Smart Trends in Computing and Communications

This is the fourth conference in the SmartCom series in which the proceedings have been published as a SIST volume by Springer. He is currently a full professor in the Department of Electrical and Electronic Engineering, University of the Ryukyus.

Accurate Detection of Facial Landmark Points Using Local Intensity Information

Introduction
Materials and Methods .1 Materials

Proposed Methodology

Performance Metrics
Results

Eye Centers
Eyebrows
Lip Corners

Discussion
Conclusion

The above steps are repeated with Ey- to determine the lower edge of the eyebrow, as shown in equation 1.4. Understanding human facial anatomy, the ROI for the mouth was segmented into the lower half of the face.

Fig. 1.1 Face detection and selection of ROI

The algorithm is robust in detection despite partial occlusion by facial hair, wrinkles, and scar tissue. In-plane head rotation can be handled by an algorithm using eye coordinates to align the face.

Statistical Approach to Detect Alzheimer’s Disease and Autism

Spectrum-Related Neurological Disorder Using Machine Learning

Introduction
Literature Review
Methodology
Result
Multiple Linear Regression
Polynomial Regression
Logistic Regression The results obtained were as follows

Conclusion
Future Work

The aim of this research is to discover the most appropriate and accurate machine learning algorithm for the detection of ASD and Alzheimer's disease. It can be used to predict many other diseases related to the brain, heart, etc. that require laboratory tests.

The Development and Gap of Chinese and American Open Access Journals

From the Perspective of Network Influence

Introduction
Data and Method

Sample Selection Method
Data Acquisition Platform
Data Collection Method

Results and Discussion
Comparison of Network Influence Based on Principal Component Analysis
Conclusion
Limited

According to the overall results of the evaluation of the network impact of open access journals, the top three influential journals are all from the USA. Therefore, the network influence of China's open access periodicals is obviously worse than that of America in terms of the comprehensive situation of network influence.

Table 3.1 Common factor variance of network influence index

Appendix

This article was supported by the National Social Science Foundation of China (Grant No. 18ZDA325 and No. 16BTQ055).

The Use of Mobile Apps to Facilitate Customers’ Choice-Making When

Introduction
Method

Participants
Apparatus and Setting
Attributes
Procedure
Analysis

Findings
Discussion

Practical Implications
Limitations and Future Research

According to a study by Dacko [9], the mobile app creates value for customers through efficiency or better shopping value. Results show that compared to standard information, digital information provided by the mobile app was the most preferred.

Table 4.1 The study was designed with four attributes given by the mobile app with two levels each

Implementation of Best Hybrid Adaptive and Intelligent MIMO Detector

Introduction
Proposed B-HAI MIMO Detector
System Model
Architecture of HAI Detector
Computational Complexity Reduction
Performance Evaluation
Conclusions

In the proposed algorithm, firstLbits were detected adaptively using SNR values of the incoming data. The performance of the proposed B-HAI MIMO detector has been compared with the hybrid detector such as MMSE and zero-forcing hard detectors proposed in [2].

Fig. 5.1 Block diagram of B-HAI detector

Automated Detection of Retinal Hemorrhage Based on Supervised

Introduction
Methodology

Feature Extraction from Splats
Preliminary Feature Selection and Classification

Results and Discussions

Classification of Splats Using kNN and ANN Classifiers
Implementation in Hardware

Conclusion

In our work, one of the classifier decisions is based on Neural Network (NN) as described in [2]. All these values are added together and the maximum of the gradient value with its scale of interest (SOI) is taken to perform watershed segmentation. It is the peculiarity of the wrapper approach that it assesses different combinations of feature subsets adapted to a certain classification algorithm with higher computation time [ 26 ]. The combinations are evaluated using a kNN classifier.

The classifier assigns the class of a particular layer based on the Euclidean distance of features in an optimized feature space. Niemeijer, M., Abramoff, M.D., van Ginneken, B.: Segmentation of the optic disc, macula and vascular arch in fundus photographs.

Data Encryption as Security Measure in IoT-Enabled Healthcare

Introduction
IoT Data in Healthcare
IoT Data Encryption and Compression .1 Basic Philosophy

Basic Details DRT and IDRT
Inverse Discrete Rajan Transform
Sparsing of Data by Retaining CPI Alone

Summary

Despite there being awareness and efforts by people, professionals and governments to allocate funds to healthcare systems, one cannot rule out the possibility of IoT-enabled healthcare being hit by cyber-attacks due to intellectual rogues and psychos. For example, the data collected by an electrocardiogram device would be of the form shown in FIG. 7.3. The first value of the final DRT spectrum is known as the 'Cumulative Point Index (CPI)'.

Now IDRT is applied to Xs1(k) and the reconstructed form of the original signal is obtained as. The application of IoT is growing day by day in every part of healthcare.

Fig. 7.1 Global expenditure during a year and improvement in life expectancy. Courtesy https://

Agents-Based Service Restoration in Electrical Secondary Distribution

Introduction
Study Approach
Current Status of the Tanzanian Secondary Distribution Network
Proposed Distributed Control Model for Service Restoration

Objective Functions
Recommendations for the Improvement of the SDN
Design for the Equipment Specifications to Be Installed to Support Service Restoration in the Pilot Site

Conclusion

Existing studies in low voltage focused on using the centralized approach during network reconfiguration and service restoration. Among others, three objective functions are found to be key for service restoration in the secondary distribution network due to the overall physical nature of the network and the overall needs for service reliability. In this paper, we have proposed the design of distributed algorithm for service restoration in secondary distribution networks.

The realization of the service restoration process requires the installation of smart meters and smart switches across the secondary distribution network. The future work will be the implementation of the designed distributed service restoration algorithm considering the integration of renewable energy sources.

Fig. 8.1 Existing secondary distribution network topology

Packet-Level and Physical-Level Cross-Technology Communications

A Brief Introduction

Introduction
Packet-Level CTC

FreeBee
ZigFi

Physical-Level CTC
Conclusion

For example, in [4], the Wi-Fi sender will emulate ZigBee signals by carefully choosing the payload bit pattern. FreeBee performs Wi-Fi to ZigBee communication using beacon position to encode CTC bits. TDW is the Wi-Fi packet transmission time and TIW is the transmission interval between two adjacent Wi-Fi packets.

99 emulates the signal as a normal ZigBee signal and ignores the Wi-Fi header and trailer. First, get the emulated ZigBee signal constellation points by reversing the Wi-Fi transmission procedure.

Smart Traffic Navigation System (STNS) to Reduce Travel Time by Integrating

Introduction
Related Work
Design of the Proposed Model
Experimental Setup
Implementation

Vehicle
Traffic Signal

Experiments and Results

Test Cases
Test System Specifications
Results

Conclusion

The proposed model (STNS) aims to reduce the average travel time for the entire grid network. The evaluation metric used to analyze the performance of STNS is the average travel time where. Average travel time test cases: A comparison of the average travel times for all test cases between the existing timed signal model and the proposed model can be seen in Table 10.3.

It can be concluded that the average travel time of vehicles is less when STNS is used compared to the time signal model. In the proposed STNS model, the traffic signals and navigation systems are part of the same feedback loop, thus communicating with each other to reduce the average travel time of vehicles.

Selective Sensor Control via Cauchy Schwarz Divergence

Introduction
Background and Problem Statement .1 Sensor Control Framework

Labeled MB Filter
Notation
Labeled-MB RFS
Labeled-MB Filter

Selective Sensor Control via CS-Divergence

CS-Divergence

Numerical Results
Discussion and Conclusion

The sensor command u∗ is chosen so that the expected objective function is maximized in all future measurements. In this paper, we use the analytical form of CS-divergence for time-updated and measurement-updated Labeled-MBposteriors to calculate the objective function. We approximate the posterior Labeled-MBdensity calculated in Labeled-MBfilter [16] to the time-updated and measurement-updated by their first moments.

The MEs of time-updated and pseudo-measurement-updated labeled MB densities are then given by: . The SC solution is expected to move the sensor closer to the targets of interest.

Table 11.1 Variable description in Eq. (11.1)

Step-Factor Resampling Technique for Imbalanced Sequence Data Classification

Introduction
Related Work
Methodology

Smoothing Technique
Resampling Technique

Experimental Results

Model Tuning with Smoothing Factor
Model Tuning with Resampling Technique
Performance Evaluation of Models
Best Performing Model

Conclusions

In this situation, class imbalance can cause a prediction bias in favor of the majority class [10]. Studies have shown that using a smoothing technique as a preprocessing step leads to a significant improvement in the performance of the model [6, 8]. The shifting process of the indices based on the MID step factor is illustrated in Fig.12.2.

The HMSM is enhanced with a smoothing factor: εas a model tuning parameter to scale model performance. The proposed resampling approach applies to the HMSM model, along with the smoothing factor, to improve the model performance.

Fig. 12.1 Index distance of minor class instances

Rice Disease Detection Based on Image Processing Technique

Introduction
Literature Review
Methodology .1 Working Diagram

Dataset Collection
Neural Network
Building Model
Compiling the Model
Training and Testing the Model

Result and Outcome .1 Learning Rate

Confusion Matrix
Classification

Conclusions

Phadikar and Sil [6] described a method for rice disease using software prototype system based on pattern recognition technique. For prediction based on the highest likelihood, SoftMax is added as activation in the model. New weight=existing weight-learning rate×gradient (13.4) Figure 13.4 displays the accuracy function graph of the model.

For real-time detection of rice disease, we have proposed convolutional neural network (CNN) based on classifier model named as sequential. Processed data is valuable in forecasting, while raw data affects model efficiency.

Fig. 13.2 Convolutional neural network (CNN)

Electromagnetic Radiation from Cell Phones Used in Dhaka City

Quamruzzaman, Munima Haque, Shahina Haque, and Utpal Chandra Das

Introduction

Motivation
Novelty and Contribution

Materials and Methods

Experimental Setup

Results

Epidemiological Data from Cell Phones
Radiated Power Measurement from Cell Phones

Discussions
Conclusion

Limitations

The health effects of EMFs from mobile phone use in college students have also been studied [15]. Since the invention of modern mobile phones, there has been an incredible increase in the use of mobile phones worldwide. Readings were recorded from different mobile phones of students, general public of different ages.

Age of cell phone users is listed, along with years of cell phone use, hours of use per day, smoking or non-smoking, symptoms of cell phone use, and any other observations. Haque, M., Quamruzzaman, M.: Health effects of EMF emitted by mobile phones used by students of Southeastern University in Bangladesh.

Expressing Opinions Application: A Case Study at Rajamangala University

Introduction
Tools and Theories .1 Flutter

Spring Framework
Java Script Object Notation (JSON)

Procedure

The Operation of the System
System Design and Development

Login Page

Adding Topics and Comments
Tools for System Development

Experimental Results
Conclusion

When users use the system via mobile applications, the application sends information over the Internet to the API to store topics, comments and users in the database, and then the API will send results to the application on a mobile phone as shown in Fig. .15.5. Then the system returns to the login page again to use the registered code to login as in Fig.15.6. If the login is correct, the system will enter the category page, as shown in Fig.15.7.

When entering the topic page shown in Fig.15.7, users can add topics by clicking the “+” button and then go to the topic page to fill in additional topics and details, as shown in Fig.15.9. When users want to post a comment, they press the “+” sign, which will display the add comment page as in Fig.15.11 and fill in the text.

Toward Clarifying Human Information Processing by Analyzing Big Data

Comparing Response Time

Introduction
Problems
Design
Results
Discussion
Conclusion

Conducting the tests and measuring each response time presented each sentence of the 120 items with an audio voice or letters (listing or silent reading); (vs. balanced order) (Figure 16.2). After testing the hypothesis, I will create scatter graphs comparing the response time between audio voice and letters. Both types of correlation coefficient between the response time presented by the auditory voice and the duration presented by it were strong (0.40

In contrast, the scatterplot describing the correlations of response time between phonemes and letters for VT shows greater dispersion than AT. In addition, the mean response time to phoneme for AT was similar to that of letters.

Figure 16.1 shows the example of an adverbial particle は/wa/ in a sentence.

Mango Species Detection from Raw Leaves Using Image Processing System

Introduction
Literature Review
Proposed Methodology

Primary Steps for Mango Species Detection
Image Acquisition
Image Pre-processing and Classification
Architecture Model

Result

Statistical Analysis
Accuracy Graph
Confusion Matrix
Layer Visualization
Species Detection

Conclusion

Basically, the output of any research is a report on the results of the proposed method used in the research work. In this research, the representation of the graphical result of the proposed model using CNN and image processing techniques is shown below in Figure 17.4 and briefly explained. In this research, the two-dimensional matrix summarizes the classification performance reciprocal to the deep learning classifier of the collected mango image test dataset.

The impressive classification result of CNN and the image processing techniques of this research are shown in Fig.17.6 by dividing an image into multiple parts through matrices. In this research, we have used the latest image processing technology and CNN algorithm to detect mango species from mango leaf image alone.

Fig. 17.1 Primary steps for mango species detection

Frequency Reconfigurable Planner Antennas for Wireless Applications

A Review

Introduction

Reconfigurable Antennas