Data-Intensive Cloud Computing: Requirements, Expectations, Challenges, and Solutions

Data-intensive systems encompass terabytes to petabytes of data. Such systems require massive storage and intensive computational power in order to execute complex queries and generate timely results. Further, the rate at which this data is being generated induces extensive challenges of data storage, linking, and processing. A data-intensive cloud provides an abstraction of high availability, usability, and efficiency to users. However, underlying this abstraction, there are stringent requirements and challenges to facilitate scalable and resourceful services through effective physical infrastructure, smart networking solutions, intelligent software tools, and useful software approaches. This paper analyzes the extensive requirements which exist in data-intensive clouds, describes various challenges related to the paradigm, and assess numerous solutions in meeting these requirements and challenges. It provides a detailed study of the solutions and analyzes their capabilities in meeting emerging needs of widespread applications.     

Targeting spam control on middleboxes: Spam detection based on layer-3 e-mail content classification

This paper proposes a spam detection technique, at the packet level (layer 3), based on classification of e-mail contents. Our proposal targets spam control implementations on middleboxes. E-mails are first pre-classified (pre-detected) for spam on a per-packet basis, without the need for reassembly. This, in turn, allows fast e-mail class estimation (spam detection) at receiving e-mail servers to support more effective spam handling on both inbound and outbound (relayed) e-mails. In this paper, the naïve Bayes classification technique is adapted to support both pre-classification and fast e-mail class estimation, on a per-packet basis. We focus on evaluating the accuracy of spam detection at layer 3, considering the constraints on processing byte-streams over the network, including packet re-ordering, fragmentation, overlapped bytes, and different packet sizes. Results show that the proposed layer-3 classification technique gives less than 0.5% false positive, which approximately equals the performance attained at layer 7. This shows that classifying e-mails at the packet level could differentiate non-spam from spam with high confidence for a viable spam control implementation on middleboxes.     

A multi-objective strategy in genetic algorithms for gene selection of gene expression data

A microarray machine offers the capacity to measure the expression levels of thousands of genes simultaneously. It is used to collect information from tissue and cell samples regarding gene expression differences that could be useful for cancer classification. However, the urgent problems in the use of gene expression data are the availability of a huge number of genes relative to the small number of available samples, and the fact that many of the genes are not relevant to the classification. It has been shown that selecting a small subset of genes can lead to improved accuracy in the classification. Hence, this paper proposes a solution to the problems by using a multiobjective strategy in a genetic algorithm. This approach was tried on two benchmark gene expression data sets. It obtained encouraging results on those data sets as compared with an approach that used a single-objective strategy in a genetic algorithm. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Selecting informative genes from microarray data by using hybrid methods for cancer classification

Gene expression technology, namely microarrays, offers the ability to measure the expression levels of thousands of genes simultaneously in biological organisms. Microarray data are expected to be of significant help in the development of an efficient cancer diagnosis and classification platform. A major problem in these data is that the number of genes greatly exceeds the number of tissue samples. These data also have noisy genes. It has been shown in literature reviews that selecting a small subset of informative genes can lead to improved classification accuracy. Therefore, this paper aims to select a small subset of informative genes that are most relevant for cancer classification. To achieve this aim, an approach using two hybrid methods has been proposed. This approach is assessed and evaluated on two well-known microarray data sets, showing competitive results. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

DeepClassRooms: a deep learning based digital twin framework for on-campus class rooms

Neural Computing and Applications - A lot of different methods are being opted for improving the educational standards through monitoring of the classrooms. The developed world uses Smart...     

Breaking the Responsivity-Bandwidth Trade-Off Limit in GaN Photoelectrodes for High-Response and Fast-Speed Optical Communication Application

Underwater optical communication (UOC) has attracted considerable interest in the continuous expansion of human activities in marine/ocean environments. The water-durable and self-powered photoelectrodes that act as a battery-free light receiver in UOC are particularly crucial, as they may directly face complex underwater conditions. Emerging photoelectrochemical (PEC)-type photodetectors are appealing owing to their intrinsic aqueous operation characteristics with versatile tunability of photoresponses. Herein, a self-powered PEC photodetector employing n-type gallium nitride (GaN) nanowires as a photoelectrode, which is decorated with an iridium oxide (IrO_x) layer to optimize charge transfer dynamics at the GaN/electrolyte interface, is reported. Strikingly, the constructed n-GaN/IrO_x photoelectrode breaks the responsivity-bandwidth trade-off limit by simultaneously improving the response speed and responsivity, delivering an ultrafast response speed with response/recovery times of only 2 µs/4 µs while achieving a high responsivity of 110.1 mA W⁻¹. Importantly, the device exhibits a large bandwidth with 3 dB cutoff frequency exceeding 100 kHz in UOC tests, which is one of the highest values among self-powered photodetectors employed in optical communication system.     

Piezoelectric Sensors Operating at Very High Temperatures and in Extreme Environments Made of Flexible Ultrawide-Bandgap Single-Crystalline AlN Thin Films

Extreme environments are often faced in energy, transportation, aerospace, and defense applications and pose a technical challenge in sensing. Piezoelectric sensor based on single-crystalline AlN transducers is developed to address this challenge. The pressure sensor shows high sensitivities of 0.4–0.5 mV per psi up to 900 °C and output voltages from 73.3 to 143.2 mV for input gas pressure range of 50 to 200 psi at 800 °C. The sensitivity and output voltage also exhibit the dependence on temperature due to two origins. A decrease in elastic modulus (Young's modulus) of the diaphragm slightly enhances the sensitivity and the generation of free carriers degrades the voltage output beyond 800 °C, which also matches with theoretical estimation. The performance characteristics of the sensor are also compared with polycrystalline AlN and single-crystalline GaN thin films to investigate the importance of single crystallinity on the piezoelectric effect and bandgap energy-related free carrier generation in piezoelectric devices for high-temperature operation. The operation of the sensor at 900 °C is amongst the highest for pressure sensors and the inherent properties of AlN including chemical and thermal stability and radiation resistance indicate this approach offers a new solution for sensing in extreme environments.     

SNELM: SqueezeNet-Guided ELM for COVID-19 Recognition

(Aim) The COVID-19 has caused 6.26 million deaths and 522.06 million confirmed cases till 17/May/2022. Chest computed tomography is a precise way to help clinicians diagnose COVID-19 patients. (Method) Two datasets are chosen for this study. The multiple-way data augmentation, including speckle noise, random translation, scaling, salt-and-pepper noise, vertical shear, Gamma correction, rotation, Gaussian noise, and horizontal shear, is harnessed to increase the size of the training set. Then, the SqueezeNet (SN) with complex bypass is used to generate SN features. Finally, the extreme learning machine (ELM) is used to serve as the classifier due to its simplicity of usage, quick learning speed, and great generalization performances. The number of hidden neurons in ELM is set to 2000. Ten runs of 10-fold cross-validation are implemented to generate impartial results. (Result) For the 296-image dataset, our SNELM model attains a sensitivity of 96.35 ± 1.50%, a specificity of 96.08 ± 1.05%, a precision of 96.10 ± 1.00%, and an accuracy of 96.22 ± 0.94%. For the 640-image dataset, the SNELM attains a sensitivity of 96.00 ± 1.25%, a specificity of 96.28 ± 1.16%, a precision of 96.28 ± 1.13%, and an accuracy of 96.14 ± 0.96%. (Conclusion) The proposed SNELM model is successful in diagnosing COVID-19. The performances of our model are higher than seven state-of-the-art COVID-19 recognition models.     

A Framework of Deep Learning and Selection-Based Breast Cancer Detection from Histopathology Images

Breast cancer (BC) is a most spreading and deadly cancerous malady which is mostly diagnosed in middle-aged women worldwide and effecting beyond a half-million people every year. The BC positive newly diagnosed cases in 2018 reached 2.1 million around the world with a death rate of 11.6% of total cases. Early diagnosis and detection of breast cancer disease with proper treatment may reduce the number of deaths. The gold standard for BC detection is biopsy analysis which needs an expert for correct diagnosis. Manual diagnosis of BC is a complex and challenging task. This work proposed a deep learning-based (DL) solution for the early detection of this deadly disease from histopathology images. To evaluate the robustness of the proposed method a large publically available breast histopathology image database containing a total of 277524 histopathology images is utilized. The proposed automatic diagnosis of BC detection and classification mainly involves three steps. Initially, a DL model is proposed for feature extraction. Secondly, the extracted feature vector (FV) is passed to the proposed novel feature selection (FS) framework for the best FS. Finally, for the classification of BC into invasive ductal carcinoma (IDC) and normal class different machine learning (ML) algorithms are used. Experimental outcomes of the proposed methodology achieved the highest accuracy of 92.7% which shows that the proposed technique can successfully be implemented for BC detection to aid the pathologists in the early and accurate diagnosis of BC.     

Self-generation of reward by logarithmic transformation of multiple sensor evaluations

Artificial Life and Robotics - Although the design of the reward function in reinforcement learning is important, it is difficult to design a system that can adapt to a variety of environments and...