scholarly journals Neural Networks for Infectious Diseases Detection: Prospects and Challenges

2021 ◽  
Author(s):  
Shumaila Javaid ◽  
Nasir Saeed
Keyword(s):  
Neural Network ◽  
Data Privacy ◽  
Critical Role ◽  
Disease Diagnosis ◽  
Future Research ◽  
Detection Accuracy ◽  
Privacy And Security ◽  
Research Directions ◽  
Medical Diagnostic ◽  
Artificial Neural Network Ann

Artificial neural network (ANN) ability to learn, correct errors, and transform a large amount of raw data into useful medical decisions for treatment and care have increased its popularity for enhanced patient safety and quality of care. Therefore, this paper reviews the critical role of ANNs in providing valuable insights for patients’ healthcare decisions and efficient disease diagnosis. We thoroughly review different types of ANNs presented in the existing literature that advanced ANNs adaptation for complex applications. Moreover, we also investigate ANN’s advances for various disease diagnoses and treatments such as viral, skin, cancer, and COVID-19. Furthermore, we propose a novel deep Convolutional Neural Network (CNN) model called ConXNet for improving the detection accuracy of COVID-19 disease. ConXNet is trained and tested using different datasets, and it achieves more than 97% detection accuracy and precision, which is significantly better than existing models. Finally, we highlight future research directions and challenges such as complexity of the algorithms, insufficient available data, privacy and security, and integration of biosensing with ANNs. These research directions require considerable attention for improving the scope of ANNs for medical diagnostic and treatment applications. <br>

scholarly journals Neural Networks for Infectious Diseases Detection: Prospects and Challenges

2021 ◽  
Author(s):  
Shumaila Javaid ◽  
Nasir Saeed
Keyword(s):  
Neural Network ◽  
Data Privacy ◽  
Critical Role ◽  
Disease Diagnosis ◽  
Future Research ◽  
Detection Accuracy ◽  
Privacy And Security ◽  
Research Directions ◽  
Medical Diagnostic ◽  
Artificial Neural Network Ann

Artificial neural network (ANN) ability to learn, correct errors, and transform a large amount of raw data into useful medical decisions for treatment and care have increased its popularity for enhanced patient safety and quality of care. Therefore, this paper reviews the critical role of ANNs in providing valuable insights for patients’ healthcare decisions and efficient disease diagnosis. We thoroughly review different types of ANNs presented in the existing literature that advanced ANNs adaptation for complex applications. Moreover, we also investigate ANN’s advances for various disease diagnoses and treatments such as viral, skin, cancer, and COVID-19. Furthermore, we propose a novel deep Convolutional Neural Network (CNN) model called ConXNet for improving the detection accuracy of COVID-19 disease. ConXNet is trained and tested using different datasets, and it achieves more than 97% detection accuracy and precision, which is significantly better than existing models. Finally, we highlight future research directions and challenges such as complexity of the algorithms, insufficient available data, privacy and security, and integration of biosensing with ANNs. These research directions require considerable attention for improving the scope of ANNs for medical diagnostic and treatment applications. <br>

scholarly journals A Survey of Supervised Learning Models for Spiking Neural Network

2021 ◽  
pp. 35-49
Author(s):  
Moses Apambila Agebure ◽  
Paula Aninyie Wumnaya ◽  
Edward Yellakuor Baagyere
Keyword(s):  
Neural Network ◽  
Supervised Learning ◽  
Second Generation ◽  
Future Research ◽  
Learning Models ◽  
Research Directions ◽  
Future Research Directions ◽  
The Difference ◽  
Artificial Neural Network Ann ◽  
Made In

There has been a significant attempt to derive supervised learning models for training Spiking Neural Networks (SNN), which is the third and most recent generation of Artificial Neural Network (ANN). Supervised SNN learning models are considered more biologically plausible and thus exploits better the computational efficiency of biological neurons and also, are less computationally expensive than second generation ANN. SNN models have also produced competitive performance in most tasks when compared to second generation ANNs. These advantages, coupled with the difficulty in adopting the well established learning models for second generation networks to train SNN due to the difference in information coding led to the recent introduction of supervised learning models for training SNN. However, lack of comprehensive source of literature detailing strides made in this area, and the challenges and prospects of SNN serves as a hindrance to further exploration and application of SNN models. A comprehensive review of supervised learning methods in SNN is presented in this paper in which some widely used SNN neural models, learning models and their basic concepts, areas of applications, limitations, prospects and future research directions are discussed. The main contribution of this paper is that it presents and discusses trends in supervised learning in SNNwith the aim of providing a reference point for those desiring further knowledge and application of SNN methods.

Social media engagement tactics in U.S. community policing: Potential privacy and security concerns

2020 ◽  
pp. 0032258X2096858
Author(s):  
Alexander E Carter ◽  
Mariea Hoy ◽  
Betsy Byrne DeSimone
Keyword(s):  
Social Media ◽  
Law Enforcement ◽  
Health Information ◽  
Community Policing ◽  
Future Research ◽  
Online Relationships ◽  
Privacy And Security ◽  
Research Directions ◽  
Personally Identifiable Information ◽  
Future Research Directions

Despite law enforcement’s best efforts to use social media as a means of community policing, some engagement tactics may lead citizens to disclose personally identifiable information (PII). We coded 200 tweets with the popular #9PMRoutine that tagged @PascoSheriff (Florida) for participant PII. We found numerous postings of adults’ and children’s PII that are problematic including pictures, health information and security-related comments about their routines or vacations. Implications for law enforcement to protect their communities are discussed as well as opportunities to continue to cultivate their online relationships in a more secure forum. We also provide future research directions.

Comparing Predictive Ability of Classifiers in Forecasting Online Buying Behaviour

2020 ◽  
pp. 1279-1296
Author(s):  
Sanjeev Prashar ◽  
S.K. Mitra
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Online Shopping ◽  
Predictive Ability ◽  
Future Research ◽  
Artificial Neural ◽  
Promotional Strategies ◽  
Artificial Neural Network Ann ◽  
Buying Behaviour ◽  
Online Buying

With Internet invading geographic boundaries and diverse demographic strata, online shopping is growing at exponential rate. Expected to grow by 45 per cent to $7.69 billion by the end of 2015, India's ecommerce market has emerged as one of the most anticipated destinations for both multinational and domestic retailers. Since their success will depend on their ability to attract shoppers to buy online, it becomes relevant for them to decipher Indian consumers' attitude and behaviour towards online shopping and to predict online buying potential in India. The effectiveness of marketing and promotional strategies and action plans also will have to be pivoted around the potential available in the market. This empirical study explores the accuracy, precision and recall of four different classifying techniques used in predicting online buying. The forecasting ability of logistic regression (LR), artificial neural network (ANN), support vector machines (SVM) and random forest (RF) in the context of willingness of shoppers' to buy online has been compared. Analysis of the data supported most of the predictions albeit with varying level of accuracy. The outcome of the study reflects the superiority of artificial neural network over the other three models in terms of the predicting power. This paper adds to the knowledge body for online retailers in reducing their vulnerability with respect to market demand and improves their preparedness to handle the market response. Managerial implications of the findings and scope for future research have been deliberated.

Narcissism as a Predictor of Facebook Users' Privacy Concern, Vigilance, and Exposure to Risk

2014 ◽  
Vol 10 (2) ◽  
pp. 78-95 ◽  
Author(s):  
Karen Smith ◽  
Francis Mendez ◽  
Garry L. White
Keyword(s):  
Security And Privacy ◽  
Future Research ◽  
Privacy Concern ◽  
Privacy And Security ◽  
Research Directions ◽  
Security Breaches ◽  
Future Research Directions ◽  
Controlling Variables ◽  
And Gender ◽  
Privacy Risks

A model is developed and tested to explain the relationships among narcissism, privacy concern, vigilance, and exposure to risk on Facebook, with age and gender as controlling variables. Two important constructs are conceptualized and measured in this research. Facebook exposure is defined as the opportunity for privacy and security breaches on Facebook. Facebook vigilance is the extent to which consumers stay focused, attentive, and alert to potential security and privacy risks on Facebook by restricting who can access and post to their Facebook accounts. Data from a survey of 286 adult Facebook users in the U.S. support the hypothesized relationships in the model. Results suggest that narcissism is related to increased Facebook exposure and lower Facebook vigilance, despite greater stated concern for privacy and security. Furthermore, females and younger users have greater risk exposure compared to males and older users. Implications of the findings and future research directions are discussed.

scholarly journals Scalable Implementation of Hippocampal Network on Digital Neuromorphic System towards Brain-Inspired Intelligence

2020 ◽  
Vol 10 (8) ◽  
pp. 2857
Author(s):  
Wei Sun ◽  
Jiang Wang ◽  
Nan Zhang ◽  
Shuangming Yang
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Critical Role ◽  
Future Research ◽  
Calculation Algorithm ◽  
Field Programmable ◽  
Innovative System ◽  
Hippocampal Network ◽  
Single Field ◽  
On Chip

In this paper, an expanded digital hippocampal spurt neural network (HSNN) is innovatively proposed to simulate the mammalian cognitive system and to perform the neuroregulatory dynamics that play a critical role in the cognitive processes of the brain, such as memory and learning. The real-time computation of a large-scale peak neural network can be realized by the scalable on-chip network and parallel topology. By exploring the latest research in the field of neurons and comparing with the results of this paper, it can be found that the implementation of the hippocampal neuron model using the coordinate rotation numerical calculation algorithm can significantly reduce the cost of hardware resources. In addition, the rational use of on-chip network technology can further improve the performance of the system, and even significantly improve the network scalability on a single field programmable gate array chip. The neuromodulation dynamics are considered in the proposed system, which can replicate more relevant biological dynamics. Based on the analysis of biological theory and the theory of hardware integration, it is shown that the innovative system proposed in this paper can reproduce the biological characteristics of the hippocampal network and may be applied to brain-inspired intelligent subjects. The study in this paper will have an unexpected effect on the future research of digital neuromorphic design of spike neural network and the dynamics of the hippocampal network.

scholarly journals A multilayer perceptron artificial neural network approach for improving the accuracy of intrusion detection systems

2020 ◽  
Vol 9 (4) ◽  
pp. 609
Author(s):  
Abdulrahman Jassam Mohammed ◽  
Muhanad Hameed Arif ◽  
Ali Adil Ali
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Intrusion Detection ◽  
Multilayer Perceptron ◽  
Detection System ◽  
Denial Of Service ◽  
Detection Accuracy ◽  
Neural Network Approach ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

<p>Massive information has been transmitted through complicated network connections around the world. Thus, providing a protected information system has fully consideration of many private and governmental institutes to prevent the attackers. The attackers block the users to access a particular network service by sending a large amount of fake traffics. Therefore, this article demonstrates two-classification models for accurate intrusion detection system (IDS). The first model develops the artificial neural network (ANN) of multilayer perceptron (MLP) with one hidden layer (MLP1) based on distributed denial of service (DDoS). The MLP1 has 38 input nodes, 11 hidden nodes, and 5 output nodes. The training of the MLP1 model is implemented with NSL-KDD dataset that has 38 features and five types of requests. The MLP1 achieves detection accuracy of 95.6%. The second model MLP2 has two hidden layers. The improved MLP2 model with the same setup achieves an accuracy of 2.2% higher than the MLP1 model. The study shows that the MLP2 model provides high classification accuracy of different request types.</p>

scholarly journals Application of Blockchain for Internet of Things: A Bibliometric Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Ruijun Duan ◽  
Li Guo
Keyword(s):  
Internet Of Things ◽  
Business Models ◽  
Citation Index ◽  
Security Requirements ◽  
Future Research ◽  
Privacy And Security ◽  
Bibliometric Method ◽  
Research Directions ◽  
Security Issues ◽  
Future Research Directions

As a disruptive emerging technology, the Internet of things (IoT) has rapidly developed, but its privacy risks and security vulnerabilities are still key challenges. The decentralized and distributed architecture of blockchain has the potential to satisfy IoT privacy and security requirements. This gives birth to the new domain of blockchain for IoT (BIoT). BIoT will cause significant transformations across several industries, paving the way for new business models. Based on the Science Citation Index Expanded (SCIE) and Social Sciences Citation Index (SSCI) databases in Web of Science (WoS) Core Collection, this study aims to explore the research trends and cooperation in the field of BIoT using the bibliometric method. The results indicate that the publications in this field have increased significantly from 2016 to 2020, with China and the USA being the most productive and influential countries. Keyword co-occurrence analysis shows that the most important research topics are as follows: security issues, core technologies, application dimensions, and transaction processes. Text mining analysis indicates that future research directions for BloT will focus more on both computing paradigms and key applications. This study will provide researchers with a greater understanding on the state of the art of BIoT and will serve as a reference for researchers engaging in this field to identify their own future research directions.

A Review of Heat Transfer in Contemporary Optical Fiber Technology

2005 ◽  
Author(s):  
Andrew D. Yablon
Keyword(s):  
Heat Transfer ◽  
Optical Fiber ◽  
Optical Fibers ◽  
Critical Role ◽  
Future Research ◽  
Research Directions ◽  
Fiber Technology ◽  
Future Research Directions ◽  
Silica Optical Fiber ◽  
Glass Science

Several recent technological breakthroughs have led to a renaissance of interest in optical fibers, which are now widely used for applications as diverse as telecommunications, medicine, and sensing. Contemporary optical fiber technology is inherently multidisciplinary, inter-relating fields as diverse as glass science, mechanical engineering, and optics. This paper reviews several aspects of silica optical fiber technology in which thermal transport plays a critical role. Future research directions are discussed.

scholarly journals Autophagy in crop plants: what's new beyond Arabidopsis ?

Open Biology ◽  
2018 ◽  
Vol 8 (12) ◽  
pp. 180162 ◽  
Author(s):  
Jie Tang ◽  
Diane C. Bassham
Keyword(s):  
Stress Responses ◽  
Critical Role ◽  
Crop Plants ◽  
Future Research ◽  
Individual Plant ◽  
Crop Species ◽  
Research Directions ◽  
Agricultural Applications ◽  
Future Research Directions ◽  
Recycling Pathway

Autophagy is a major degradation and recycling pathway in plants. It functions to maintain cellular homeostasis and is induced by environmental cues and developmental stimuli. Over the past decade, the study of autophagy has expanded from model plants to crop species. Many features of the core machinery and physiological functions of autophagy are conserved among diverse organisms. However, several novel functions and regulators of autophagy have been characterized in individual plant species. In light of its critical role in development and stress responses, a better understanding of autophagy in crop plants may eventually lead to beneficial agricultural applications. Here, we review recent progress on understanding autophagy in crops and discuss potential future research directions.

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