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A new framework in the form of Polarimetric Synthetic Aperture Radar (PolSAR) image classification, where deep Convolutional Neural Networks (CNNs) were integrated with the traditional Machine Learning (ML) techniques under a Temporal Convolutional Network (TCN) architecture, was introduced in the paper. The main aim behind this new approach is to overcome the severe limitations inherent in both deep CNN and conventional ML approaches. The application of the sliding-window strategy eliminates the necessity of requiring extensive feature extraction procedures while reducing computational complexity simultaneously. Experiments on four benchmark PolSAR datasets for C-Band, L-Band, AIRSAR, and RADARSAT-2 data attest to the framework's remarkable classification accuracies in the range of 94.55% to 99.39%. This integrated framework is thus a significant advancement in PolSAR image analysis in offering an efficient methodology that combines the strengths of deep CNNs and traditional ML, by mitigating their respective limitations. It also combines the sliding-window technique with the architecture of TCN and then yields excellent classification accuracy with no much additional computational overhead. The results obtained thus indicate a good chance of revolutionizing the state of the art in PolSAR image classification, providing crucial efficiency improvements and making applications in environmental applications stronger, across almost all kinds of fields. more...

The proliferation of Internet of Things (IoT) devices has become inevitable in contemporary life, significantly affecting myriad applications. Nevertheless, the pervasive use of heterogeneous IoT gadgets introduces vulnerabilities to malicious cyber-attacks, resulting in data breaches that jeopardize the network’s integrity and resilience. This study proposes an Intrusion Detection System (IDS) for IoT environments that leverages Transfer Learning (TL) and the Convolutional Block Attention Module (CBAM). We extensively evaluate four prominent pre-trained models, each integrated with an independent CBAM at the uppermost layer. Our methodology is validated using the BoT-IoT dataset, which undergoes preprocessing to rectify the imbalanced data distribution, eliminate redundancy, and reduce dimensionality. Subsequently, the tabular dataset is transformed into RGB images to enhance the interpretation of complex patterns. Our evaluation results demonstrate that integrating TL models with the CBAM significantly improves classification accuracy and reduces false-positive rates. Additionally, to further enhance the system performance, we employ an Ensemble Learning (EL) technique to aggregate predictions from the two best-performing models. The final findings prove that our TL-CBAM-EL model achieves superior performance, attaining an accuracy of 99.93% as well as high recall, precision, and F1-score. Henceforth, the proposed IDS is a robust and efficient solution for securing IoT networks. more...

One of the main reasons for human death is diseases caused by the heart. Detecting heart diseases in the early stage can stop heart failure or any damage related to the heart muscle. One of the main signals that can be beneficial in the diagnosis of diseases of the heart is the electrocardiogram (ECG). This paper concentrates on the diagnosis of four types of ECG records such as myocardial infarction (MYC), normal (N), variances in the ST-segment (ST), and supraventricular arrhythmia (SV). The methodology captures the data from six main datasets, and then the ECG records are filtered using a pre-processing chain. Afterward, a proposed 1D CNN model is applied to extract features from the ECG records. Then, two different classifiers are applied to test the extracted features’ performance and obtain a robust diagnosis accuracy. The two classifiers are the softmax and random forest (RF) classifiers. An experiment is applied to diagnose the four types of ECG records. Finally, the highest performance was achieved using the RF classifier, reaching an accuracy of 98.3%. The comparison with other related works showed that the proposed methodology could be applied as a medical application for the early detection of heart diseases. more...

Abstract Arabic automatic speech recognition (ASR) methods with diacritics have the ability to be integrated with other systems better than Arabic ASR methods without diacritics. In this work, the application of state‐of‐the‐art end‐to‐end deep learning approaches is investigated to build a robust diacritised Arabic ASR. These approaches are based on the Mel‐Frequency Cepstral Coefficients and the log Mel‐Scale Filter Bank energies as acoustic features. To the best of our knowledge, end‐to‐end deep learning approach has not been used in the task of diacritised Arabic automatic speech recognition. To fill this gap, this work presents a new CTC‐based ASR, CNN‐LSTM, and an attention‐based end‐to‐end approach for improving diacritisedArabic ASR. In addition, a word‐based language model is employed to achieve better results. The end‐to‐end approaches applied in this work are based on state‐of‐the‐art frameworks, namely ESPnet and Espresso. Training and testing of these frameworks are performed based on the Standard Arabic Single Speaker Corpus (SASSC), which contains 7 h of modern standard Arabic speech. Experimental results show that the CNN‐LSTM with an attention framework outperforms conventional ASR and the Joint CTC‐attention ASR framework in the task of Arabic speech recognition. The CNN‐LSTM with an attention framework could achieve a word error rate better than conventional ASR and the Joint CTC‐attention ASR by 5.24% and 2.62%, respectively. more...

The security of mobile applications has become a major research field which is associated with a lot of challenges. The high rate of developing mobile applications has resulted in less secure applications. This is due to what is called the “rush to release” as defined by Ponemon Institute. Security testing—which is considered one of the main phases of the development life cycle—is either not performed or given minimal time; hence, there is a need for security testing automation. One of the techniques used is Automated Vulnerability Detection. Vulnerability detection is one of the security tests that aims at pinpointing potential security leaks. Fixing those leaks results in protecting smart-phones and tablet mobile device users against attacks. This paper focuses on building a hybrid approach of static and dynamic analysis for detecting the vulnerabilities of Android applications. This approach is capsuled in a usable platform (web application) to make it easy to use for both public users and professional developers. Static analysis, on one hand, performs code analysis. It does not require running the application to detect vulnerabilities. Dynamic analysis, on the other hand, detects the vulnerabilities that are dependent on the run-time behaviour of the application and cannot be detected using static analysis. The model is evaluated against different applications with different security vulnerabilities. Compared with other detection platforms, our model detects information leaks as well as insecure network requests alongside other commonly detected flaws that harm users’ privacy. The code is available through a GitHub repository for public contribution. more...

One of the main reasons for human death is diseases caused by the heart. Detecting heart diseases in the early stage can stop heart failure or any damage related to the heart muscle. One of the main signals that can be beneficial in the diagnosis of diseases of the heart is the electrocardiogram (ECG). This paper concentrates on the diagnosis of four types of ECG records such as myocardial infarction (MYC), normal (N), variances in the ST-segment (ST), and supraventricular arrhythmia (SV). The methodology captures the data from six main datasets, and then the ECG records are filtered using a pre-processing chain. Afterward, a proposed 1D CNN model is applied to extract features from the ECG records. Then, two different classifiers are applied to test the extracted features’ performance and obtain a robust diagnosis accuracy. The two classifiers are the softmax and random forest (RF) classifiers. An experiment is applied to diagnose the four types of ECG records. Finally, the highest performance was achieved using the RF classifier, reaching an accuracy of 98.3%. The comparison with other related works showed that the proposed methodology could be applied as a medical application for the early detection of heart diseases. [ABSTRACT FROM AUTHOR] more...

The proliferation of Internet of Things (IoT) devices has become inevitable in contemporary life, significantly affecting myriad applications. Nevertheless, the pervasive use of heterogeneous IoT gadgets introduces vulnerabilities to malicious cyber-attacks, resulting in data breaches that jeopardize the network's integrity and resilience. This study proposes an Intrusion Detection System (IDS) for IoT environments that leverages Transfer Learning (TL) and the Convolutional Block Attention Module (CBAM). We extensively evaluate four prominent pre-trained models, each integrated with an independent CBAM at the uppermost layer. Our methodology is validated using the BoT-IoT dataset, which undergoes preprocessing to rectify the imbalanced data distribution, eliminate redundancy, and reduce dimensionality. Subsequently, the tabular dataset is transformed into RGB images to enhance the interpretation of complex patterns. Our evaluation results demonstrate that integrating TL models with the CBAM significantly improves classification accuracy and reduces false-positive rates. Additionally, to further enhance the system performance, we employ an Ensemble Learning (EL) technique to aggregate predictions from the two best-performing models. The final findings prove that our TL-CBAM-EL model achieves superior performance, attaining an accuracy of 99.93% as well as high recall, precision, and F1-score. Henceforth, the proposed IDS is a robust and efficient solution for securing IoT networks. [ABSTRACT FROM AUTHOR] more...

Numerous researches on content-based video indexing and retrieval besides video search engines are tied to a large-scaled video dataset. Unfortunately, reduction in open-sourced datasets resulted in complications for novel approaches exploration. Although, video datasets that index video files located on public video streaming services have other purposes, such as annotation, learning, classification, and other computer vision areas, with little interest in indexing public video links for purpose of searching and retrieval. This paper introduces a novel large-scaled dataset based on YouTube video links to evaluate the proposed content-based video search engine, gathered 1088 videos, that represent more than 65 hours of video, 11,000 video shots, and 66,000 unmarked and marked keyframes, 80 different object names used for marking. Moreover, a state-of-the-art features vector, and combinational-based matching, beneficial to the accuracy, speed, and precision of the video retrieval process. Any video record in the dataset is represented by three features: temporal combination vector, object combination vector with shot annotations, and 6 keyframes, sideways with other metadata. Video classification for the dataset was also imposed to expand the efficiency of retrieval of video-based queries. A two-phased approach has been used based on object and event classification, storing video records in aggregations related to feature vectors extracted. While object aggregation stores video records with the maximal occurrence of extracted object/concept from all shots, event aggregation classify based on groups according to the number of shots per video. This study indexed 58 out of 80 different object/concept categories, each has 9 shot number groups. [ABSTRACT FROM AUTHOR] more...

A new framework in the form of Polarimetric Synthetic Aperture Radar (PolSAR) image classification, where deep Convolutional Neural Networks (CNNs) were integrated with the traditional Machine Learning (ML) techniques under a Temporal Convolutional Network (TCN) architecture, was introduced in the paper. The main aim behind this new approach is to overcome the severe limitations inherent in both deep CNN and conventional ML approaches. The application of the sliding-window strategy eliminates the necessity of requiring extensive feature extraction procedures while reducing computational complexity simultaneously. Experiments on four benchmark PolSAR datasets for C-Band, L-Band, AIRSAR, and RADARSAT-2 data attest to the framework's remarkable classification accuracies in the range of 94.55% to 99.39%. This integrated framework is thus a significant advancement in PolSAR image analysis in offering an efficient methodology that combines the strengths of deep CNNs and traditional ML, by mitigating their respective limitations. It also combines the sliding-window technique with the architecture of TCN and then yields excellent classification accuracy with no much additional computational overhead. The results obtained thus indicate a good chance of revolutionizing the state of the art in PolSAR image classification, providing crucial efficiency improvements and making applications in environmental applications stronger, across almost all kinds of fields. [ABSTRACT FROM AUTHOR] more...

A longer battery life is a highly sought-after feature for most smartphone users when considering their next device. However, with the emergence of new hardware technology and software applications that require heavy processing, the demand for battery power has significantly increased. Unfortunately, the development of battery technology has not kept up with the rapid advancements in smartphone hardware and software, which rely heavily on battery power. To address this issue, several approaches have been proposed to regulate battery consumption and the charging process on smartphones. In this paper, we summarize the different approaches related to this problem that managed to achieve up to a 61% increase in battery daily usage in simulation testing, highlighting their strengths, limitations, and current challenges. Furthermore, we provide a comprehensive review of various open-source datasets that have the potential to be used in developing new approaches to improve battery drainage and degradation in smartphones. We also discuss the methodology for collecting each dataset. Finally, we propose a new approach to address the current limitations and challenges to solving the problem of battery drainage and degradation that could be developed using the currently available datasets. These new approaches may involve incorporating Machine Learning (ML) techniques to predict battery charging patterns and minimize battery drainage. [ABSTRACT FROM AUTHOR] more...

The severe circumstances caused by COVID-19 make online education the best replacement for regular face-to-face education for continuing the education process. One year ago, and till now most schools adopted online learning during this pandemic shutdown, which indicates the applicability of this teaching methodology. However, the efficiency of this method needs to be improved to guarantee its effectiveness. Although face-to-face teaching has many advantages over online education, there is a chance to promote online learning by utilizing the recent techniques of artificial intelligence. From this perspective, we propose a framework to detect and recognize emotions in the speech of students during virtual classes to keep instructors updated with the feelings of students so and can behave accordingly. The approach of detecting emotions from the speech is much more helpful for cases when turning on the cameras at the student's side could be embarrassing. This case is very common, especially for schools in Middle East countries. The proposed framework can also be applied to other similar scenarios such as online meetings. [ABSTRACT FROM AUTHOR] more...

One of the pandemics that have caused many deaths is the Coronavirus disease 2019 (COVID-19). It first appeared in late 2019, and many deaths are increasing day by day until now. Therefore, the early diagnosis of COVID-19 has become a salient issue. Additionally, the current diagnosis methods have several demerits, and a new investigation is required to enhance the diagnosis performance. In this paper, a set of phases are performed, such as collecting data, filtering and augmenting images, extracting features, and classifying ECG images. The data were obtained from two publicly available ECG image datasets, and one of them contained COVID ECG reports. A set of preprocessing methods are applied to the ECG images, and data augmentation is performed to balance the ECG images based on the classes. A deep learning approach based on a convolutional neural network (CNN) is performed for feature extraction. Four different pre-trained models are applied, such as Vgg16, Vgg19, ResNet-101, and Xception. Moreover, an ensemble of Xception and the temporary convolutional network (TCN), which is named ECGConvnet, is proposed. Finally, the results obtained from the former models are fed to four main classifiers. These classifiers are softmax, random forest (RF), multilayer perception (MLP), and support vector machine (SVM). The former classifiers are used to evaluate the diagnosis ability of the proposed methods. The classification scenario is based on fivefold cross-validation. Seven experiments are presented to evaluate the performance of the ECGConvnet. Three of them are multi-class, and the remaining are binary class diagnosing. Six out of seven experiments diagnose COVID-19 patients. The aforementioned experimental results indicated that ECGConvnet has the highest performance over other pre-trained models, and the SVM classifier showed higher accuracy in comparison with the other classifiers. The resulting accuracies from ECGConvnet based on SVM are (99.74%, 98.6%, 99.1% on the multi-class diagnosis tasks) and (99.8% on one of the binary-class diagnoses, while the remaining achieved 100%). It is possible to develop an automatic diagnosis system for COVID based on deep learning using ECG data. [ABSTRACT FROM AUTHOR] more...

Heart diseases are one of the main reasons that cause human death. The early-stage detection of heart diseases can prevent irreversible heart muscle damage or heart failure. Electrocardiogram (ECG) is one of the main heart signals that can be useful in early diagnosis because of its obvious peaks and segments. This paper focuses on using a methodology depending on deep learning for the diagnosis of the electrocardiogram records into normal (N), Supraventricular arrhythmia (SV), ST-segment changes (ST), and myocardial infarction (MYC) conditions. The continuous wavelet transform (CWT) converts the ECG signals to the time-frequency domain to compute the scalogram of the ECG signals and for the conversion of ECG signal from one dimension signal to a two-dimension image. In addition to this, a pertained model using transfer learning is applied based on Resnet50. Moreover, three main classifiers are verified to estimate the accuracy of the proposed system which are based on the Softmax, Random Forest (RF), and XGBoost classifier. An experiment is applied for the diagnosis of four main kinds of ECG records. Finally, the results based on the class-oriented schema achieved an accuracy of 98.3% based on Resnet50 with the XGBoost classifier. The comparison with the related previous work presented the excellent performance of the proposed methodology as it can be applied as a clinical application. [ABSTRACT FROM AUTHOR] more...

Arabic language has a set of sound letters called diacritics, these diacritics play an essential role in the meaning of words and their articulations. The change in some diacritics leads to a change in the context of the sentence. However, the existence of these letters in the corpus transcription affects the accuracy of speech recognition. In this paper, we investigate the effect of diactrics on the Arabic speech recognition based end-to-end deep learning. The applied end-to-end approach includes CNN-LSTM and attention-based technique presented in the state-of-the-art framework namely, Espresso using Pytorch. In addition, and to the best of our knowledge, the approach of CNN-LSTM with attention-based has not been used in the task of Arabic Automatic speech recognition (ASR). To fill this gap, this paper proposes a new approach based on CNN-LSTM with attention based method for Arabic ASR. The language model in this approach is trained using RNN-LM and LSTM-LM and based on nondiacritized transcription of the speech corpus. The Standard Arabic Single Speaker Corpus (SASSC), after omitting the diacritics, is used to train and test the deep learning model. Experimental results show that the removal of diacritics decreased out-of-vocabulary and perplexity of the language model. In addition, the word error rate (WER) is significantly improved when compared to diacritized data. The achieved average reduction in WER is 13.52%. [ABSTRACT FROM AUTHOR] more...

Arabic automatic speech recognition (ASR) methods with diacritics have the ability to be integrated with other systems better than Arabic ASR methods without diacritics. In this work, the application of state‐of‐the‐art end‐to‐end deep learning approaches is investigated to build a robust diacritised Arabic ASR. These approaches are based on the Mel‐Frequency Cepstral Coefficients and the log Mel‐Scale Filter Bank energies as acoustic features. To the best of our knowledge, end‐to‐end deep learning approach has not been used in the task of diacritised Arabic automatic speech recognition. To fill this gap, this work presents a new CTC‐based ASR, CNN‐LSTM, and an attention‐based end‐to‐end approach for improving diacritisedArabic ASR. In addition, a word‐based language model is employed to achieve better results. The end‐to‐end approaches applied in this work are based on state‐of‐the‐art frameworks, namely ESPnet and Espresso. Training and testing of these frameworks are performed based on the Standard Arabic Single Speaker Corpus (SASSC), which contains 7 h of modern standard Arabic speech. Experimental results show that the CNN‐LSTM with an attention framework outperforms conventional ASR and the Joint CTC‐attention ASR framework in the task of Arabic speech recognition. The CNN‐LSTM with an attention framework could achieve a word error rate better than conventional ASR and the Joint CTC‐attention ASR by 5.24% and 2.62%, respectively. [ABSTRACT FROM AUTHOR] more...

End-to-end deep learning approach has greatly enhanced the performance of speech recognition systems. With deep learning techniques, the overfitting stills the main problem with a little data. Data augmentation is a suitable solution for the overfitting problem, which is adopted to improve the quantity of training data and enhance robustness of the models. In this paper, we investigate data augmentation method for enhancing Arabic automatic speech recognition (ASR) based on end-to-end deep learning. Data augmentation is applied on original corpus for increasing training data by applying noise adaptation, pitch-shifting, and speed transformation. An CNN-LSTM and attentionbased encoder-decoder method are included in building the acoustic model and decoding phase. This method is considered as state-of-art in end-to-end deep learning, and to the best of our knowledge, there is no prior research employed data augmentation for CNN-LSTM and attention-based model in Arabic ASR systems. In addition, the language model is built using RNN-LM and LSTM-LM methods. The Standard Arabic Single Speaker Corpus (SASSC) without diacritics is used as an original corpus. Experimental results show that applying data augmentation improved word error rate (WER) when compared with the same approach without data augmentation. The achieved average reduction in WER is 4.55%. [ABSTRACT FROM AUTHOR] more...

In wireless environment researches on security issues in various layering level of the networks are focused recent times. One of the major issue is denial of service attacks. This paper mainly deals with the detection of syn flooding attacks which is one form of denial of service attacks in wireless sensor networks. It is a type of attack done by the attacker to a specific server to down them by flooding the requests. So, the server will be busy waiting for the requests created by the attacker. In view to this attack an efficient routing approach by distance-2 dominating set is proposed to exhibit the plan of clustering the nodes in the network for effective data transmission. The traffic limit method is used to monitor the bandwidth usage of the nodes concerned in the network to find the flooding attacks in real time event detection environment. The test cases are implemented using network simulation tool. The outcomes discussed about here are to demonstrate the packet delivery ratio, end-to-end delay and the bandwidth usage by the malicious nodes which will be high of the various other authorized nodes in the system. [ABSTRACT FROM AUTHOR] more...

Energy Efficiency has become a crucial concern in modern data centers. Dynamic VM consolidation is one of the effective approaches endorsed to achieve energy efficiency in cloud data centers hosting thousands of servers. Live migration is a core feature enabling VM consolidation. However, live migration is a costly operation imposing energy and performance overhead. An efficient dynamic virtual machine consolidate should consider the cost due to live migration. In this paper, we design and implement a dynamic VM consolidation algorithm based on simulated annealing that accounts for the migration cost imposed by a consolidation plan. We conduct simulation-based experiments on CloudSim using real cloud workload traces from PlanetLab to evaluate the performance of the proposed algorithm. Results show that the using the proposed algorithm, the simulated data center consumes almost the same amount of energy of that using a FF based consolidation. However, the proposed algorithm accounts for the cost due to live migration and hence, reducing SLA violations and performance degradations. [ABSTRACT FROM AUTHOR] more...

The main objective of this study was to understand the residue and persistence behaviour of new insecticide chlorantraniliprole in tomato fruit and soil samples. Its residue was analyzed by HPLC and it dissipated in tomato fruit and soil following first order kinetics. The results showed half life (t) value of 3.30 and 3.66 days for chlorantraniliprole in tomato fruit and soil, respectively. According to maximum residue limit (MRL) the pre-harvest interval (PHI) of chlorantraniliprole on tomato was 8-days after the treatment. [ABSTRACT FROM AUTHOR] more...

The security of mobile applications has become a major research field which is associated with a lot of challenges. The high rate of developing mobile applications has resulted in less secure applications. This is due to what is called the "rush to release" as defined by Ponemon Institute. Security testing—which is considered one of the main phases of the development life cycle—is either not performed or given minimal time; hence, there is a need for security testing automation. One of the techniques used is Automated Vulnerability Detection. Vulnerability detection is one of the security tests that aims at pinpointing potential security leaks. Fixing those leaks results in protecting smart-phones and tablet mobile device users against attacks. This paper focuses on building a hybrid approach of static and dynamic analysis for detecting the vulnerabilities of Android applications. This approach is capsuled in a usable platform (web application) to make it easy to use for both public users and professional developers. Static analysis, on one hand, performs code analysis. It does not require running the application to detect vulnerabilities. Dynamic analysis, on the other hand, detects the vulnerabilities that are dependent on the run-time behaviour of the application and cannot be detected using static analysis. The model is evaluated against different applications with different security vulnerabilities. Compared with other detection platforms, our model detects information leaks as well as insecure network requests alongside other commonly detected flaws that harm users' privacy. The code is available through a GitHub repository for public contribution. [ABSTRACT FROM AUTHOR] more...