping, (iii) high‐speed response, (iv) robust operation, (v) less computational effort and (vi) compact solution for multiple‐variable problems. Hence, ANN algorithms have been widely applied as PV MPPT techniques. Among various available ANN‐based PV MPPT techniques, very limited references gather those techniques as a survey. Neither classification nor comparisons between those competitors exist. Moreover, no detailed analysis of the system performance under those techniques has been previously discussed. This study presents a detailed survey for ANN based PV MPPT techniques. The authors propose new categorisation for ANN PV MPPT techniques based on controller structure and input variables. In addition, a detailed comparison between those techniques from several points of view, such as ANN structure, experimental verification and transient/steady‐state performance is presented. Recent references are taken into consideration for update purpose.

Abstract The measurement of the luminosity recorded by the CMS detector installed at LHC interaction point 5, using proton–proton collisions at $$\sqrt{s}=13\,{\text {TeV}} $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msqrt> <mml:mi>s</mml:mi> </mml:msqrt> <mml:mo>=</mml:mo> <mml:mn>13</mml:mn> <mml:mspace/> <mml:mtext>TeV</mml:mtext> </mml:mrow> </mml:math> in 2015 and 2016, is reported. The absolute luminosity scale is measured for individual bunch crossings using beam-separation scans (the van der Meer method), with a relative precision of 1.3 and 1.0% in 2015 and 2016, respectively. The dominant sources of uncertainty are related to residual differences between the measured beam positions and the ones provided by the operational settings of the LHC magnets, the factorizability of the proton bunch spatial density functions in the coordinates transverse to the beam direction, and the modeling of the effect of electromagnetic interactions among protons in the colliding bunches. When applying the van der Meer calibration to the entire run periods, the integrated luminosities when CMS was fully operational are 2.27 and 36.3 $$\,\text {fb}^{-1}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mspace/> <mml:msup> <mml:mtext>fb</mml:mtext> <mml:mrow> <mml:mo>-</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:msup> </mml:mrow> </mml:math> in 2015 and 2016, with a relative precision of 1.6 and 1.2%, respectively. These are among the most precise luminosity measurements at bunched-beam hadron colliders.

In this work, a new framework for breast cancer image segmentation and classification is proposed. Different models including InceptionV3, DenseNet121, ResNet50, VGG16 and MobileNetV2 models, are applied to classify Mammographic Image Analysis Society (MIAS), Digital Database for Screening Mammography (DDSM) and the Curated Breast Imaging Subset of DDSM (CBIS-DDSM) into benign and malignant. Moreover, the trained modified U-Net model is utilized to segment breast area from the mammogram images. This method will aid as a radiologist's assistant in early detection and improve the efficiency of our system. The Cranio Caudal (CC) vision and Mediolateral Oblique (MLO) view are widely used for the identification and diagnosis of breast cancer. The accuracy of breast cancer diagnosis will be improved as the number of views is increased. Our proposed frame work is based on MLO view and CC view to enhance the system performance. In addition, the lack of tagged data is a big challenge. Transfer learning and data augmentation are applied to overcome this problem. Three mammographic datasets; MIAS, DDSM and CBIS-DDSM, are utilized in our evaluation. End-to-end fully convolutional neural networks (CNNs) are introduced in this paper. The proposed technique of applying data augmentation with modified U-Net model and InceptionV3 achieves the best result, specifically with the DDSM dataset. This achieves 98.87% accuracy, 98.88% area under the curve (AUC), 98.98% sensitivity, 98.79% precision, 97.99% F1 score, and a computational time of 1.2134 s on DDSM datasets.

Abstract Electric load forecasting (ELF) is a vital process in the planning of the electricity industry and plays a crucial role in electric capacity scheduling and power systems management and, therefore, it has attracted increasing academic interest. Hence, the accuracy of electric load forecasting has great importance for energy generating capacity scheduling and power system management. This paper presents a review of forecasting methods and models for electricity load. About 45 academic papers have been used for the comparison based on specified criteria such as time frame, inputs, outputs, the scale of the project, and value. The review reveals that despite the relative simplicity of all reviewed models, the regression analysis is still widely used and efficient for long-term forecasting. As for short-term predictions, machine learning or artificial intelligence-based models such as Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Fuzzy logic are favored.

Variable‐step incremental conductance (Inc.Cond.) technique, for photovoltaic (PV) maximum power point tracking, has merits of good tracking accuracy and fast convergence speed. Yet, it lacks simplicity in its implementation due to the mathematical division computations involved in its algorithm structure. Furthermore, the conventional variable step‐size, based on the division of the PV module power change by the PV voltage change, encounters steady‐state power oscillations and dynamic problems especially under sudden environmental changes. In this study, an enhancement is introduced to Inc.Cond. algorithm in order to entirely eliminate the division calculations involved in its structure. Hence, algorithm implementation complexity is minimised enabling the utilisation of low‐cost microcontrollers to cut down system cost. Moreover, the required real processing time is reduced, thus sampling rate can be improved to fasten system response during sudden changes. Regarding the applied step‐size, a modified variable‐step size, which depends solely on PV power, is proposed. The latter achieves enhanced transient performance with minimal steady‐state power oscillations around the MPP even under partial shading. For proposed technique's validation, simulation work is carried out and an experimental set up is implemented in which ARDUINO Uno board, based on low‐cost Atmega328 microcontroller, is employed.

The first Dow Jones Islamic market index (DJIMI) was launched in February 1999. A comprehensive study of the performance of each Islamic index that accounts for the effects of industry, size, and economic conditions reveals that Islamic indexes provided investors with positive abnormal returns over 1996-2003, but underperformed over the bear market “subperiod.” Equally important, the empirical findings of this article, in contrast to the common belief that technology sector firms are behind the Islamic indexes9 positive abnormal returns, provide evidence that smallness and basic materials, consumer cyclicals, and industrial and telecommunication industries are the dominant driving factors in Islamic index positive abnormal returns

Abstract Foreign language anxiety (FLA) has been a perennial concern in language learning, as foreign language (FL) learners often communicate feelings of anxiety, stress, or nervousness. This study explored the role of artificial intelligence (AI) applications in speaking practice for FLA management of 48 undergraduate participants in an EFL class in Egypt. An eight‐week, quasi‐experimental pretest–posttest design examined learner anxiety levels using a 33‐item FLA questionnaire. Their oral proficiency was assessed via roleplaying using an interaction‐enhanced public version of the IELTS speaking evaluation rubric. The results confirmed that FL learners experienced FLA pre‐ and post‐intervention. The identified anxiety levels played a facilitative role in FL learning with several ensuing gains. The use of conversationally enhanced AI chatbots in interactive activities slightly intensified learners' FLA, thus meriting further investigation of these objectives. Overall, subject to further development, AI chatbots are promising for significantly improving linguistic output gains; however, this study found that learners' speech‐related anxieties were not reduced following the interactions with the chatbots. It is concluded that FLA plays an underexplored facilitative role in sharpening learner cognitive faculties and linguistic capacities. Moreover, AI chatbots may be beneficial in advancing FL learning with significant potential in EFL contexts, facilitating improved interaction and oral communication. These findings support the integration of AI technologies as effective tools in FL education, providing flexible, interactive, and learner‐centred learning. This study is expected to be of considerable interest to FL educators and learners.

Navigation/positioning systems have become critical to many applications, such as autonomous driving, Internet of Things (IoT), Unmanned Aerial Vehicle (UAV), and smart cities. However, it is difficult to provide a robust, accurate, and seamless solution with single navigation/positioning technology. For example, the Global Navigation Satellite System (GNSS) cannot perform satisfactorily indoors; consequently, multi-sensor integrated systems provide the solution, as they compensate for the limitations of single technology by using the complementary characteristics of different sensors. This article describes a thorough investigation into multi-sensor data fusion, which over the last ten years has been used for integrated positioning/navigation systems. In this article, different navigation/positioning systems are classified and elaborated upon from three aspects: (1) sources, (2) algorithms and architectures, and (3) scenarios, which we further divide into two categories: (i) analytics-based fusion and (ii) learning-based fusion. For analytics-based fusion, we discuss the Kalman filter and its variants, graph optimization methods, and integrated schemes. For learning-based fusion, several supervised, unsupervised, reinforcement learning, and deep learning techniques are illustrated in multi-sensor integrated positioning/navigation systems. Design consideration of these integrated systems is discussed in detail from several aspects and their application scenarios are categorized. Finally, future directions for their research and implementation are discussed.

Over a long period of time, humans have explored many natural resources looking for remedies of various ailments. Traditional medicines have played an intrinsic role in human life for thousands of years, with people depending on medicinal plants and their products as dietary supplements as well as using them therapeutically for treatment of chronic disorders, such as cancer, malaria, diabetes, arthritis, inflammation, and liver and cardiac disorders. However, plant resources are not sufficient for treatment of recently emerging diseases. In addition, the seasonal availability and other political factors put constrains on some rare plant species. The actual breakthrough in drug discovery came concurrently with the discovery of penicillin from Penicillium notatum in 1929. This discovery dramatically changed the research of natural products and positioned microbial natural products as one of the most important clues in drug discovery due to availability, variability, great biodiversity, unique structures, and the bioactivities produced. The number of commercially available therapeutically active compounds from microbial sources to date exceeds those discovered from other sources. In this review, we introduce a short history of microbial drug discovery as well as certain features and recent research approaches, specifying the microbial origin, their featured molecules, and the diversity of the producing species. Moreover, we discuss some bioactivities as well as new approaches and trends in research in this field.

Integrated on-board battery chargers (OBCs) have been recently introduced as an optimal/elegant solution to increase electric vehicle (EV) market penetration as well as minimize overall EV cost. Unlike conventional off-board and on-board battery chargers, integrated OBCs exploit the existing propulsion equipment for battery charging without extra bulky components and/or dedicated infrastructure. OBCs are broadly categorized into three-phase and single-phase types with unidirectional or bidirectional power flow. This paper starts with surveying the main topologies introduced in the recent literature employing either induction or permanent magnet motors to realize fully integrated slow (single-phase) and fast (three-phase) on-board EV battery charging systems, with emphasis on topologies that entail no or minimum hardware reconfiguration. Although, permanent magnet (PM) motors with conventional double-layer distributed winding layouts have been deployed in most commercial EV motors, the non-overlapped fractional slot concentrated winding (FSCW) has been the prevailing choice in the most recent permanent magnet motor designs due to its outstanding operational merits. Hence, a thorough investigation of the impact different FSCW stator winding designs have on machine performance under the charging process is presented in this paper. To this end, the induced magnet losses, which represent a challenging demerit of the FSCW, have been used to compare different topologies under both propulsion and charging operation modes. Based on the introduced comparative study, the optimal slot/pole combinations that correspond to the best compromise under both operational modes have been highlighted.

This manuscript presents a survey on new challenges in wireless communication systems and discusses recent approaches to address some recently raised problems by the wireless community. At first a historical background is briefly introduced. Challenges based on modern and real life applications are then described. Up to date research fields to solve limitations of existing systems and emerging new technologies are discussed. Theoretical and experimental results based on several research projects or studies are briefly provided. Essential, basic and many self references are cited. Future researcher axes are briefly introduced.

This study investigates the convective heat transfer of a hybrid nanofluid filled in a triangular cavity subjected to a constant magnetic field and heated by a constant heat flux element from below. The inclined side of the cavity is cooled isothermally while the remaining sides are thermally insulated. The finite difference method with the stream function-vorticity formulation of the governing equations has been utilized in the numerical solution. The problem is governed by several pertinent parameters namely, the size and position of the heater element, B = 0.2–0.8 and D = 0.3–0.7, respectively, the Rayleigh number, Ra = 102–106, the Hartmann number, Ha = 0–100, the volume fraction of the suspended nanoparticles, ϕ = 0–0.2, and the heat generation parameter Q = 0–6. The results show significant effect of increasing the volume fraction of the hybrid nanofluid when the natural convection is very small. Moreover, the hybrid nanofluid composed of equal quantities of Cu and Al2O3 nanoparticles dispersed in water base fluid has no significant enhancement on the mean Nusselt number compared with the regular nanofluid.

Singular Value Decomposition (SVD) has recently emerged as a new paradigm for processing different types of images. SVD is an attractive algebraic transform for image processing applications. The paper proposes an experimental survey for the SVD as an efficient transform in image processing applications. Despite the well known fact that SVD offers attractive properties in imaging, the exploring of using its properties in various image applications is currently at its infancy. Since the SVD has many attractive properties have not been utilized, this paper contributes in using these generous properties in newly image applications and gives a highly recommendation for more research challenges. In this paper, the SVD properties for images are experimentally presented to be utilized in developing new SVD-based image processing applications. The paper offers survey on the developed SVD based image applications. The paper also proposes some new contributions that were originated from SVD properties analysis in different image processing. The aim of this paper is to provide a better understanding of the SVD in image processing and identify important various applications and open research directions in this increasingly important area; SVD based image processing in the future research.

Purpose The purpose of this study is to investigate the role of stock markets in economic growth and to shed some light on the macroeconomic determinants which must have an important influence on stock markets development. Design/methodology/approach The empirical study is conducted using an unbalanced panel data from 12 Middle Eastern and North African (MENA) region countries. Econometric issues are based on estimation of some fixed and random effects specifications. Findings It is found that saving rate, financial intermediary, stock market liquidity and the stabilization variable are the important determinants of stock market development. In addition, it is found that financial intermediaries and stock markets are complements rather than substitutes in the growth process. Practical implications This paper has some policy implications to MENA region countries. In order to promote stock market development in the region, it is important to encourage savings by appropriate incentives, to improve stock market liquidity, to develop financial intermediaries and to control inflation. Originality/value Since it is unclear whether emerging markets in the MENA region respond, similarly, to economic and political shocks like other emerging markets and/or developed markets. This paper fills this gap by making an in‐depth analysis of 12 MENA capital markets in order to assess how they can improve their capital markets, and hence, benefit the global investor.

Large-scale solar photovoltaic (PV) plants play an essential role in providing the increasing demand for energy in recent time. Therefore, in the purpose of achieving the highest harvested power under the partial shading conditions as well as protecting the PV array from the hot-spot calamity, the PV reconfiguration strategy is established as an efficient procedure. This is performed by redistribution of PV modules according to their levels of shading. Motivated by this, the authors in this article have introduced a novel population-based algorithm that is known as marine predators algorithm (MPA) to restructure the PV array dynamically. Moreover, a novel objective function is introduced to enhance the algorithm performance rather than utilizing the regular weighted objective function in the literature. The effectiveness of the proposed algorithms based on the novel objective function is evaluated using several metrics such as fill factor, mismatch losses, percentage of power loss, and percentage of power enhancement. Besides, the obtained results are compared with a regular total-cross-tied (TCT) connection, manta ray foraging optimization (MRFO), harris hawk optimizer (HHO) and particle swarm optimizer (PSO) based reconfiguration techniques. Furthermore, to demonstrate the suitability of the proposed methods, large scale PV arrays of $16\times16$ and $25\times25$ are considered and evaluated. The results reveal that MPA enhanced the PV array power by percentage of 28.6 %, 2.7 % and 5.7 % in cases of $9\times9$ , $16\times16$ and $25\times25$ PV arrays, respectively. The comprehensive comparisons endorse that MPA shows a successful shade dispersion; hence the number of multiple peaks in the PV characteristics has reduced, and high values of power have been harvested with least mean execution time in comparison with PSO, HHO and MRFO. Moreover, the Wilcoxon signed-rank test has been accomplished to confirm the reliability and applicability of the proposed approach for the PV large scale arrays as well.

Cervical cancer is the fourth most common malignant disease in women’s worldwide. In most cases, cervical cancer symptoms are not noticeable at its early stages. There are a lot of factors that increase the risk of developing cervical cancer like human papilloma virus, sexual transmitted diseases, and smoking. Identifying those factors and building a classification model to classify whether the cases are cervical cancer or not is a challenging research. This study aims at using cervical cancer risk factors to build classification model using Random Forest (RF) classification technique with the synthetic minority oversampling technique (SMOTE) and two feature reduction techniques recursive feature elimination and principle component analysis (PCA). Most medical data sets are often imbalanced because the number of patients is much less than the number of non-patients. Because of the imbalance of the used data set, SMOTE is used to solve this problem. The data set consists of 32 risk factors and four target variables: Hinselmann, Schiller, Cytology, and Biopsy. After comparing the results, we find that the combination of the random forest classification technique with SMOTE improve the classification performance.

The importance of business performance measurement across industries has elevated in the last decade in what has been described as a revolution. Meanwhile, the construction industry has been criticised for its underperformance and the Latham and Egan reports emphasized the need for performance improvement and measurement. Companies have had to face the dilemma of choosing among different performance measurement frameworks. Hence, a need has been identified for a comprehensive framework. The aim of this research is to fulfil this need by building a conceptual framework for measuring the business performance of construction organizations. The framework had been formulated in previous research upon the principles of the Balanced Scorecard and Business Excellence Models. The research attempts to empirically evaluate and revise the framework through a series of expert interviews and case studies. In addition, empirical feedback has been used to: express the revised framework in a more communicative form, illustrate how business performance can be measured; and highlight the differences between the proposed framework and contemporary performance frameworks.