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Outstanding effectiveness of transformers in visual tasks has resulted in its fast growth and adoption in three dimensions (3D) vision tasks. Vision transformers have shown numerous advantages over earlier convolutional neural network (CNN) architectures including broad modelling abilities, more substantial modelling capabilities, convolution complementarity, scalability to model data size, and better connection for enhancing the performance records of many visual tasks. We present thorough review that classifies and summarizes the popular transformer-based approaches based on key features for transformer integration such as the input data, scalability element that enables transformer processing, architectural design, and context level through which the transformer functions as well as a highlight of the primary contributions of each transformer approach. Furthermore, we compare the results of these techniques with commonly employed non-transformer techniques in 3D object classification, segmentation, and object detection using standard 3D datasets including ModelNet, SUN RGB-D, ScanNet, nuScenes, Waymo, ShapeNet, S3DIS, and KITTI. This study also includes the discussion of numerous potential future options and limitation for 3D vision transformers. [ABSTRACT FROM AUTHOR]

The success of deep learning over the traditional machine learning techniques in handling artificial intelligence application tasks such as image processing, computer vision, object detection, speech recognition, medical imaging and so on, has made deep learning the buzz word that dominates Artificial Intelligence applications. From the last decade, the applications of deep learning in physiological signals such as electrocardiogram (ECG) have attracted a good number of research. However, previous surveys have not been able to provide a systematic comprehensive review including biometric ECG based systems of the applications of deep learning in ECG with respect to domain of applications. To address this gap, we conducted a systematic literature review on the applications of deep learning in ECG including biometric ECG based systems. The study analyzed systematically, 150 primary studies with evidence of the application of deep learning in ECG. The study shows that the applications of deep learning in ECG have been applied in different domains. We presented a new taxonomy of the domains of application of the deep learning in ECG. The paper also presented discussions on biometric ECG based systems and meta-data analysis of the studies based on the domain, area, task, deep learning models, dataset sources and preprocessing methods. Challenges and potential research opportunities were highlighted to enable novel research. We believe that this study will be useful to both new researchers and expert researchers who are seeking to add knowledge to the already existing body of knowledge in ECG signal processing using deep learning algorithm. [ABSTRACT FROM AUTHOR]

Deep learning (DL) models are becoming pervasive and applicable to computer vision, image processing, and synthesis problems. The performance of these models is often improved through architectural configuration, tweaks, the use of enormous training data, and skillful selection of hyperparameters. The application of deep learning models to medical image processing has yielded interesting performance, capable of correctly detecting abnormalities in medical digital images, making them surpass human physicians. However, advancing research in this domain largely relies on the availability of training datasets. These datasets are sometimes not publicly accessible, insufficient for training, and may also be characterized by a class imbalance among samples. As a result, inadequate training samples and difficulty in accessing new datasets for training deep learning models limit performance and research into new domains. Hence, generative adversarial networks (GANs) have been proposed to mediate this gap by synthesizing data similar to real sample images. However, we observed that benchmark datasets with regions of interest (ROIs) for characterizing abnormalities in breast cancer using digital mammography do not contain sufficient data with a fair distribution of all cases of abnormalities. For instance, the architectural distortion and breast asymmetry in digital mammograms are sparsely distributed across most publicly available datasets. This paper proposes a GAN model, named ROImammoGAN, which synthesizes ROI-based digital mammograms. Our approach involves the design of a GAN model consisting of both a generator and a discriminator to learn a hierarchy of representations for abnormalities in digital mammograms. Attention is given to architectural distortion, asymmetry, mass, and microcalcification abnormalities so that training distinctively learns the features of each abnormality and generates sufficient images for each category. The proposed GAN model was applied to MIAS datasets, and the performance evaluation yielded a competitive accuracy for the synthesized samples. In addition, the quality of the images generated was also evaluated using PSNR, SSIM, FSIM, BRISQUE, PQUE, NIQUE, FID, and geometry scores. The results showed that ROImammoGAN performed competitively with state-of-the-art GANs. The outcome of this study is a model for augmenting CNN models with ROI-centric image samples for the characterization of abnormalities in breast images. [ABSTRACT FROM AUTHOR]

Recently, cybercriminals have infiltrated different sectors of the human venture to launch ransomware attacks against information technology infrastructure. They demand ransom from individuals and industries, thereby inflicting significant loss of data. The use of intelligent algorithms for ransomware attack detection began to gain popularity in recent times and proved feasible. However, no comprehensive dedicated literature review on the applications of intelligent machine learning algorithms to detect ransomware attacks on information technology infrastructure. Unlike the previous reviews on ransomware attacks, this paper aims to conduct a comprehensive survey on the detection of ransomware attacks using intelligent machine learning algorithms. The study analysed literature from different perspectives focusing on intelligent algorithms detection of ransomware. The survey shows that there is a growing interest in recent times (2016—date) on the application of intelligent algorithms for ransomware detection. Deep learning algorithms are gaining tremendous attention because of their ability to handle large scale datasets, prominence in the research community, and ability to solve problems better than the conventional intelligent algorithms. To date, the potentials of big data analytics are yet to be fully exploited for the smart detection of ransomware attacks. Future research opportunities from the perspective of deep learning and big data analytics to solve the challenges identified from the survey are outlined to give the research community a new direction in dealing with ransomware attacks. [ABSTRACT FROM AUTHOR]

Research in metaheuristics for global optimization problems are currently experiencing an overload of wide range of available metaheuristic-based solution approaches. Since the commencement of the first set of classical metaheuristic algorithms namely genetic, particle swarm optimization, ant colony optimization, simulated annealing and tabu search in the early 70s to late 90s, several new advancements have been recorded with an exponential growth in the novel proposals of new generation metaheuristic algorithms. Because these algorithms are neither entirely judged based on their performance values nor according to the useful insight they may provide, but rather the attention is given to the novelty of the processes they purportedly models, these area of study will continue to periodically see the arrival of several new similar techniques in the future. However, there is an obvious reason to keep track of the progressions of these algorithms by collating their general algorithmic profiles in terms of design inspirational source, classification based on swarm or evolutionary search concept, existing variation from the original design, and application areas. In this paper, we present a relatively new taxonomic classification list of both classical and new generation sets of metaheuristic algorithms available in the literature, with the aim of providing an easily accessible collection of popular optimization tools for the global optimization research community who are at the forefront in utilizing these tools for solving complex and difficult real-world problems. Furthermore, we also examined the bibliometric analysis of this field of metaheuristic for the last 30 years. [ABSTRACT FROM AUTHOR]

The purpose of smart city is to enhance the optimal utilization of scarce resources and improve the resident's quality of live. The smart cities employed Internet of Things (IoT) to create a sustainable urban life. The IoT devices such as sensors, actuators, and smartphones in the smart cities generate data. The data generated from the smart cities are subjected to analytics to gain insight and discover new knowledge for improving the efficiency and effectiveness of the smart cities. Recently, the application of deep learning in smart cities has gained a tremendous attention from the research community. However, despite raise in popularity and achievements made by deep learning in solving problems in smart cities, no survey has been dedicated mainly on the application of deep learning in smart cities to show recent progress and direction for future development. To bridge this gap, this paper proposes to conduct a dedicated survey on the applications of deep learning in smart cities. In this paper, recent progress, new taxonomies, challenges and opportunities for future research opportunities on the application of deep learning in smart cities have been unveiled. The paper can provide opportunities for experts in the research community to propose a novel approach for developing the research area. On the other hand, new researchers interested in the research area can use the paper as an entry point. [ABSTRACT FROM AUTHOR]

Whale optimization algorithm (WOA) is a recently developed swarm-based meta-heuristic algorithm that is based on the bubble-net hunting maneuver technique—of humpback whales—for solving the complex optimization problems. It has been widely accepted swarm intelligence technique in various engineering fields due to its simple structure, less required operator, fast convergence speed and better balancing capability between exploration and exploitation phases. Owing to its optimal performance and efficiency, the applications of the algorithm have extensively been utilized in multidisciplinary fields in the recent past. This paper investigates further into WOA of its applications, modifications, and hybridizations across various fields of engineering. The description of the strengths, weaknesses and opportunities to support future research are also explored. The Systematic Literature Review is opted as a method to disseminate the findings and gap from the existing literature. The authors select eighty-two (82) articles as a primary studies out of nine hundred and thirty-nine (939) articles between 2016 and 2020. As per our result, WOA-based techniques are applied in 5 fields and 17 subfields of various engineering domains. 61% work has been found on modification, 27% on hybridization and 12% on multi-objective variants of WOA techniques. The growing research trend on WOA is expected to continue into the future. The review presented in the paper has the potential to motivate expert researchers to propose more novel WOA-based algorithms, and it can serve as an initial reading material for a novice researcher. [ABSTRACT FROM AUTHOR]

Due to the exponential increase in research papers on a daily basis, finding and accessing related academic documents over the Internet is monotonous. One of the leading approaches was the use of recommendation systems to proactively recommend scholarly papers to individual researchers. The primary drawback to these methods, however, is that their success depends on user profile information and is therefore unable to provide useful suggestions to the new user. In addition, both the public and the non-public used descriptive metadata are used. The scope of the recommendation is therefore limited to a number of documents which are either publicly available or which are granted copyright permits. In alleviating the above problems, we proposed an alternative approach using public contextual metadata for an independent framework that customizes scholarly papers, regardless of the research field and user expertise. Experimental tests have shown significant improvements over other baseline methods. [ABSTRACT FROM AUTHOR]

Mobile phone touch screen devices are equipped with high processing power and high memory. This led to users not only storing photos or videos but stored sensitive application such as banking applications. As a result of that the security system of the mobile phone touch screen devices becomes sacrosanct. The application of machine learning algorithms in enhancing security on mobile phone touch screen devices is gaining a tremendous popularity in both academia and the industry. However, notwithstanding the growing popularity, up to date no comprehensive survey has been conducted on machine learning algorithms solutions to improve the security of mobile phone touch screen devices. This survey aims to connect this gap by conducting a comprehensive survey on the solutions of machine learning algorithms to improve the security of mobile phone touch screen devices including the analysis and synthesis of the algorithms and methodologies provided for those solutions. This article presents a comprehensive survey and a new taxonomy of the state-of-the-art literature on machine learning algorithms in improving the security of mobile phone touch screen devices. The limitation of the methodology in each article reviewed is pointed out. Challenges of the existing approaches and new perspective of future research directions for developing more accurate and robust solutions to mobile phone touch screen security are discussed. In particular, the survey found that exploring of different aspects of deep learning solutions to improve the security of mobile phone touch screen devices is under-explored. [ABSTRACT FROM AUTHOR]

Recent trends in big data have shown that the amount of data continues to increase at an exponential rate. This trend has inspired many researchers over the past few years to explore new research direction of studies related to multiple areas of big data. The widespread popularity of big data processing platforms using MapReduce framework is the growing demand to further optimize their performance for various purposes. In particular, enhancing resources and jobs scheduling are becoming critical since they fundamentally determine whether the applications can achieve the performance goals in different use cases. Scheduling plays an important role in big data, mainly in reducing the execution time and cost of processing. This paper aims to survey the research undertaken in the field of scheduling in big data platforms. Moreover, this paper analyzed scheduling in MapReduce on two aspects: taxonomy and performance evaluation. The research progress in MapReduce scheduling algorithms is also discussed. The limitations of existing MapReduce scheduling algorithms and exploit future research opportunities are pointed out in the paper for easy identification by researchers. Our study can serve as the benchmark to expert researchers for proposing a novel MapReduce scheduling algorithm. However, for novice researchers, the study can be used as a starting point. [ABSTRACT FROM AUTHOR]

Numerous types of hybridizations between type 2 fuzzy logic system (T2FLS) and sliding mode control (SMC) have been proposed to construct an intelligent and robust controller that departs from the drawbacks of SMC and T2FLS. Recently, these hybridizations have been extended to the hybrid structures that are composed of type 2 fuzzy neural network (T2FNN) and SMC in order to produce adaptive, intelligent and robust controllers. Moreover, optimization algorithms are integrated with these controllers in order to tune/optimize their parameters for a superior control performance. In this paper, a survey of the advances on the hybridization of T2FLS, T2FNN, SMC and computational intelligence algorithms is presented. It has been observed that all the works involving T2FLS employed interval type 2 fuzzy logic systems. Despite the advantages of general type 2 fuzzy logic systems (GT2FLS), no record of applying GT2FLSs has been encountered in this domain. The trend of publications, the limitations associated with previous works and future research directions are outlined in the paper. Expert researchers can use this survey as a benchmark for proposing novel approaches while novice researchers (especially graduate students) can use this survey as a starting point. [ABSTRACT FROM AUTHOR]

Prior research studies have shown that the peak signal to noise ratio (PSNR) is the most frequent watermarked image quality metric that is used for determining the levels of strength and weakness of watermarking algorithms. Conversely, normalised cross correlation (NCC) is the most common metric used after attacks were applied to a watermarked image to verify the strength of the algorithm used. Many researchers have used these approaches to evaluate their algorithms. These strategies have been used for a long time, however, which unfortunately limits the value of PSNR and NCC in reflecting the strength and weakness of the watermarking algorithms. This paper considers this issue to determine the threshold values of these two parameters in reflecting the amount of strength and weakness of the watermarking algorithms. We used our novel watermarking technique for embedding four watermarks in intermediate significant bits (ISB) of six image files one-by-one through replacing the image pixels with new pixels and, at the same time, keeping the new pixels very close to the original pixels. This approach gains an improved robustness based on the PSNR and NCC values that were gathered. A neural network model was built that uses the image quality metrics (PSNR and NCC) values obtained from the watermarking of six grey-scale images that use ISB as the desired output and that are trained for each watermarked image's PSNR and NCC. The neural network predicts the watermarked image's PSNR together with NCC after the attacks when a portion of the output of the same or different types of image quality metrics (PSNR and NCC) are obtained. The results indicate that the NCC metric fluctuates before the PSNR values deteriorate. [ABSTRACT FROM AUTHOR]

In data mining, association rules mining is one of the common and popular techniques used in various domain applications. The purpose of this study is to apply an enhanced association rules mining method, so called significant least pattern growth for capturing interesting rules from students suffering from exam, family, presentation and library anxiety datasets. The datasets were taken from a survey among engineering students in Universiti Malaysia Pahang. The results of this research will provide useful information for educators to make more accurate decisions concerning their students, and to adapt their teaching strategies accordingly. Moreover, it can also highlight the role of non-academic staff in supporting learning environments for students. The obtained findings can be very helpful in assisting students to handle their fear and anxiety, and, finally, increasing the quality of the learning processes at the university. [ABSTRACT FROM AUTHOR]