A Joint Resource Allocation, Security with Efficient Task Scheduling in Cloud Computing Using Hybrid Machine Learning Techniques.

The rapid growth of cloud computing environment with many clients ranging from personal users to big corporate or business houses has become a challenge for cloud organizations to handle the massive volume of data and various resources in the cloud. Inefficient management of resources can degrade the performance of cloud computing. Therefore, resources must be evenly allocated to different stakeholders without compromising the organization's profit as well as users' satisfaction. A customer's request cannot be withheld indefinitely just because the fundamental resources are not free on the board. In this paper, a combined resource allocation security with efficient task scheduling in cloud computing using a hybrid machine learning (RATS-HM) technique is proposed to overcome those problems. The proposed RATS-HM techniques are given as follows: First, an improved cat swarm optimization algorithm-based short scheduler for task scheduling (ICS-TS) minimizes the make-span time and maximizes throughput. Second, a group optimization-based deep neural network (GO-DNN) for efficient resource allocation using different design constraints includes bandwidth and resource load. Third, a lightweight authentication scheme, i.e., NSUPREME is proposed for data encryption to provide security to data storage. Finally, the proposed RATS-HM technique is simulated with a different simulation setup, and the results are compared with state-of-art techniques to prove the effectiveness. The results regarding resource utilization, energy consumption, response time, etc., show that the proposed technique is superior to the existing one.

A Systematic Review on Machine Learning and Deep Learning Models for Electronic Information Security in Mobile Networks.

Today's advancements in wireless communication technologies have resulted in a tremendous volume of data being generated. Most of our information is part of a widespread network that connects various devices across the globe. The capabilities of electronic devices are also increasing day by day, which leads to more generation and sharing of information. Similarly, as mobile network topologies become more diverse and complicated, the incidence of security breaches has increased. It has hampered the uptake of smart mobile apps and services, which has been accentuated by the large variety of platforms that provide data, storage, computation, and application services to end-users. It becomes necessary in such scenarios to protect data and check its use and misuse. According to the research, an artificial intelligence-based security model should assure the secrecy, integrity, and authenticity of the system, its equipment, and the protocols that control the network, independent of its generation, in order to deal with such a complicated network. The open difficulties that mobile networks still face, such as unauthorised network scanning, fraud links, and so on, have been thoroughly examined. Numerous ML and DL techniques that can be utilised to create a secure environment, as well as various cyber security threats, are discussed. We address the necessity to develop new approaches to provide high security of electronic data in mobile networks because the possibilities for increasing mobile network security are inexhaustible.

Fluid-Structure Coupling Effects in a Dual U-Tube Coriolis Mass Flow Meter.

Coriolis mass flowmeters are highly customized products involving high-degree fluid-structure coupling dynamics and high-precision manufacture. The typical delay from from order to shipment is at least 4 months. This paper presents some important design considerations through simulation and experiments, so as to provide manufacturers with a more time-efficient product design and manufacture process. This paper aims at simulating the fluid-structure coupling dynamics of a dual U-tube Coriolis mass flowmeter through the COMSOL simulation package. The simulation results are experimentally validated using a dual U-tube CMF manufactured by Yokogawa Co., Ltd. in a TAF certified flow testing factory provided by FineTek Co., Ltd. Some important design considerations are drawn from simulation and experiment. The zero drift will occur when the dual U-tube structure is unbalanced and therefore the dynamic balance is very important in the manufacturing of dual U-tube CMF. The fluid viscosity can be determined from the driving current of the voice coil actuator or the pressure loss between the inlet and outlet of CMF. Finally, the authors develop a simulation application based on COMSOL's development platform. Users can quickly evaluate their design through by using this application. The present application can significantly shorten product design and manufacturing time.

Recent Advances in Evolving Computing Paradigms: Cloud, Edge, and Fog Technologies.

Cloud computing has become integral lately due to the ever-expanding Internet-of-things (IoT) network. It still is and continues to be the best practice for implementing complex computational applications, emphasizing the massive processing of data. However, the cloud falls short due to the critical constraints of novel IoT applications generating vast data, which entails a swift response time with improved privacy. The newest drift is moving computational and storage resources to the edge of the network, involving a decentralized distributed architecture. The data processing and analytics perform at proximity to end-users, and overcome the bottleneck of cloud computing. The trend of deploying machine learning (ML) at the network edge to enhance computing applications and services has gained momentum lately, specifically to reduce latency and energy consumed while optimizing the security and management of resources. There is a need for rigorous research efforts oriented towards developing and implementing machine learning algorithms that deliver the best results in terms of speed, accuracy, storage, and security, with low power consumption. This extensive survey presented on the prominent computing paradigms in practice highlights the latest innovations resulting from the fusion between ML and the evolving computing paradigms and discusses the underlying open research challenges and future prospects.

Improved Diver Communication System by Combining Optical and Electromagnetic Trackers.

The increasing need for observation in seawater or ocean monitoring systems has ignited a considerable amount of interest and the necessity for enabling advancements in technology for underwater wireless tracking and underwater sensor networks for wireless communication. This type of communication can also play an important role in investigating ecological changes in the sea or ocean-like climate change, monitoring of biogeochemical, biological, and evolutionary changes. This can help in controlling and maintaining the production facilities of outer underwater grid blasting by deploying unmanned underwater vehicles (UUVs). Underwater tracking-based wireless networks can also help in maintaining communication between ships and divers, submarines, and between multiple divers. At present, the underwater acoustic communication system is unable to provide the data rate required to monitor and investigate the aquatic environment for various industrial applications like oil facilities or underwater grit blasting. To meet this challenge, an optical and magnetic tracking-based wireless communication system has been proposed as an effective alternative. Either optical or magnetic tracking-based wireless communication can be opted for according to the requirement of the potential application in sea or ocean. However, the hybrid version of optical and wireless tracking-based wireless communication can also be deployed to reduce the latency and improve the data rate for effective communication. It is concluded from the discussion that high data rate optical, magnetic or hybrid mode of wireless communication can be a feasible solution in applications like UUV-to-UUV and networks of aquatic sensors. The range of the proposed wireless communication can be extended using the concept of multihop.

Thermal-Feature System Identification for a Machine Tool Spindle.

The internal temperature is an important index for the prevention and maintenance of a spindle. However, the temperature inside the spindle is undetectable directly because there is no space to embed a temperature sensor, and drilling holes will reduce its mechanical stiffness. Therefore, it is worthwhile understanding the thermal-feature of a spindle. This article presents a methodology to identify the thermal-feature model of an externally driven spindle. The methodology contains self-made hardware of the temperature sensing and wireless transmission module (TSWTM) and software for the system identification (SID); the TSWTM acquires the temperature training data, while the SID identifies the parameters of the thermal-feature model of the spindle. Then the resulting thermal-feature model is written into the firmware of the TSWTM to give it the capability of accurately calculating the internal temperature of the spindle from its surface temperature during the operation, or predicting its temperature at various speeds. The thermal-feature of the externally driven spindle is modeled by a linearly time-invariant state-space model whose parameters are identified by the SID, which integrates the command "n4sid" provided by the System ID Toolbox of MATLAB and the k-fold cross-validation that is common in machine learning. The present SID can effectively strike a balance between the bias and variance of the model, such that both under-fitting and over-fitting can be avoided. The resulting thermal-feature model can not only predict the temperature of the spindle rotating at various speeds but can also calculate the internal temperature of the spindle from its surface temperature. Its validation accuracy is higher than 98.5%. This article illustrates the feasibility of accurately calculating the internal temperature (undetectable directly) of the spindle from its surface temperature (detectable directly).

Vibration of a Rotating Micro-Ring under Electrical Field Based on Inextensible Approximation.

The problem of vibrations of rotating rings has been of interest for its wide applications in engineering, such as the vibratory ring gyroscopes. For the vibratory ring gyroscopes, the vibration of a micro ring is usually actuated and sensed by means of electrostatics. The analytical models of electrostatic microstructures are complicated due to their non-linear electromechanical coupling behavior. Therefore, this paper presents for the first time the free vibration of a rotating ring under uniform electrical field and the results will be helpful for extending our knowledge on the problem of vibrations of rotating rings, helping the design of vibratory ring gyroscopes, and inspiring the feasibilities of other engineering applications. An analytical model, based on thin-ring theory, is derived by means of energy method for a rotating ring under uniformly distributed electrical field. After that, the closed form solutions of the natural frequencies and modes are obtained by means of modal expansion method. Some valuable conclusions are made according to the results of the present analytical model. The electrical field causes not only an electrostatic force but also an equivalently negative electrical-stiffness. The equivalent negative electrical-stiffness will reduce either the natural frequencies or critical speeds of the rotating ring. It is known that the ring will buckle when its rotational speed equals its natural frequencies. The introduction of electrical field will further reduce the buckling speeds to a value less than the natural frequencies. The rotation effect will induce the so-called traveling modes, each one travels either in the same direction as the rotating ring or in the opposite direction with respect to stationary coordinate system. The electrical field will reduce the traveling velocities of the traveling modes.

Parameter Estimation of the Thermal Network Model of a Machine Tool Spindle by Self-made Bluetooth Temperature Sensor Module.

Thermal characteristic analysis is essential for machine tool spindles because sudden failures may occur due to unexpected thermal issue. This article presents a lumped-parameter Thermal Network Model (TNM) and its parameter estimation scheme, including hardware and software, in order to characterize both the steady-state and transient thermal behavior of machine tool spindles. For the hardware, the authors develop a Bluetooth Temperature Sensor Module (BTSM) which accompanying with three types of temperature-sensing probes (magnetic, screw, and probe). Its specification, through experimental test, achieves to the precision ±(0.1 + 0.0029|t|) °C, resolution 0.00489 °C, power consumption 7 mW, and size Ø40 mm × 27 mm. For the software, the heat transfer characteristics of the machine tool spindle correlative to rotating speed are derived based on the theory of heat transfer and empirical formula. The predictive TNM of spindles was developed by grey-box estimation and experimental results. Even under such complicated operating conditions as various speeds and different initial conditions, the experiments validate that the present modeling methodology provides a robust and reliable tool for the temperature prediction with normalized mean square error of 99.5% agreement, and the present approach is transferable to the other spindles with a similar structure. For realizing the edge computing in smart manufacturing, a reduced-order TNM is constructed by Model Order Reduction (MOR) technique and implemented into the real-time embedded system.

The stiffness variation of a micro-ring driven by a traveling piecewise-electrode.

In the practice of electrostatically actuated micro devices; the electrostatic force is implemented by sequentially actuated piecewise-electrodes which result in a traveling distributed electrostatic force. However; such force was modeled as a traveling concentrated electrostatic force in literatures. This article; for the first time; presents an analytical study on the stiffness variation of microstructures driven by a traveling piecewise electrode. The analytical model is based on the theory of shallow shell and uniform electrical field. The traveling electrode not only applies electrostatic force on the circular-ring but also alters its dynamical characteristics via the negative electrostatic stiffness. It is known that; when a structure is subjected to a traveling constant force; its natural mode will be resonated as the traveling speed approaches certain critical speeds; and each natural mode refers to exactly one critical speed. However; for the case of a traveling electrostatic force; the number of critical speeds is more than that of the natural modes. This is due to the fact that the traveling electrostatic force makes the resonant frequencies of the forward and backward traveling waves of the circular-ring different. Furthermore; the resonance and stability can be independently controlled by the length of the traveling electrode; though the driving voltage and traveling speed of the electrostatic force alter the dynamics and stabilities of microstructures. This paper extends the fundamental insights into the electromechanical behavior of microstructures driven by electrostatic forces as well as the future development of MEMS/NEMS devices with electrostatic actuation and sensing.

Enhance the pyroelectricity of polyvinylidene fluoride by graphene-oxide doping.

The high quality properties and benefits of graphene-oxide have generated an active area of research where many investigations have shown potential applications in various technological fields. This paper proposes a methodology for enhancing the pyro-electricity of PVDF by graphene-oxide doping. The PVDF film with graphene-oxide is prepared by the sol-gel method. Firstly, PVDF and graphene-oxide powders are dispersed into dimethylformamide as solvent to form a sol solution. Secondly, the sol solution is deposited on a flexible ITO/PET substrate by spin-coating. Thirdly, the particles in the sol solution are polymerized through baking off the solvent to produce a gel in a state of a continuous network of PVDF and graphene-oxide. The final annealing process pyrolyzes the gel and form a ß-phase PVDF film with graphene-oxide doping. A complete study on the process of the graphene oxide doping of PVDF is accomplished. Some key points about the process are addressed based on experiments. The solutions to some key issues are found in this work, such as the porosity of film, the annealing temperature limitation by the use of flexible PET substrate, and the concentrations of PVDF and graphene-oxide.

Leveraging Computational Intelligence Techniques for Diagnosing Degenerative Nerve Diseases: A Comprehensive Review, Open Challenges, and Future Research Directions.

Degenerative nerve diseases such as Alzheimer's and Parkinson's diseases have always been a global issue of concern. Approximately 1/6th of the world's population suffers from these disorders, yet there are no definitive solutions to cure these diseases after the symptoms set in. The best way to treat these disorders is to detect them at an earlier stage. Many of these diseases are genetic; this enables machine learning algorithms to give inferences based on the patient's medical records and history. Machine learning algorithms such as deep neural networks are also critical for the early identification of degenerative nerve diseases. The significant applications of machine learning and deep learning in early diagnosis and establishing potential therapies for degenerative nerve diseases have motivated us to work on this review paper. Through this review, we covered various machine learning and deep learning algorithms and their application in the diagnosis of degenerative nerve diseases, such as Alzheimer's disease and Parkinson's disease. Furthermore, we also included the recent advancements in each of these models, which improved their capabilities for classifying degenerative nerve diseases. The limitations of each of these methods are also discussed. In the conclusion, we mention open research challenges and various alternative technologies, such as virtual reality and Big data analytics, which can be useful for the diagnosis of degenerative nerve diseases.

Computational Intelligence in Cancer Diagnostics: A Contemporary Review of Smart Phone Apps, Current Problems, and Future Research Potentials.

Cancer is a dangerous and sometimes life-threatening disease that can have several negative consequences for the body, is a leading cause of mortality, and is becoming increasingly difficult to detect. Each form of cancer has its own set of traits, symptoms, and therapies, and early identification and management are important for a positive prognosis. Doctors utilize a variety of approaches to detect cancer, depending on the kind and location of the tumor. Imaging tests such as X-rays, Computed Tomography scans, Magnetic Resonance Imaging scans, and Positron Emission Tomography (PET) scans, which may provide precise pictures of the body's interior structures to spot any abnormalities, are some of the tools that doctors use to diagnose cancer. This article evaluates computational-intelligence approaches and provides a means to impact future work by focusing on the relevance of machine learning and deep learning models such as K Nearest Neighbour (KNN), Support Vector Machine (SVM), Naïve Bayes, Decision Tree, Deep Neural Network, Deep Boltzmann machine, and so on. It evaluates information from 114 studies using Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). This article explores the advantages and disadvantages of each model and provides an outline of how they are used in cancer diagnosis. In conclusion, artificial intelligence shows significant potential to enhance cancer imaging and diagnosis, despite the fact that there are a number of clinical issues that need to be addressed.

Impact of Cross-Validation on Machine Learning Models for Early Detection of Intrauterine Fetal Demise.

Intrauterine fetal demise in women during pregnancy is a major contributing factor in prenatal mortality and is a major global issue in developing and underdeveloped countries. When an unborn fetus passes away in the womb during the 20th week of pregnancy or later, early detection of the fetus can help reduce the chances of intrauterine fetal demise. Machine learning models such as Decision Trees, Random Forest, SVM Classifier, KNN, Gaussian Naïve Bayes, Adaboost, Gradient Boosting, Voting Classifier, and Neural Networks are trained to determine whether the fetal health is Normal, Suspect, or Pathological. This work uses 22 features related to fetal heart rate obtained from the Cardiotocogram (CTG) clinical procedure for 2126 patients. Our paper focuses on applying various cross-validation techniques, namely, K-Fold, Hold-Out, Leave-One-Out, Leave-P-Out, Monte Carlo, Stratified K-fold, and Repeated K-fold, on the above ML algorithms to enhance them and determine the best performing algorithm. We conducted exploratory data analysis to obtain detailed inferences on the features. Gradient Boosting and Voting Classifier achieved 99% accuracy after applying cross-validation techniques. The dataset used has the dimension of 2126 × 22, and the label is multiclass classified as Normal, Suspect, and Pathological condition. Apart from incorporating cross-validation strategies on several machine learning algorithms, the research paper focuses on Blackbox evaluation, which is an Interpretable Machine Learning Technique used to understand the underlying working mechanism of each model and the means by which it picks features to train and predict values.

CMOS-MEMS test-key for extracting wafer-level mechanical properties.

This paper develops the technologies of mechanical characterization of CMOS-MEMS devices, and presents a robust algorithm for extracting mechanical properties, such as Young's modulus, and mean stress, through the external electrical circuit behavior of the micro test-key. An approximate analytical solution for the pull-in voltage of bridge-type test-key subjected to electrostatic load and initial stress is derived based on Euler's beam model and the minimum energy method. Then one can use the aforesaid closed form solution of the pull-in voltage to extract the Young's modulus and mean stress of the test structures. The test cases include the test-key fabricated by a TSMC 0.18 µm standard CMOS process, and the experimental results refer to Osterberg's work on the pull-in voltage of single crystal silicone microbridges. The extracted material properties calculated by the present algorithm are valid. Besides, this paper also analyzes the robustness of this algorithm regarding the dimension effects of test-keys. This mechanical properties extracting method is expected to be applicable to the wafer-level testing in micro-device manufacture and compatible with the wafer-level testing in IC industry since the test process is non-destructive.

The electromechanical behavior of a micro-ring driven by traveling electrostatic force.

There is no literature mentioning the electromechanical behavior of micro structures driven by traveling electrostatic forces. This article is thus the first to present the dynamics and stabilities of a micro-ring subjected to a traveling electrostatic force. The traveling electrostatic force may be induced by sequentially actuated electrodes which are arranged around the flexible micro-ring. The analysis is based on a linearized distributed model considering the electromechanical coupling effects between electrostatic force and structure. The micro-ring will resonate when the traveling speeds of the electrostatic force approach some critical speeds. The critical speeds are equal to the ratio of the natural frequencies to the wave number of the correlative natural mode of the ring. Apart from resonance, the ring may be unstable at some unstable traveling speeds. The unstable regions appear not only near the critical speeds, but also near some fractions of some critical speeds differences. Furthermore the unstable regions expand with increasing driving voltage. This article may lead to a new research branch on electrostatic-driven micro devices.

AI-Powered Blockchain Technology for Public Health: A Contemporary Review, Open Challenges, and Future Research Directions.

Blockchain technology has been growing at a substantial growth rate over the last decade. Introduced as the backbone of cryptocurrencies such as Bitcoin, it soon found its application in other fields because of its security and privacy features. Blockchain has been used in the healthcare industry for several purposes including secure data logging, transactions, and maintenance using smart contracts. Great work has been carried out to make blockchain smart, with the integration of Artificial Intelligence (AI) to combine the best features of the two technologies. This review incorporates the conceptual and functional aspects of the individual technologies and innovations in the domains of blockchain and artificial intelligence and lays down a strong foundational understanding of the domains individually and also rigorously discusses the various ways AI has been used along with blockchain to power the healthcare industry including areas of great importance such as electronic health record (EHR) management, distant-patient monitoring and telemedicine, genomics, drug research, and testing, specialized imaging and outbreak prediction. It compiles various algorithms from supervised and unsupervised machine learning problems along with deep learning algorithms such as convolutional/recurrent neural networks and numerous platforms currently being used in AI-powered blockchain systems and discusses their applications. The review also presents the challenges still faced by these systems which they inherit from the AI and blockchain algorithms used at the core of them and the scope of future work.

Performance Assessment of Certain Machine Learning Models for Predicting the Major Depressive Disorder among IT Professionals during Pandemic times.

Major depressive disorder (MDD) is the most common mental disorder in the present day as all individuals' lives, irrespective of being employed or unemployed, is going through the depression phase at least once in their lifetime. In simple terms, it is a mood disturbance that can persist for an individual for more than a few weeks to months. In MDD, in most cases, the individuals do not consult a professional, and even if being consulted, the results are not significant as the individuals find it challenging to identify whether they are depressed or not. Depression, most of the time, cooccurs with anxiety and leads to suicide in few cases, among the employees, who are about to handle the pressure at work and home and mostly unnoticing such problems. This is why this work aims to analyze the IT employees who are mostly working with targets. The artificial neural network, which is modeled loosely like the brain, has proved in recent days that it can perform better than most of the classification algorithms. This study has implemented the multilayered neural perceptron and experimented with the backpropagation technique over the data samples collected from IT professionals. This study aims to develop a model that can classify depressed individuals from those who are not depressed effectively with the data collected from them manually and through sensors. The results show that deep-MLP with backpropagation outperforms other machine learning-based models for effective classification.

Review on the modeling of electrostatic MEMS.

Electrostatic-driven microelectromechanical systems devices, in most cases, consist of couplings of such energy domains as electromechanics, optical electricity, thermoelectricity, and electromagnetism. Their nonlinear working state makes their analysis complex and complicated. This article introduces the physical model of pull-in voltage, dynamic characteristic analysis, air damping effect, reliability, numerical modeling method, and application of electrostatic-driven MEMS devices.

A flexible proximity sensor fully fabricated by inkjet printing.

A flexible proximity sensor fully fabricated by inkjet printing is proposed in this paper. The flexible proximity sensor is composed of a ZnO layer sandwiched in between a flexible aluminum sheet and a web-shaped top electrode layer. The flexible aluminum sheet serves as the bottom electrode. The material of the top electrode layer is nano silver. Both the ZnO and top electrode layers are deposited by inkjet printing. The fully inkjet printing process possesses the advantages of direct patterning and low-cost. It does not require photolithography and etching processes since the pattern is directly printed on the flexible aluminum sheet. The prototype demonstrates that the presented flexible sensor is sensitive to the human body. It may be applied to proximity sensing or thermal eradiation sensing.

Sensor-Assisted Assessment of the Tribological Behavior Patterns of AA7075 Hybrid MMC Reinforced with Multi-Wall Carbon Nanotubes and Pulverized Fuel Ash.

In recent years, the deployment of sensors and other ancillary technologies has turned out to be vital in the investigation of tribological behavioral patterns of composites. The tribological behavioral patterns of AA7075 hybrid metal matrix composites (MMCs) reinforced with multi-wall carbon nanotubes (MWCNTs), and pulverized fuel ash (PFA) were investigated in this work. The stir casting technique was used to fabricate the composites. The mechanical properties such as tensile strength and hardness were determined for the fabricated material. Besides, microstructure analysis was performed for these AA7075 hybrid MMCs reinforced with MWCNTs and pulverized fuel ash. A pin-on-disc wear testing setup was used to evaluate the wear rate, in which the EN 31 steel disc was used as the counter-face. Taguchi's design of the experiments was used to optimize the input parameters that impact the characteristics of the hybrid composites, and ANOVA (analysis of variance) was used to determine the contribution of input parameters on the wear behavior. Electrical discharge machining (EDM) was conducted on the AA7075 hybrid metal matrix composites using a copper electrode for determining the material removal rate. These investigations and the results were utilized for determining the optimized output process parameter values of the AA7075 metal matrix composite.