An evaluation of CNN and ANN in prediction weather forecasting: A review

Artificial intelligence through deep neural networks is now widely used in a variety of applications that have profoundly altered human livelihoods in a variety of ways.  People's daily lives have become much more convenient. Image recognition, smart recommendations, self-driving vehicles, voice translation, and a slew of other neural network innovations have had a lot of success in their respective fields. The authors present the ANN applied in weather forecasting. The prediction technique relies solely upon learning previous input values from intervals in order to forecast future values. And also, Convolutional Neural Networks (CNNs) are a form of deep learning technique that can help classify, recognize, and predict trends in climate change and environmental data. However, due to the inherent difficulties of such results, which are often independently identified, non-stationary, and unstable CNN algorithms should be built and tested with each dataset and system separately. On the other hand, to eradicate error and provides us with data that is virtually identical to the real value we need Artificial Neural Networks (ANN) algorithms or benefit from it. The presented CNN model's forecasting efficiency was compared to some state-of-the-art ANN algorithms. The analysis shows that weather prediction applications become more efficient when using ANN algorithms because it is really easy to put into practice

Deep Forest Based Internet of Medical Things System for Diagnosis of Heart Disease

Due to advancement in internet of medical things, the conventional health-care systems are transformed into smart health-care systems. The medical emergence services can be significantly enhanced by integration of IoMT and data analytic techniques. These technologies also examine the unexplored area of medical services that are still unseen and provide opportunity for investigation. Moreover, the concept of smart cities is not achievable without providing a smart connected healthcare scheme. Hence, the main purpose of this research is to come up with a smart healthcare system based on IoMT, Cloud and Fog computing and intelligent data analytic technique. The major objective of the proposed healthcare system is to develop a diagnostic model capable for earlier treatment of heart disease. The suggested scheme consists of distinct phases such as data acquisition, feature extraction, FogBus based edge/fog computing environment, classification, and evaluation. In data acquisition, different IoMT such as wearables and sensors devices are considered to acquire the data related to heart disease and the various features related to signal and data are extracted. Further, the deep forest technique is integrated into the proposed system for classification task and effective diagnosis capabilities of heart issues. The performance of the suggested scheme is evaluated through set of well-defined parameters. Comparison with other healthcare model was conducted for the purpose of performance evaluation. It is concluded that the proposed model has a superiority over other all other models in different aspects namely, the sensitivity measure, accuracy measure, and specificity

Adaptive Load Balancing Scheme For Data Center Networks Using Software Defined Network

A new adaptive load balancing scheme for data center networks is proposed in this paper by utilizing the characteristics of Software Defined Networks. Mininet was utilized for the purpose of emulating and evaluating the proposed design, Miniedit was utilized as a GUI tool. In order to obtain a similar environment to the data center network, Fat-Tree topology was utilized. Different scenarios and traffic distributions were applied in order to cover as much cases of the real traffic as possible. The suggested design showed superiority over the traditional scheme in term of throughput and loss rate for all the evaluated scenarios. Two scenarios were implemented; the proposed algorithm presented a loss-free performance compared to 15% to 31% loss rate in the traditional scheme for the first scenario. The proposed scheme showed up to 81% improvement in the loss rate in the second scenario. In term of throughput, the proposed scheme maintained the same level of throughput in the first scenario compared to an average of 5Mbps reduction in the throughput when using the traditional scheme. While in the second scenario, the new scheme outperformed the traditional scheme by showing an improvement of up to 16.6% in the throughput value

A Novel QoS provisioning Scheme for OBS networks

This paper presents Classified Cloning, a novel QoS provisioning mechanism for OBS networks carrying real-time applications (such as video on demand, Voice over IP, online gaming and Grid computing). It provides such applications with a minimum loss rate while minimizing end-to-end delay and jitter. ns-2 has been used as the simulation tool, with new OBS modules having been developed for performance evaluation purposes. Ingress node performance has been investigated, as well as the overall performance of the suggested scheme. The results obtained showed that new scheme has superior performance to classical cloning. In particular, QoS provisioning offers a guaranteed burst loss rate, delay and expected value of jitter, unlike existing proposals for QoS implementation in OBS which use the burst offset time to provide such differentiation. Indeed, classical schemes increase both end-to-end delay and jitter. It is shown that the burst loss rate is reduced by 50% reduced over classical cloning

Plant Disease Diagnosing Based on Deep Learning Techniques: A Survey and Research Challenges

Agriculture crops are highly significant for the sustenance of human life and act as an essential source for national income development worldwide. Plant diseases and pests are considered one of the most imperative factors influencing food production, quality, and minimize losses in production. Farmers are currently facing difficulty in identifying various plant diseases and pests, which are important to prevent plant diseases effectively in a complicated environment. The recent development of deep learning techniques has found use in the diagnosis of plant diseases and pests, providing a robust tool with highly accurate results. In this context, this paper presents a comprehensive review of the literature that aims to identify the state of the art of the use of convolutional neural networks (CNNs) in the process of diagnosing and identification of plant pest and diseases. In addition, it presents some issues that are facing the models performance, and also indicates gaps that should be addressed in the future. In this regard, we review studies with various methods that addressed plant disease detection, dataset characteristics, the crops, and pathogens. Moreover, it discusses the commonly employed five-step methodology for plant disease recognition, involving data acquisition, preprocessing, segmentation, feature extraction, and classification. It discusses various deep learning architecture-based solutions that have a faster convergence rate of plant disease recognition. From this review, it is possible to understand the innovative trends regarding the use of CNN’s algorithms in the plant diseases diagnosis and to recognize the gaps that need the attention of the research community

Investigation of the Impact of Ddos Attack on Network Efficiency of the University Of Zakho

In this paper an investigation was conducted to have an insight into the impact of the DDoS attack on the network efficiency of the University of Zakho, in particular, after deploying a fiber infrastructure and a datacenter with services such as web server, email server, and ftp server. Tests were conducted on one of the most popular DDoS attacks, that is, the flooding attack that has many types based on the start-of-the-art research in this field. For the evaluation purpose, two Internet services were chosen which are namely; file transfer service and E-learning video service. OPNET was used as a simulation tool to simulate Zakho University network and to conduct attacks because of its high reliability and reputation in this field. Results showed that DDoS attack has a big adverse impact on the legitimate users’ accessibility for both services. Where the file traffic download reduced from 6000 Bytes/s in the case of no attack into only 500 Bytes/s in the case of attack which makes an approximate of 92% loss in the network traffic. In addition, video throughput is reduced from 180KB/s in the case of no attack into less than 20KB/s in the case of attack which makes around 89% loss that is considered a huge degradation in the network performance

Scheduling of Multiple Energy Consumption in The Smart Buildings with Peak Demand Management

The global energy crisis and the depletion of fossil fuels have become pressing concerns, leading experts to search for alternative solutions. This paper presents an analysis of the day-ahead operation of the multi-carrier energy system (MCES) with the aim of minimizing operational costs, reducing pollution emissions, and maximizing consumers' comfort. The authors propose an optimal scheduling strategy called energy demand curtailment (EDCS), which aims at efficiently managing electrical energy consumption. Additionally, they consider an on-site generation strategy (OGS) for consumers to operate their own energy storages. Both EDCS and OGS are modeled based on demand-side management (DSM). To optimize these strategies and achieve their objectives, fuzzy logic is employed as an optimization approach along with objective functions. Finally, two scenarios are examined through numerical simulations to illustrate the effectiveness of this approach in optimizing energy utilization in MCE

Scheduling of Multiple Energy Consumption in The Smart Buildings with Peak Demand Management

The global energy crisis and the depletion of fossil fuels have become pressing concerns, leading experts to search for alternative solutions. This paper presents an analysis of the day-ahead operation of the multi-carrier energy system (MCES) with the aim of minimizing operational costs, reducing pollution emissions, and maximizing consumers' comfort. The authors propose an optimal scheduling strategy called energy demand curtailment (EDCS), which aims at efficiently managing electrical energy consumption. Additionally, they consider an on-site generation strategy (OGS) for consumers to operate their own energy storages. Both EDCS and OGS are modeled based on demand-side management (DSM). To optimize these strategies and achieve their objectives, fuzzy logic is employed as an optimization approach along with objective functions. Finally, two scenarios are examined through numerical simulations to illustrate the effectiveness of this approach in optimizing energy utilization in MCE

Classified cloning for QoS provisioning in OBS networks

This thesis investigates the challenges underlying QoS provisioning over OBS networks, considering both absolute and relative cases. Novel QoS provisioning schemes are proposed, and their effectiveness in minimizing loss probability is demonstrated in a range of network scenarios, while either minimizing or maintaining latency. Firstly, the important aspects of optical network evolution are discussed, and justification is provided for the deployment of WDM telecommunication systems in backbone networks. Insight is provided into the contention problem, facilitating the discussion and categorisation of loss reduction mechanisms and how they deal with contention. Our novel scheme for burst loss reduction, referred to as CCS, is then introduced. Comprehensive simulation results are presented which evaluate CCS and compare it to other proposals, demonstrating the superiority of CCS. Furthermore, two novel schemes to improve the quality of streamed video over OBS are introduced. The first of these yields an average PSNR gain over existing proposals of 4.2 dB and 2.4 dB with 10% and 30% network loads respectively. The second scheme is less affected by the increased percentage of video traffic, and an average improvement of 5 dB is obtained in the worst- case scenario with 50% video traffic and medium/high network loads. Finally, the relationship between loss rate, ETE-delay and the aggregation parameters are studied in detail in order to inform the design of a novel absolute QoS provisioning scheme. Adaptive aggregation is combined with admission control at the ingress nodes, together with the CCS scheme, to produce the ACCS scheme, which is evaluated through extensive simulations. It exhibits superior loss rate and maintains acceptable bounds on ETE-delay when compared to CCS, basic cloning, and standard OBS.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

CDDO–HS: Child Drawing Development Optimization–Harmony Search Algorithm

Child drawing development optimization (CDDO) is a recent example of a metaheuristic algorithm. The motive for inventing this method is children’s learning behavior and cognitive development, with the golden ratio being employed to optimize the aesthetic value of their artwork. Unfortunately, CDDO suffers from low performance in the exploration phase, and the local best solution stagnates. Harmony search (HS) is a highly competitive algorithm relative to other prevalent metaheuristic algorithms, as its exploration phase performance on unimodal benchmark functions is outstanding. Thus, to avoid these issues, we present CDDO–HS, a hybridization of both standards of CDDO and HS. The hybridized model proposed consists of two phases. Initially, the pattern size (PS) is relocated to the algorithm’s core and the initial pattern size is set to 80% of the total population size. Second, the standard harmony search (HS) is added to the pattern size (PS) for the exploration phase to enhance and update the solution after each iteration. Experiments are evaluated using two distinct standard benchmark functions, known as classical test functions, including 23 common functions and 10 CEC-C06 2019 functions. Additionally, the suggested CDDO–HS is compared to CDDO, the HS, and six others widely used algorithms. Using the Wilcoxon rank-sum test, the results indicate that CDDO–HS beats alternative algorithms