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.

A Novel Method of Identifying Paddy Seed Varieties.

This paper presents a novel method for identifying three varieties (Taikong 9, Tainan 11, and Taikong 14) of foundation paddy seeds. Taikong 9, Tainan 11, and Taikong 14 paddy seeds are indistinguishable by inspectors during seed purity inspections. The proposed method uses image segmentation and a key point identification algorithm that can segment paddy seed images and extract seed features. A back propagation neural network was used to establish a classifier based on seven features that could classify the three paddy seed varieties. The classification accuracies of the resultant classifier for varieties Taikong 9, Tainan 11, and Taikong 14 were 92.68%, 97.35% and 96.57%, respectively. The experimental results indicated that the three paddy seeds can be differentiated efficiently using the developed system.

A Novel Auto-Sorting System for Chinese Cabbage Seeds.

This paper presents a novel machine vision-based auto-sorting system for Chinese cabbage seeds. The system comprises an inlet-outlet mechanism, machine vision hardware and software, and control system for sorting seed quality. The proposed method can estimate the shape, color, and textural features of seeds that are provided as input neurons of neural networks in order to classify seeds as "good" and "not good" (NG). The results show the accuracies of classification to be 91.53% and 88.95% for good and NG seeds, respectively. The experimental results indicate that Chinese cabbage seeds can be sorted efficiently using the developed system.

Encapsulation of Au/Fe3O4 nanoparticles into a polymer nanoarchitecture with combined near infrared-triggered chemo-photothermal therapy based on intracellular secondary protein understanding.

The combination of the functions of near infrared-triggered molecule release and chemo-photothermal therapy improved the therapeutic effect, but clarification of the cancer damage pathway in terms of protein molecule levels has yet to be well studied. In this study, we developed a polymer encapsulation synthesis of Au/Fe3O4@polymer nanoparticles as a Swiss army knife to integrate near infrared absorption, magnetism, and doxorubicin (DOX) loading ability into a single package. By exposing to near infrared absorption, the Au/Fe3O4@polymer nanoparticles possessed photothermal therapy, exhibiting anti-tumor growth suppression of HT-29 tumor-bearing nude mice with less body weight loss. To deeply understand the interactions between the drug-loaded nanocarriers and the protein structures of the treated cells, delivering therapeutic DOX agent combined with photothermal therapy with Au/Fe3O4@polymer nanostructures to cancer cells was investigated. Synchrotron-based FTIR imaging and confocal imaging showed direct observation of the efficient photo-chemotherapy impacting MCF7, MCF7/ADR, and HT-29 cells after the near infrared radiation-triggered DOX release. Our demonstration outlines how the cell destruction in the molecular mechanism was initiated by chemo-photothermal combination therapy after the translocation of DOX from the cytosol to the nuclei, leading to altered intracellular secondary proteins. For preclinical application of potential diagnosis to cancer cells, Au/Fe3O4@polymer nanoparticles performed integrated computed tomography/magnetic resonance imaging contrast enhancement and near infrared-triggered chemo-photothermal therapy.

A Novel Machine Vision System for the Inspection of Micro-Spray Nozzle.

In this study, we present an application of neural network and image processing techniques for detecting the defects of an internal micro-spray nozzle. The defect regions were segmented by Canny edge detection, a randomized algorithm for detecting circles and a circle inspection (CI) algorithm. The gray level co-occurrence matrix (GLCM) was further used to evaluate the texture features of the segmented region. These texture features (contrast, entropy, energy), color features (mean and variance of gray level) and geometric features (distance variance, mean diameter and diameter ratio) were used in the classification procedures. A back-propagation neural network classifier was employed to detect the defects of micro-spray nozzles. The methodology presented herein effectively works for detecting micro-spray nozzle defects to an accuracy of 90.71%.

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.

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.

A Fast Inspection of Tool Electrode and Drilling Depth in EDM Drilling by Detection Line Algorithm.

The purpose of this study was to develop a novel measurement method using a machine vision system. Besides using image processing techniques, the proposed system employs a detection line algorithm that detects the tool electrode length and drilling depth of a workpiece accurately and effectively. Different boundaries of areas on the tool electrode are defined: a baseline between base and normal areas, a ND-line between normal and drilling areas (accumulating carbon area), and a DD-line between drilling area and dielectric fluid droplet on the electrode tip. Accordingly, image processing techniques are employed to extract a tool electrode image, and the centroid, eigenvector, and principle axis of the tool electrode are determined. The developed detection line algorithm (DLA) is then used to detect the baseline, ND-line, and DD-line along the direction of the principle axis. Finally, the tool electrode length and drilling depth of the workpiece are estimated via detected baseline, ND-line, and DD-line. Experimental results show good accuracy and efficiency in estimation of the tool electrode length and drilling depth under different conditions. Hence, this research may provide a reference for industrial application in EDM drilling measurement.

Fabrication of a ZnO Pyroelectric Sensor.

This paper proposes a two-step radio frequency (RF) sputtering process to forma ZnO film for pyroelectric sensors. It is shown that the two-step sputtering process with alower power step followed by a higher power step can significantly improve the voltageresponsivity of the ZnO pyroelectric sensor. The improvement is attributed mainly to theformation of ZnO film with a strongly preferred orientation towards the c-axis.Furthermore, a nickel film deposited onto the uncovered parts of the ZnO film caneffectively improve the voltage responsivity at higher modulating frequencies since thenickel film can enhance the incident energy absorption of the ZnO layer.

An Auto-Recognizing System for Dice Games Using a Modified Unsupervised Grey Clustering Algorithm.

In this paper, a novel identification method based on a machine vision system is proposed to recognize the score of dice. The system employs image processing techniques, and the modified unsupervised grey clustering algorithm (MUGCA) to estimate the location of each die and identify the spot number accurately and effectively. The proposed algorithms are substituted for manual recognition. From the experimental results, it is found that this system is excellent due to its good capabilities which include flexibility, high speed, and high accuracy.

Effectiveness of case management for community elderly with hypertension, diabetes mellitus, and hypercholesterolemia in Taiwan: a record review.

OBJECTIVE: To identify the effectiveness of case management for community elderly with hypertension (HT), diabetes mellitus, and hypercholesterolemia (HC) (the so-called three highs). DESIGN AND SETTING: Secondary data of the first and 3-month-after visiting records were extracted from 33 Public Health Centers in Taiwan. PARTICIPANTS: Seven hundred and sixty-six clients were selected who were at least 65 years old and had been diagnosed twice on the Case Management Record with at least one of the three highs. RESULTS: This sample had a mean age of 72.6 years, 59.7% were female. Approximately 74% of the clients had HT, 55% had diabetes, and 15% had HC. Each elderly revealed 1.4 highs of the three highs. The elderly with HT, and diabetes, their blood pressures (BP) and blood sugars significantly decreased after being managed by public health nurses. Males and the elderly living in urban areas had more decrease in systolic BP. Females had more decrease in fasting blood sugar. CONCLUSIONS: The study found that the case management of the three highs presented effectiveness on reducing the values of the three highs of the elderly in community.