A holistic evaluation of smart city performance in the context of China0 تومان
Development of smart city has been increasingly accepted as a new technology-based solution to itigate urban diseases. The Chinese government has been devoting good efforts to the promotion of smart city through introducing a series of policies. However, policies may have limited effectiveness in application if they do not respond to the practice. There is little study examining what results have been achieved in practice by applying policy measures. This study presents a holistic evaluation of smart city performance in the context of China. The evaluation indicators in this study are selected by applying a hybrid research methodology including literature review and semi structured interviews. Indicator data are collected from 44 sample smart cities. The evaluation was conducted by applying Entropy method and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) technique collectively. This study highlights that the overall smart city performance in China is at a relatively low level. There is also a significant unbalance in performance between five smart city dimensions including smart infrastructure, governance, people, economy and environment. The smart performance between cities varies significantly since cities implement smart city programs in different ways. These differences impede experience sharing between cities. Actions have been recommended in this study for promoting further development of smart city in the context of China, such as increasing the investment on smart infrastructure, providing training programs, and establishing evaluation mechanism.
A Neural Network Based Expert System for the Diagnosis of Diabetes Mellitus0 تومان
Diabetes is a disease in which the blood glucose, or blood sugar levels in the body are too high. The damage caused by diabetes can be very severe and even more pronounced in pregnant women due to the tendency of transmitting the hereditary disease to the next generation. Expert systems are now used in medical diagnosis of diseases in patients so as to detect the ailment and help in providing a solution to it. This research developed and trained a neural network model for the diagnosis of diabetes mellitus in pregnant women. The model is a four-layer feed forward network, trained using back-propagation and Bayesian Regulation algorithm. The input layer has 8 neurons, two hidden layers have 10 neurons each, and the output layer has one neuron which is the diagnosis result. The developed model was also incorporated into a web-based application to facilitate its use. Validation by regression shows that the trained network is over 92% accurate.
A review of information system integration in mergers and acquisitions0 تومان
For three decades, research has investigated the role of information systems integration (ISI) in mergers and acquisitions (M&As). This research has improved our understanding of the M&A IS challenges and their solutions. However, consolidation and integration across the research is limited. To redress this omission, we review 70 articles published between 1989 and 2016. To do this, we adopt and extend the methodology developed by Lacity and her colleagues to review the empirical evidence in a fragmented IT literature. We code 53 dependent variables and 195 independent variables to identify the robust relationships among them and to model how ISI decisions, including the choice of IS integration methods, partially mediate the effects of the independent variables on ISI outcomes. Examining the relationships in this model, we identify five quasi-independent thematic domains on which we draw to develop an agenda for future research. Our contribution is the aggregation, organization and structuring of the empirical findings in the M&A ISI literature as a basis on which to develop a cumulative knowledge process.
A Survey of the Sensing, Communication, and Security Planes in Smart City System Design0 تومان
The Internet of Things (IoT) era is evolving into sensor initiated, actuation-driven, and machine intelligence-based decision making platform for smart cities. A smart city system aims at seamless and secure interconnection of sensors, actuators, and data processing resources to ensure digital, efficient, and reliable services. In this article, we present a brief planar overview of a smart city system architecture by introducing the application, sensing, communication, data, and security/privacy planes. Tailoring existing communication protocols and infrastructures to bridge massively deployed sensors and data processing/storage resources introduces unique communication challenges for smart cities. Furthermore, co-existence, integration, and control of dedicated and non-dedicated sensors is a grand challenge while IoT sensors continuously push sensory data through the communication medium towards data processing and analysis planes. While pervasiveness and ubiquity of smart city services are ensured by the interaction of communication and sensing technologies, their robustness and resilience calls for customized security and privacy solutions. With these in mind, we focus on sensing/actuation, communication, and security planes of a smart city system and present a comprehensive survey of the challenges and state-of-the-art solutions in each plane. Furthermore, we provide insights for open issues and opportunities in these planes.
Abstracting Massive Data for Lightweight Intrusion Detection in Computer Networks0 تومان
Anomaly intrusion detection in big data environments calls for lightweight models that are able to achieve real-time performance during detection. Abstracting audit data provides a solution to improve the efficiency of data processing in intrusion detection. Data abstraction refers to abstract or extract the most relevant information from the massive dataset. In this work, we propose three strategies of data abstraction, namely, exemplar extraction, attribute selection and attribute abstraction. We first propose an effective method called exemplar extraction to extract representative subsets from the original massive data prior to building the detection models. Two clustering algorithms, Affinity Propagation (AP) and traditional k-means, are employed to find the exemplars from the audit data. K-Nearest Neighbor (k-NN), Principal Component Analysis (PCA) and one-class Support Vector Machine (SVM) are used for the detection. We then employ another two strategies, attribute selection and attribute extraction, to abstract audit data for anomaly intrusion detection. Two http streams collected from
a real computing environment as well as the KDD’99 benchmark data set are used to validate these three strategies of data abstraction. The comprehensive experimental results show that while all the three strategies improve the detection efficiency, the AP-based exemplar extraction achieves the best performance of data abstraction.
Applying sentiment analysis in social web for smart decision support marketing0 تومان
Because of the rapid development of communication and service in Taiwan, competition among telecommunication companies has become ever fercer. Diﬀerences in marketing strategy usually become the key factor in keeping existing customers while attracting new ones. Although electronic word-of-mouth (e-WOM) is one of the most important pieces of information to a consumer making a purchase decision, very few articles on opinion mining have discussed and compared the relationship between multifaceted word-of-mouth (WOM) and marketing strategy. In this paper, we use our Chinese opinion-mining system (Wu et al. in J Supercomput 73:2987–3001, 2017) not only to retrieve articles related to 4G and conduct reputation analysis but also to discuss the relation between WOM and marketing strategy. The results show that (1) e-WOM can immediately and directly reﬂect the results of marketing strategy, and (2) although users are primarily concerned with aspects of price, online speed, and signal quality, for most Taiwanese customers, price is the key in choosing a telecommunication company. Moreover, although this paper used 4G-related articles from June 2014 to June 2015 for analysis, the results are consistent with the Taiwanese telecommunication companies’ current marketing strategy of attracting customers through low pricing.
Artificial neural networks and intelligent finite elements in non-linear structural mechanics0 تومان
In recent years, artificial neural networks were included in the prediction of deformations of structural elements, such as pipes or tensile specimens. Following this method, classical mechanical calculations were replaced by a set of matrix multiplications by means of artificial intelligence. This was also continued in finite element approaches, wherein constitutive equations were substituted by an artificial neural network (ANN). However, little is known about predicting complex non-linear structural deformations with artificial intelligence. The aim of the present study is to make ANN accessible to complicated structural deformations. Here, shock-wave loaded plates are chosen, which lead to a boundary value problem taking geometrical and physical non-linearities into account. A wide range of strain-rates and highly dynamic deformations are covered in this type of deformation. One ANN is proposed for the entire structural model and another ANN is developed for replacing viscoplastic constitutive equations, integrated into a finite element code, leading to an intelligent finite element. All calculated results are verified by experiments with a shock tube and short-time measurement techniques.
Automatic washing system of LED street lighting via Internet of Things0 تومان
The illumination of the streets and public area in metropolitan cities is a vital service, which is not only related to the type of the light but also the dirtiness of the surface of the light. In this paper, both subjects are considered to increase the productivity of the light. To achieve this goal, a novel Automatic washing system (AWS) of LED street/public light surface was designed, manufactured and installed practically. The proposed mechanism consists of two main parts comprising mechanical and electrical systems. AWS operates based on internet interconnection technique known as Internet of Things (IoT) with a high productivity. The system has the potential to be designed and employed by four types of control system; (i) using a timer switch, (ii) using a GSM 900, (iii) using a push button manually by an operator, and (iv) using a remote-control module such as GSM, SIM 808 or GPRS/GPS/SMS through the Ethernet network. A practical system has been manufactured and installed in Kermanshah city in Iran, due to its low cost, low maintenance, upgradability, and feasibility of installing different recognition sensors such as rain and dust sensors.
Automation of boiler process at thermal power plant using sensors and IoT0 تومان
Internet of Things is the new era for industrial automation to produced quality products, by implementing automation in accordance with the existing man power at low cost. Despite the use of Distributed Control System or Programmable Logic Controller (DCS/PLC) in Thermal Power Plants, controlling parameters like temperature, humidity and pressure is a critical and essential process, and require a well-organized and trained labor for the completion of tasks without any mishappening. This paper focuses on developing a smart simulation and utomated system to makes use of modeling and pattern discovery along with the data mining techniques to collect data from the thermal power plant. Each boiler is attached with each other through a sensor which in turn is connected to an IOT driven application at remote location. The sensors work on a specific pattern depending on the possible situations that may arise while the plant is in process. The same pattern is recognized in a simulated environment with the help of modeling technique. The message passing service in the whole system makes the working of the automation project prone to accidents since modification can be made according to the environment conditions. This paper also tends to propose an idea for an IOT device that provides a virtual knob .facility to adjust the parameters of the furnace from the remote area
CAMP: cluster aided multi-path routing protocol for wireless sensor networks0 تومان
In this article, we propose a novel routing algorithm for wireless sensor network, which achieves uniform energy depletion across all the nodes and thus leading to prolonged network lifetime. The proposed algorithm, divides the Region of Interest into virtual zones, each having some designated cluster head nodes. In the entire process, a node can either be a part of a cluster or it may remain as an independent entity. A non-cluster member transmits its data to next hop node using IRPIntelligent Routing Process (based on the trade-off between the residual energy of itself as well as its neighbor, and therequired energy to transmit packets to its neighbor). If on the transmission path, some cluster member is elected as a next hop, it rejects IRP and transmits the packets to cluster head, which later forwards them to sink (adopting multihop communication among cluster heads). Routing is not solely performed using clusters, rather they aid the overall routing process, hence this protocol is named as Cluster Aided Multipath Routing (CAMP). CAMP has been compared with various sensor network routing protocols, viz., LEACH, PEGASIS, DIRECT TRANSMISSION, CEED, and CBMR. It is found that the proposed algorithm outperformed them in network lifetime, energy consumption and coverage ratio.
Characterizing the Impact of Topology on IoT Stream Processing0 تومان
The Internet of Things (IoT) extends traditional cyber-physical systems by linking sensor based edge devicesto network accessible services and resources. In most current IoT deployments, sensor data is streamed from edge devices to servers for storage. Analytical pipelines are then used to translate this raw sensor data into actionable information in real-time. As additional IoT devices are deployed, the volume and rate of data received on the server side can increase dramatically. This has a possibility of offsetting the response latencies beyond acceptable limits for IoT analytical systems. In this paper, we compare the impact of alternative serverside stream processing topologies for ingesting and analyzing IoT sensor data in real-time. We use real building sensor data with our real-time IoT platform called Namatad. We have characterized and analyzed the latency and QoS impact due to the different levels of granularity of the ingestion and routing process by which we transmit data into the analytical pipelines. Our results show that as IoT systems continue to scale in density, server- side topology management for IoT data streamsis critical for latency-sensitive control and analysis applications.
Deep Artificial Neural Networks as a Tool for the Analysis of Seismic Data0 تومان
The number of seismological studies based on artificial neural networks has been increasing. However, neural networks with one hidden layer have almost reached the limit of their capabilities. In the last few years, there has been a new boom in neuroinformatics associated with the development of third- generationnetworks, deep neural networks. These networks operate with data at a higher level. Unlabeled data can be used to pretrain the network, i.e., there is no need for an expert to determine in advance the phenomenon to which these data correspond. Final training requires a small amount of labeled data. Deep networks have a higher level of abstraction and produce fewer errors. The same network can be used to solve several tasks at the same time, or it is easy to retrain it from one task to another. The paper discusses the possibility of applying deep networks in seismology. We have described what deep networks are, their advantages, how they are trained, how to adapt them to the features of seismic data, and what prospects are opening up in connection with their use.
Deep Artificial Neural Networks as a Tool for the Analysis of Seismic Data0 تومان
The number of seismological studies based on artificial neural networks has been increasing. However, neural networks with one hidden layer have almost reached the limit of their capabilities. In the last few years, there has been a new boom in neuroinformatics associated with the development of third-generation networks, deep neural networks. These networks operate with data at a higher level. Unlabeled data can be used to pretrain the network, i.e., there is no need for an expert to determine in advance the phenomenon to which these data correspond. Final training requires a small amount of labeled data. Deep networks have a higher level of abstraction and produce fewer errors. The same network can be used to solve several tasks at the same time, or it is easy to retrain it from one task to another. The paper discusses the possibility of applying deep networks in seismology. We have described what deep networks are, their advantages, how they are trained, how to adapt them to the features of seismic data, and what prospects are opening up in connection with their use.
Detecting Human Emotions Using Electroencephalography (EEG) using Dynamic Programming Approach0 تومان
The relation between human emotions and EEG signals have been actively studied during the last few decades. In this paper, we study a novel attempt to measure the human brain activity and analyze its signals using electroencephalography (EEG) in order to classify the human emotions indicated by thebrain wavelets. The study will measure the brain wavelets of female students in order to detect the emotions of happiness, sadness, and fear. This study will use specially designed sensors placed around the scalp. The measured signals transfer to a computing device. The data collected analyze by a software system, which developed by our team. We use dynamic programming to extract the maximum number of quality service that provides to the user when the device captures specific signals for each emotion.
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